CAMBARERI VALERIO (DE)
SONY DEPTHSENSING SOLUTIONS SA/NV (BE)
US20210088636A1 | 2021-03-25 | |||
EP3015881B1 | 2018-08-15 | |||
US10996335B2 | 2021-05-04 | |||
US20210063576A1 | 2021-03-04 |
R.A. NEWCOMBE: "KinectFusion: Real-time dense surface mapping and tracking", 2011 10TH IEEE INTERNATIONAL SYMPOSIUM ON MIXED AND AUGMENTED REALITY, 2011, pages 127 - 136
CLAIMS 1. An electronic device comprising circuitry configured to update a camera configuration based on camera mode feedback information obtained by relating depth information obtained from ToF measurements with a reconstructed model of a scene. 2. The electronic device of claim 1, wherein the camera configuration is described by configuration settings of an imaging sensor and/or an illumination unit of an iToF camera. 3. The electronic device of claim 1, wherein the circuitry is configured to reconstruct and/or update the model of the scene based on the depth information obtained from ToF measurements. 4. The electronic device of claim 1, wherein the circuitry is configured to determine an overlap between the depth information and the model of the scene, and to update the camera configuration based on the overlap. 5. The electronic device of claim 4, wherein the circuitry is configured to decide, based on the overlap, whether or not the camera configuration is to be updated. 6. The electronic device of claim 1, wherein the circuitry is configured to improve a signal-to- noise ratio by updating the camera configuration. 7. The electronic device of claim 1, wherein the camera configuration comprises one or more of a modulation frequency of an illumination unit of a ToF camera, an integration time, a duty cycle, a number samples per correlation waveform period, a number of sub-frames per measurement, a frame rate, a length of a read-out period, a number of sub-integration cycles and a time span of the sub-integration cycles. 8. The electronic device of claim 1, wherein the camera mode feedback information controlling the camera configuration comprises an effective range of the scene. 9. The electronic device of claim 1, wherein the camera mode feedback information controlling the camera configuration comprises a saturation value the ToF signal amplitude. 10. The electronic device of claim 1, wherein the circuitry is configured to determine unwrapping feedback based on the model of the scene. 11. The electronic device of claim 10, wherein the circuitry is configured to determine unwrapping feedback for a pixel based on the model of the scene, and an estimated camera pose. 12. The electronic device of claim 11, wherein the circuitry is configured to determine a wrapping index for a pixel based on the unwrapping feedback for the pixel. 13. The electronic device of claim 1, wherein the circuitry is configured to determine model feedback based on an overlap between the depth information from ToF measurements and the model of the scene. 14. The electronic device of claim 1, wherein the circuitry is configured to update parts of the model of the scene. 15. The electronic device of claim 1, wherein the circuitry is configured to estimate a camera pose and to determine an overlap between the model of the scene and a current frame viewed from the estimated pose of the camera corresponding to the current frame. 16. A method comprising updating a camera configuration based on camera mode feedback information obtained by relating depth information obtained from ToF measurements with a reconstructed model of a scene. 17. A computer program comprising instructions which when executed by a processor cause the processor to update a camera configuration based on camera mode feedback information obtained by relating depth information obtained from ToF measurements with a reconstructed model of a scene. |
wherein the function performs perspective projection of including de- homogenization to obtain and, where is the angle between the associated pixel ray direction and the surface normal measurement TSDFs are for example also described in more detail in the KinectFusion paper cited above. Still further, the model reconstruction 703 may receive a model feedback (for example a model feedback matrix see below) which indicates for each pixel if it is reliable (overlap pixel in case that overlap is sufficient and in case that overlap is not sufficient), unreliable (non-overlap pixel in case that overlap is sufficient) or new (non-overlap pixel in case that overlap is not sufficient). The depth data of a reliable or new pixel may be used to improve the 3 model as described above (that means the model is created or updated with the corresponding depth measurement), the depth data of an unreliable pixel may be discarded or stored to a dedicated buffer that can be used or not. Surface prediction The surface prediction 704 receives the updated TSDF model and determines a dense 3 model surface prediction of the scene 101 viewed from the currently estimated pose That is a dense 3 model surface prediction of the scene 101 viewed from the currently estimated pose can be determined by evaluating the surface encoded in the zero-level-set, that is That means a model estimated vertex map and model estimated normal vector stated in the ToF camera coordinate system of the current frame are determined. This evaluation is based on ray casting the TSDF function That means each pixel’s corresponding ray within the global coordinate system, which is given by is “marched” within the volume and stopped when a zero crossing is found indicating the surface interface. That means each pixel’s ray (or a value