Human hand motion and shape sensing is an area of high interest in medical communities and for human interaction researchers. Measurement of small hand movements could help professionals to quantize the stage of conditions like Parkinson's Disease (PD) and Essential Tremor (ET). Similar data is also useful for designers of human interaction algorithms to infer information about hand pose and gesture recognition. In this paper we present a structured light sensor capable of measuring hand shape and color at 121 FPS. Our algorithm uses a novel structured light method developed by us, called Chebyshev Phase Shifting (CPS). This method uses a digital projector and a camera to create high-resolution color 3D models from sequences of color images. We show how to encode CPS patterns in three RGB images for a reduced acquisition time, enabling high speed capture. We have built a prototype to measure rapid trembling hands. Our results show our prototype accurately captures fast tremors similar to those of PD patients. Color 3D model sequences recorded at high speed with our sensor will be used to study hand kinematic properties in a future.
{"title":"Rapid Hand Shape Reconstruction with Chebyshev Phase Shifting","authors":"Daniel Moreno, Wook-Yeon Hwang, G. Taubin","doi":"10.1109/3DV.2016.24","DOIUrl":"https://doi.org/10.1109/3DV.2016.24","url":null,"abstract":"Human hand motion and shape sensing is an area of high interest in medical communities and for human interaction researchers. Measurement of small hand movements could help professionals to quantize the stage of conditions like Parkinson's Disease (PD) and Essential Tremor (ET). Similar data is also useful for designers of human interaction algorithms to infer information about hand pose and gesture recognition. In this paper we present a structured light sensor capable of measuring hand shape and color at 121 FPS. Our algorithm uses a novel structured light method developed by us, called Chebyshev Phase Shifting (CPS). This method uses a digital projector and a camera to create high-resolution color 3D models from sequences of color images. We show how to encode CPS patterns in three RGB images for a reduced acquisition time, enabling high speed capture. We have built a prototype to measure rapid trembling hands. Our results show our prototype accurately captures fast tremors similar to those of PD patients. Color 3D model sequences recorded at high speed with our sensor will be used to study hand kinematic properties in a future.","PeriodicalId":425304,"journal":{"name":"2016 Fourth International Conference on 3D Vision (3DV)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127800115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper presents a new large scale dataset targeting evaluation of local shape descriptors and 3d object recognition algorithms. The dataset consists of point clouds and triangulated meshes from 292 physical scenes taken from 11 different views, a total of approximately 3204 views. Each of the physical scenes contain 10 occluded objects resulting in a dataset with 32040 unique object poses and 45 different object models. The 45 object models are full 360 degree models which are scanned with a high precision structured light scanner and a turntable. All the included objects belong to different geometric groups, concave, convex, cylindrical and flat 3D object models. The object models have varying amount of local geometric features to challenge existing local shape feature descriptors in terms of descriptiveness and robustness. The dataset is validated in a benchmark which evaluates the matching performance of 7 different state-of-the-art local shape descriptors. Further, we validate the dataset in a 3D object recognition pipeline. Our benchmark shows as expected that local shape feature descriptors without any global point relation across the surface have a poor matching performance with flat and cylindrical objects. It is our objective that this dataset contributes to the future development of next generation of 3D object recognition algorithms. The dataset is public available at http://roboimagedata.compute.dtu.dk/.