rounded to the nearest voxel is inserted into the TSDF value and if a zero-level is determined it is stopped and the voxel is determined as part of the model surface (i.e. of the zero-level-set and thereby the estimated model vertex map ) is determined. If the ray of the ray casting of a certain pixel “marches” in a region outside the volume which means that the model s not defined in this region, the estimated model vertex map at this pixel is defined for example as (not a number). Still further, after a pose estimation in the pose estimation 702 and before the model reconstruction in the model reconstruction 703, ray tracing viewed from the currently estimated pose is determined based on ray casting the previously updated model may be performed as described above, which may yield an estimated model vertex map map for each pixel viewed from the currently estimated pose In this case for the first subscript refers to the currently estimated pose with regards to the frame and the second subscript refers to the previously updated model with regards to the frame This may be used in the model overlap decision 105-1 as described below. 3D Model Fig.8 shows an example of a 3D model of a scene as produced by 3D reconstruction. The 3D model is implemented as a triangle mesh grid 801. This triangle mesh may be a local or global three- dimensional triangle mesh. In alternative embodiments a 3D model may also be described by: a local or global voxel representation of a point cloud (uniform or octree); a local or global occupancy grid; a mathematical description of the scene in terms of planes, statistical distributions (e.g., Gaussian mixture models), or similar attributes extracted from the measured point cloud. In another embodiment a model may be characterized as a mathematical object that fulfills one or more of the following aspects: it is projectable to any arbitrary view, it can be queried for nearest neighbors (closest model points) with respect to any input 3D point, it computes distances with respect to any 3D point cloud, it estimates normals and/or it can be resampled at arbitrary 3D coordinates. Model overlap decision As shown in the exemplary embodiment of Fig.5 above, model overlap decision (105-1 in Fig.5) determines camera feedback information for the adaptive mode generator (105-2 in Fig.5), model feedback for the 3D model reconstruction (104-2 in Fig.5) and unwrapping feedback for the ToF datapath (102-2 in Fig.5) based on the updated 3D model as obtained from 3D reconstruction (104 in Fig.5), the registered point cloud as obtained from pose estimation (104-1 in Fig.5) and the depth map as obtained from the iToF camera (102 in Fig.5). In another embodiment the 3D model as obtained from 3D reconstruction (104 in Fig.5) may be not updated. In this case it may only be registered so that the new points in the current view may be mapped to the past model and the update may be finalized afterwards (by discarding for example the most uncertain parts of the update, etc.). Fig.9 shows an exemplary process performed in the model overlap decision. At 901, a model overlap decision is determined as part of the camera mode feedback information based on updated 3D model and registered point cloud. At 902, an effective range variable is determined as part of the camera mode feedback information based on depth map. At 903, a saturation of the ToF signal amplitude is determined as part of the camera mode feedback information. At 904, model feedback is determined based on the current 3D model and registered point cloud. At 905, unwrapping feedback is determined based on an updated 3D model. It should be noted that the model feedback may be determined based on the registered point cloud (which is based on the received point cloud from the device and the current scene 3D model from the past (obtained from the memory)).The model overlap decision es determined at 901 defines a model overlap between the previous (i.e. ^ − 1) reconstructed 3D model and the current frame ^ (FoV of the current frame) frame based on the registered point cloud and decides on a camera mode update based on the model overlap. By means of the estimated camera pose, the 3D model can be projected to the desired view, and it can be assessed what fraction of the ToF data of the current frame is overlapping (and therefore improving) the 3D model, and what fraction is new and may be annotated as such in a model feedback. Based on a predetermined criterion, (for example but not limited to a minimum overlapping region) a decision is made whether or not to modify the camera configuration mode. In another embodiment the 3D model may be projected to the view of the point cloud and the overlap may be computed (photometric error, point-to-mesh distance, depth map distances between depth information from ToF sensor and 3D model projected to depth map (using camera intrinsics)). At this point, it may be decided whether the overlap is sufficient (see Figs.10 and 11). Therefore, in order to decide whether the overlap is sufficient or not those points that are overlapping are taken to improve the current 3D model into an updated 3D model, where the new points that come in from the measurements (registered point cloud) refine it. Still further, the new, non-overlapping parts may be used to complete the 3D model with new information (which is also equipped with uncertainty weights) which yields the model feedback (see below). When taking the model feedback into account an updated 3D model is obtained. This updated 3D model may be projected to the depth camera pose and converted into wrapping indexes from the current pose. These wrapping indexes may become the most likely indexes for the next frame (with a smaller prior probability for the neighboring wrapping indexes as well), which yields an unwrapping feedback (see Fig.13). In addition, based on the model projected to the depth camera pose, and the current depth map, it may be predicted that at the next frame a certain “depth swing” and may be also related quantities such as an “amplitude swing” may occur, so that it may be decided on the integration time and the modulation frequency for the next frame. For example, if the effective range (see Fig.12) is smaller, the modulation frequency may be increased to reduce noise and if the amplitude is too large, the integration time may be reduced to avoid saturation. These informations are comprised in the camera mode feedback information (see below) which is delivered to the adaptive mode generator (105-2 in Fig.5, as part of a camera mode sequencer (105 in Fig.5)) on which basis the adaptive mode generator controls a camera mode update of the iToF camera (see below). Fig.10 schematically shows an exemplary overlap between a previous reconstructed 3D model and the field of view of the current frame. The currently available 3D model 1002 (which is schematic 2D projection of the 3D model) is reconstructed viewed from the camera pose with its corresponding FOV 1001. The current frame ^ yields an estimated pose and a corresponding FOV 1003. The FOV 1003 and (with its corresponding the estimated pose camera of the current frame ^ and the reconstructed 3D model 902 overlap within the region 1004 and do not overlap within the region 1005, which may yield an overlap of 95%. A predetermined criterion for minimal overlapping region could be 90% and therefore it is decided that the overlap is sufficient, and the camera configuration mode is modified (see below) and the 3D model can further be updated with the new information about the scene (for example higher SNR depth data) from the current frame ^ to complete and improve the model. Fig.11 schematically shows a flowchart of an exemplary model overlap determination procedure carried out by the model overlap decision of the embodiment. At 1011, an estimated model vertex map viewed from the currently estimated pose (from the surface prediction 704) based on the previously updated model and the vertex map of the current frame is received (it is also possible to receive an estimated model vertex map viewed from the previously estimated pose based on the previously updated model At 1012 the number of pixels where the estimated model vertex map entry is determined. At 1013, it is determined the number ^ of pixels (for each pixel ^ where the estimated model vertex map does not have a entry) where the normed difference between the estimated model vertex map and the vertex map is greater than a predetermined threshold The norm may be a Euclidean norm, an norm, a maximum norm, or the like. The predetermined threshold ^ may be for example between 1cm and 5cm. At 1014, a model overlap value ^ between reconstructed 3D model and the current frame ^ is determined as where is the total number of pixels of the imaging sensor of the iToF camera. At 1015, it is asked if the model overlap value between reconstructed 3D model and the current frame ^ is greater than a predetermined minimum overlap value that is If the answer a 1015 is yes, it is proceeded further with 1016. At 1016, a Boolean variable that represents the model overlap decision is set to = 1 to indicate an adaptation of the current camera configuration mode. This may lead to an increased signal-to-noise ratio of the next frame and therefore improve the 3D model or it may improve the depth precision (for example by increasing the modulation frequency). If the answer at 1015 is no, it is proceeded further with 1017. At 1017, the Boolean variable that represents the model overlap decision is set to = 0 to indicate the use of a default camera configuration mode. The model overlap decision passes this model overlap decision on to the adaptive mode generator (150-2 in Fig.5) as part of the camera mode feedback information that controls the adaptive mode generator. If, for example, the predetermined overlap threshold is exceeded, it is decided that the current camera pose benefits from using a different camera configuration mode which is decided by the adaptive mode generation, as described in more detail below. It should be noted that it may be sufficient to only count the number entries in the estimated model vertex map and determine the model overlap value as This allows to determine if the camera pose has changed significantly (but does not allow to determine if the elements within the scene have moved). It should further be noted that the model overlap decision described above determines a model overlap between the previous reconstructed 3D model and the current frame (FoV of the current frame) frame based on the registered point cloud. It should however be noted that in alternative embodiments, the model overlap decision may alternatively determine a model overlap based on the depth map of the scene. Camera mode feedback information As shown in the exemplary embodiment of Fig.5 above, model overlap decision (105-1 in Fig.5) determines camera mode feedback information for the adaptive mode generator (105-2 in Fig.5) based on e.g. the updated 3D model as obtained from 3D reconstruction (104 in Fig.5), the registered point cloud as obtained from pose estimation (104-1 in Fig.5) and the depth map as obtained from the iToF camera (102 in Fig.5). This camera mode feedback information is passed to the adaptive mode generator which used this information to determine if the current camera configuration mode is to be adapted/changed as described below in more detail. For example, the camera mode feedback information may comprise the Boolean variable that represents the model overlap decision as defined in the example of Fig.11 above. The camera mode feedback information may further comprise information on which basis the adaptive mode generation (105-2 in Fig.5) adapts the camera configuration mode. For example, the camera mode feedback information may further comprise an effective range variable which characterizes the effective range of the scene 101 as described below in more detail, and/or the camera mode feedback information may further comprise a saturation value of the ToF signal amplitude (i.e. amplitude of the IQ values/ amplitude of the phasor). That means if digital numbers in a certain range (for example 0-1500) are expected and the at a certain number above that range (for example 2000) clipping will start, a flag is received in the depth map indicating that the value is invalid by saturation of the ToF signal. Therefore, the number of saturated pixels may be counted (from the amplitude, or from the depth map) and it may be looked at the amplitude histogram. For example, if 1% of the depth map pixels correspond to amplitude values that have saturated, the integration time must be likely lowered to meet the best sensing conditions. Effective Range Fig.12 shows a probability density function of a depth map of a scene with a given exemplary camera mode. Along the x-axis it is plotted the distance of the depth measurement in meters, and along the y-axis the corresponding values of a probability density function (which may be obtained by obtaining an ensemble histogram from the projected 3D model, when projected to a view from the estimated camera pose as a depth map or from computing such a histogram from the last acquired depth map). The graph of the exemplary probability density function has the shape of a Gaussian distribution. The mean-value (expected value) of the Gaussian distribution of this exemplifying distribution of Fig.12 is 3 meters and the standard deviation of the probability density function is = 1.02 (a relative standard deviation of 34%). That is, one standard deviation above the mean value corresponds to = 4,02 m and one standard deviation above the mean value = 1,98 In another embodiment the probability density function may have another density function than a Gaussian distribution and it is looked at this density function to decide where to acquire the bulk of depth map information. Further, it may be looked at the amplitude histogram, so that the exposure is so that there is no saturation. For example, if 5% of the current depth map is saturated, the modulation frequency is changed (which does not affect integration time, it can be changed independently) to adapt to, e.g., a reduced unambiguous range (to improve the SNR), where the integration time may have to be reduced also to remove that saturation. The effective range is defined by the mean value and the standard deviation that is In the diagram of Fig.13 it is also shown an example of the unambiguous range (see Fig.4) of the camera configuration mode, here = 5 m. The unambiguous range is above the mean value ^ (that is and also outside the first standard deviation This effective range which characterizes the effective range of the scene may for example be determined by the model overlap decision (105-1 in Fig.5) and may be passed on as part of camera mode feedback information to the adaptive mode generator (105-2 in Fig.5). In the example above, the effective range ^ comprises the mean depth of the standard deviation of the depth distribution of the current depth map Alternatively, the effective range may also be defined by the mean depth alone, or by the mean depth weighted by the standard deviation or the like. Still further, the effective range may comprise the minimum and maximum depth of the current depth map or the 5th and 95th depth percentiles of the current depth map or a full depth histogram. It should also be noted that in the example given above, the effective range of the scene is defined by the mean depth of the depth map Alternatively, the median depth of the depth map may be used instead of the mean depth. In another embodiment the effective range may be the interval where may be the 90th or 95th percentile of the current depth histogram. Model Feedback As shown in Fig.5 above, model overlap decision (105-1 in Fig.5) may determine, in addition to the camera mode feedback information for the adaptive mode generator (105-2 in Fig.5), model feedback for the 3D model reconstruction (104-2 in Fig.5) based on the depth map as obtained from the iToF camera (102 in Fig.5). As described above, in order to decide whether the overlap is sufficient or not those points that are overlapping are taken to improve the current 3D model into an updated 3D model, where the new points that come in from the measurements (registered point cloud) refine it. Still further, the new, non-overlapping parts may be used to complete the 3D model with new information (which is also equipped with uncertainty weights) which yields the model feedback. In another embodiment the model feedback may comprise a model feedback matrix (which may also be implemented as a vector or any other data structure) which has the same size as the depth map and where an entry is set to 0 if the pixel is not known so far in the 3D model (i.e. if the pixel has that is = 0 or set to 1 if the pixel is known in the 3D model, = 1. The model feedback matrix may be provided together with the Boolean variable as model feedback to the 3D model reconstruction (104-2 in Fig.5). Thereby, it is provided information on which part of the depth map is not present in the known model (e.g., the depth map covers an unknown area in the scene and should be regarded as new) and which part is overlapping. When the overlap is sufficient (i.e. = 1), the model feedback will annotate the overlapping data as “reliable” and the non-overlapping data as “unreliable” The former will contribute to improving the 3D model, the latter will be discarded or stored to a dedicated buffer that can be used or not (e.g., as a higher/lower confidence measure for the reliable/unreliable data) based on the use-case. When the overlap is insufficient (i.e. = 0), the model feedback will annotate the overlapping data as “reliable” = 1) and the non-overlapping data as “new” = 0). Both data will be used to improve the 3D model, with this additional information that can be leveraged or not (e.g., as a higher/lower confidence measure for the reliable/new data) based on the use-case. Thereby, the 3D reconstruction precision is increased mostly when the ToF camera would acquire redundant data, as it is often the case during 3D reconstruction acquisitions. Unwrapping Feedback As shown in Fig.5 above, model overlap decision (105-1 in Fig.5) may further determine, in addition to the camera mode feedback information for the adaptive mode generator (105-2 in Fig.5) unwrapping feedback for the ToF datapath (102-2 in Fig.5) based on the depth map as obtained from the iToF camera (102 in Fig.5). When the overlap decision (105-1 in Fig.5) determines that the overlap between the updated 3D model and the registered point cloud is sufficient, the model overlap decision may deliver such unwrapping feedback to the ToF datapath (102-2 in Fig.5). The unwrapping feedback may comprise information for each pixel about the probability for the pixel of being inside a certain wrapping index (or “bin”) based on the reconstructed 3D model. As described with respect to Fig.5, when determining the phase of a pixel a wrapping problem may occur for measurements which exceed the unambiguous range If a phase measurement beyond the unambiguous range are expected, than the wrapping index of each pixel has to be determined by some “unwrapping” process. Fig.13 shows a schematic example of determining a wrapping index probability based on a 3D model maintained by 3D Reconstruction of the embodiment described in Fig.5 and Fig.7. A reconstructed 3D model 1302 (here, for sake of visualization, a schematic 2D projection of the 3D model) is viewed from the camera pose with its corresponding FOV. This reconstructed model 1302 is represented by an estimated model vertex map in the description of Fig.7 above). Based on the reconstructed model, for each pixel in the description of Fig.7 above) a prior probability is determined for the likelihoods of the wrapping indices. a wrapping index with the highest probability is determined for each part of the model 1302, that is for each pixel. The parts of the model 1302 indicated by brackets 1303 and 1304 are determined to have a high prior probability, e.g = 1, for a wrapping index = 1 but a low prior probability, e.g. = 0, for wrapping indexes ≠ 1. The part of the model 1302 indicated by bracket 1305 is determined to have a high prior probability, e.g. = 1 for a wrapping index = 2 but a low prior probability, e.g. = 0, for wrapping indeces ≠ 2. Alternatively, the prior probability may be chosen as a soft distribution. That is, for each pixel the wrapping index obtained by the 3D model reconstruction may be promoted with a high prior probability, but the neighboring wrapping indices may also be weighted with a slightly higher prior probability than the rest of the available wrapping indices. In another embodiment an estimated model vertex map viewed from the current estimated pose based on the previously updated model is used to determine an estimated depth data or phase. From the received estimated model vertex map an estimated depth data is determined for each pixel by using a back-transformation following from Eq.22 : Based on Eq.32, a model-estimated phase is determined and for a pixel a maximum likelihood estimator is determined (it may be assumed relatively smooth motion). This corresponds to finding the wrapping index hypothesis that maximizes the likelihood of observing a certain phase in the model and in the measurements: That is, a wrapping index deduced from the modeled phase from the reconstructed 3D model is weighted higher. Further, this approach can be extended to maximizing a posteriori criterion if prior information is available, for example leveraging spatial priors on the neighboring measurements. Still further, this approach can be applied also to a coarser scene discretization, for example by looking at an occupancy grid of the 3D model rather than the wrapping indexes of the projected depth map. When the overlap is insufficient to deduce a wrapping index from the model, it may for example be determined that all unwrapping coefficients have equal probability. Adaptive Mode Generator As shown in Fig.5 above, an adaptive mode generator (105-2 in Fig.5) of a camera mode sequencer (105 in Fig.5) controls a camera mode update of an iToF camera (102 in Fig.5) based on camera mode feedback information obtained from a model overlap decision (105-1 in Fig.5). Based on the camera mode feedback information determined by the model decision, the adaptive mode generation may for example determine a camera mode update in such a way to receive increase the signal-to-noise ratio of the next frame and therefore improve the reconstruction of the 3D model of the scene. The adaptive mode generator may for example manage a number of camera modes which each defines a set of configuration parameters for the iToF camera (see Figs.6a, b, c and corresponding description). Based on the camera mode feedback information the adaptive mode generator selects a camera configuration mode from the available camera configuration modes. The adaptive mode generator may for example select the camera mode based on the camera mode feedback information in such a way that the signal-to-noise ratio of the next frame to improve the 3D reconstruction model. Fig.14 shows an exemplary process performed in the adaptive mode generator. In the example given in Fig.14, at 1401, the adaptive mode generator receives as part of the camera mode feedback information an overlap decision parameter (Boolean variable which indicates if the camera configuration should be adapted (Boolean variable = 1) or not = 0). At 1401, the adaptive mode generator also receives as part of the camera mode feedback information further information, here the effective range of the scene defined by mean value and standard deviation ^ of the probability function of the depth map as described in more detail with regard to Fig.13 above. At 1402, the adaptive mode generator determines based on overlap decision parameter obtained by model overlap decision if there is enough overlap for a camera mode update (901 in Fig. 9). If = 0 (there is insufficient overlap for a camera mode update) the mode generator continues at 1403 and sets the camera configuration mode to (or keeps it at) a default camera configuration mode with sufficiently long unambiguous range and corresponding modulation frequency. In the exemplary default camera mode, the modulation frequency is for set at an exemplary value of (see Fig.6a and corresponding description above). According to Eq.19 the modulation frequency of this default mode results in an unambiguous range In a case where the overlap decision detects that there is sufficient overlap between the currently measured point cloud and the previous 3D model (i.e = 1), the adaptive mode generator decides that it is possible to switch the camera mode to an optimized mode and continues at 1404. In the example provided here, the adaptive mode generator selects the alternative camera mode on the basis of the effective range as obtained from the model overlap decision and described with regard to Fig.12 above. At 1404 the frequency modulation is determined based on Eq.19 above in such a way that unambiguous range exceeds the first standard deviation above the mean depth: With exemplifying parameters of = 0.61 m, this yields a modulation frequency for the mode update (see also Fig.15A). Based on this envisaged modulation frequency the adaptive mode generator, at 1405, selects a camera mode which fits best to the frequency requirement. At 1406 the adaptive mode generator controls the ToF camera to switch from the default camera mode A to this selected camera mode (mode B). This increases the signal-to-noise ratio of the next frame as it increases the resolution within the decreased unambiguous range that fits the current scene better than the configuration settings applied in the previous frame in which the unambiguous range of the was longer than needed. In the example provided above, the adaptive mode generator sets the modulation frequency so that the unambiguous range one standard deviation above the mean depth of the scene. In alternative embodiments, this may be chosen differently. For example, the modulation frequency may be set to correspond to two or three standard deviations above the mean, or to correspond to an unambiguous range defined as a certain percentile of the probability density function of depth of a depth map or the like. In the example above, the adaptive mode generator responds to camera mode feedback information comprising the effective range of the scene and adapts the camera mode by changing the modulation frequency of a multi-frequency ToF camera. Focusing here on the effective range and modulation frequency, however, serves only as an example. Any single configuration parameters or groups of configuration parameters may be adapted by the adaptive mode generator in a similar way. And the adaptive mode generator might base its decision on any other camera mode feedback information that is suitable to control the camera mode. It should also be noted that in another embodiment it may not be required that a predefined camera mode comprises multiple configuration parameters as shown in Figs.6a, b, c above. A camera mode might also be described by a single configuration parameter, like the modulation frequency as such, or the integration time as such. In other words: An alternative to applying predefined camera modes A, B, C with multiple configuration parameters as described in the examples above, the adaptive mode generator might as well directly alter a specific configuration setting as such, without reference any reference to mode settings. Fig.15A shows an embodiment of a camera configuration mode adaptation for a dual-frequency iToF camera as described in the example of Fig.14 above. The x-axis shows the frame number and the y-axis shows the modulation frequency During a first frame period 1501 a camera configuration mode with a first modulation frequency is used, which is a default camera configuration mode. Then, during a second frame period 1502 the camera configuration mode is changed to a camera configuration mode with a second modulation frequency which is higher than the first modulation frequency and therefore decreases the unambiguous range and increases the signal-to-noise ratio within the reduced unambiguous range (meaning an increased signal-to-noise ratio more information about a scene is received). Then, during a third frame period 1503 the camera configuration is again changed back to the default camera configuration mode where the first modulation frequency is used again. Fig.15B shows an embodiment of a camera configuration mode adaptation for a multi-frequency iToF camera. The x-axis shows the frame number and the y-axis shows the modulation frequency During a first frame period 1504 a camera configuration mode with a first modulation frequency is used, which is a default camera configuration mode. Then, during the frame periods 1505 – 1510 a second camera configuration is used for the dual-frequency iToF camera which alternates between two different modulation frequencies The second modulation frequency is used during the frame periods 1505, 1507 and 1509 and the third modulation frequency is used during the frame periods 1506, 1508 and 1510. The second and the third modulation frequencies are both higher than the first modulation frequency and therefore the unambiguous range is decreased and the signal-to-noise ratio within the reduced unambiguous range is increased (meaning an increased signal-to-noise ratio ). Then, during the frame period 1511 the camera configuration is again changed back to the default camera configuration mode with where the first modulation frequency is used again. Fig.15C shows an alternative embodiment of a camera configuration mode adaptation for a multi- frequency iToF camera. The x-axis shows the frame number and the y-axis shows the modulation frequency During a first frame period 1512 a camera configuration mode with a first modulation frequency is used, which is a default camera configuration mode. Then, during the frame periods 1513 – 1516 a second camera configuration is used for the multi-frequency iToF camera which step-by-step increases the different modulation frequencies with The modulation frequencies are all higher than the first modulation frequency and therefore the unambiguous range is decreased and the signal-to- noise ratio within the reduced unambiguous range is increased (meaning an increased signal-to-noise ratio ). The multi-frequency approach allows to obtain measurements at several frequencies and therefore for example leads to a multipath mitigation. Then, during the frame period 1517 the camera configuration is again changed back to the default camera configuration mode with where the first modulation frequency is used again. Fig.15D shows an embodiment of a camera configuration mode adaptation that changes the integration time to receive more information about a scene. The x-axis shows the frame number and the y-axis shows the integration time . During a first frame period 1518 a camera configuration mode with a first integration time is used, which is a default camera configuration mode. Then, during a second frame period 1519 a second camera configuration is used with a second integration time which is shorter than the first integration time Then, during a third frame period 1520 a third camera configuration is used with a third integration time which is shorter than the first integration time . With a short integration time (see also Fig.4) a saturation of a pixel can be avoided. By decreasing the integration time in a plurality of steps objects with different distances can be captured and the quality of the captured data with respect to the 3D model can be maximized (meaning an increased signal-to-noise ratio ). Then during the frame period 1221 the camera configuration is again changed back to the default camera configuration mode with where the first modulation frequency is used again. Implementation Fig.16 schematically describes an embodiment of an iToF device that can implement the camera mode sequencer as described in the embodiments above, in particular the processes of performing depth measurements, determining a depth map in a datapath, determining 3D model reconstruction, determining a model overlap and generating an adaptive camera configuration mode configuration. The electronic device 1600 may further implement all other processes of a standard iToF system. The electronic device 1600 comprises a CPU 1601 as processor. The electronic device 1600 further comprises an iToF imaging sensor 1608, an illumination unit 1609 and auxiliary sensors 1604 connected to the processor 1601. The processor 1601 may for example implement performing a pose estimation and 3D model reconstruction (see Fig.7) or overlap decision (see Figs.10 and 11). The electronic device 1600 further comprises a user interface 1607 that is connected to the processor 1601. This user interface 1607 acts as a man-machine interface and enables a dialogue between an administrator and the electronic system. For example, an administrator may make configurations to the system using this user interface 1607. The electronic device 1600 further comprises a WLAN interface 1605, and an Ethernet interface 1606. These units 1605, 1606 act as I/O interfaces for data communication with external devices. The electronic device 1600 further comprises a data storage 1602, and a data memory 1603 (here a RAM). The data storage 1602 is arranged as a long-term storage, e.g. for storing camera configuration modes and 3D models or the like. The data memory 1603 is arranged to temporarily store or cache data or computer instructions for processing by the processor 1601. It should be noted that the description above is only an example configuration. Alternative configurations may be implemented with additional or other sensors, storage devices, interfaces, or the like. *** It should be recognized that the embodiments describe methods with an exemplary ordering of method steps. The specific ordering of method steps is, however, given for illustrative purposes only and should not be construed as binding. It should also be noted that the division of the electronic device of Figs.1, 6 and 14 into units is only made for illustration purposes and that the present disclosure is not limited to any specific division of functions in specific units. For instance, at least parts of the circuitry could be implemented by a respectively programmed processor, field programmable gate array (FPGA), dedicated circuits, and the like. All units and entities described in this specification and claimed in the appended claims can, if not stated otherwise, be implemented as integrated circuit logic, for example, on a chip, and functionality provided by such units and entities can, if not stated otherwise, be implemented by software. In so far as the embodiments of the disclosure described above are implemented, at least in part, using software-controlled data processing apparatus, it will be appreciated that a computer program providing such software control and a transmission, storage or other medium by which such a computer program is provided are envisaged as aspects of the present disclosure. Note that the present technology can also be configured as described below: (1) An electronic device comprising circuitry configured to update a camera configuration mode A, B, C) based on camera mode feedback information obtained by relating depth information obtained from ToF measurements with a reconstructed model of a scene (101). (2) The electronic device of (1), wherein the camera configuration is described by configuration settings of an imaging sensor and/or an illumination unit of an iToF camera. (3) The electronic device of (1) or (2), wherein the circuitry is configured to reconstruct and/or update the model of the scene (101) based on the depth information obtained from ToF measurements. (4) The electronic device of anyone of (1) to (3), wherein the circuitry is configured to determine an overlap between the depth information and the model of the scene, and to update the camera configuration based on the overlap (5) The electronic device of (4), wherein the circuitry is configured to decide, based on the overlap whether or not the camera configuration is to be updated. (6) The electronic device of anyone of (1) to (5), wherein the circuitry is configured to improve a signal-to-noise ratio by updating the camera configuration. (7) The electronic device of anyone of (1) to (6), wherein the camera configuration comprises one or more of a modulation frequency of an illumination unit (210) of a ToF camera, an integration time, a duty cycle, a number samples per correlation waveform period, a number of sub- frames per measurement, a frame rate, a length of a read-out period, a number of sub-integration cycles and a time span of the sub-integration cycles. (8) The electronic device of anyone of (1) to (7), wherein the camera mode feedback information controlling the camera configuration comprises an effective range of the scene. (9) The electronic device of anyone of (1) to (8), wherein the camera mode feedback information controlling the camera configuration comprises a saturation value the ToF signal amplitude. (10) The electronic device of anyone of (1) to (9), wherein the circuitry is configured to determine unwrapping feedback based on the model of the scene. (11) The electronic device of (10), wherein the circuitry is configured to determine unwrapping feedback for a pixel based on the model ) of the scene (101), and an estimated camera pose (12) The electronic device of (11), wherein the circuitry is configured to determine a wrapping index for a pixel based on the unwrapping feedback for the pixel (13) The electronic device of anyone of (1) to (12) , wherein the circuitry is configured to determine model feedback based on an overlap between the depth information from ToF measurements and the model of the scene. (14) The electronic device of anyone of (1) to (13), wherein the circuitry is configured to update parts of the model of the scene (101). (15) The electronic device of anyone of (1) to (14), wherein the circuitry is configured to estimate a camera pose and to determine an overlap between the model of the scene (101) and a current frame viewed from the estimated pose of the camera corresponding to the current frame . (16) A method comprising updating a camera configuration mode A, B, C) based on camera mode feedback information obtained by relating depth information obtained from ToF measurements with a reconstructed model of a scene (101). (17) A computer program comprising instructions which when executed by a processor cause the processor to update a camera configuration mode A, B, C) based on camera mode feedback information obtained by relating depth information obtained from ToF measurements with a reconstructed model of a scene (101).