{"title":"A Large-Scale 3D Object Recognition Dataset","authors":"Thomas Sølund, A. Buch, N. Krüger, H. Aanæs","doi":"10.1109/3DV.2016.16","DOIUrl":"https://doi.org/10.1109/3DV.2016.16","url":null,"abstract":"This paper presents a new large scale dataset targeting evaluation of local shape descriptors and 3d object recognition algorithms. The dataset consists of point clouds and triangulated meshes from 292 physical scenes taken from 11 different views, a total of approximately 3204 views. Each of the physical scenes contain 10 occluded objects resulting in a dataset with 32040 unique object poses and 45 different object models. The 45 object models are full 360 degree models which are scanned with a high precision structured light scanner and a turntable. All the included objects belong to different geometric groups, concave, convex, cylindrical and flat 3D object models. The object models have varying amount of local geometric features to challenge existing local shape feature descriptors in terms of descriptiveness and robustness. The dataset is validated in a benchmark which evaluates the matching performance of 7 different state-of-the-art local shape descriptors. Further, we validate the dataset in a 3D object recognition pipeline. Our benchmark shows as expected that local shape feature descriptors without any global point relation across the surface have a poor matching performance with flat and cylindrical objects. It is our objective that this dataset contributes to the future development of next generation of 3D object recognition algorithms. The dataset is public available at http://roboimagedata.compute.dtu.dk/.","PeriodicalId":425304,"journal":{"name":"2016 Fourth International Conference on 3D Vision (3DV)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121413401","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper addresses the problem of automatic 3D shape segmentation in point cloud representation. Of particular interest are segmentations of noisy real scans, which is a difficult problem in previous works. To guide segmentation of target shape, a small set of pre-segmented exemplar shapes in the same category is adopted. The main idea is to register the target shape with exemplar shapes in a piece-wise rigid manner, so that pieces under the same rigid transformation are more likely to be in the same segment. To achieve this goal, an over-complete set of candidate transformations is generated in the first stage. Then, each transformation is treated as a label and an assignment is optimized over all points. The transformation labels, together with nearest-neighbor transferred segment labels, constitute final labels of target shapes. The method is not dependent on high-order features, and thus robust to noise as can be shown in the experiments on challenging datasets.
{"title":"Exemplar-Based 3D Shape Segmentation in Point Clouds","authors":"Rongqi Qiu, U. Neumann","doi":"10.1109/3DV.2016.29","DOIUrl":"https://doi.org/10.1109/3DV.2016.29","url":null,"abstract":"This paper addresses the problem of automatic 3D shape segmentation in point cloud representation. Of particular interest are segmentations of noisy real scans, which is a difficult problem in previous works. To guide segmentation of target shape, a small set of pre-segmented exemplar shapes in the same category is adopted. The main idea is to register the target shape with exemplar shapes in a piece-wise rigid manner, so that pieces under the same rigid transformation are more likely to be in the same segment. To achieve this goal, an over-complete set of candidate transformations is generated in the first stage. Then, each transformation is treated as a label and an assignment is optimized over all points. The transformation labels, together with nearest-neighbor transferred segment labels, constitute final labels of target shapes. The method is not dependent on high-order features, and thus robust to noise as can be shown in the experiments on challenging datasets.","PeriodicalId":425304,"journal":{"name":"2016 Fourth International Conference on 3D Vision (3DV)","volume":"132 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122902606","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, we develop a novel self-calibration method for single view 3D reconstruction using a mirror sphere. Unlike other mirror sphere based reconstruction methods, our method needs neither the intrinsic parameters of the camera, nor the position and radius of the sphere be known. Based on eigen decomposition of the matrix representing the conic image of the sphere and enforcing a repeated eignvalue constraint, we derive an analytical solution for recovering the focal length of the camera given its principal point. We then introduce a robust algorithm for estimating both the principal point and the focal length of the camera by minimizing the differences between focal lengths estimated from multiple images of the sphere. We also present a novel approach for estimating both the principal point and focal length of the camera in the case of just one single image of the sphere. With the estimated camera intrinsic parameters, the position(s) of the sphere can be readily retrieved from the eigen decomposition(s) and a scaled 3D reconstruction follows. Experimental results on both synthetic and real data are presented, which demonstrate the feasibility and accuracy of our approach.
{"title":"Single View 3D Reconstruction under an Uncalibrated Camera and an Unknown Mirror Sphere","authors":"K. Han, Kwan-Yee Kenneth Wong, Xiao Tan","doi":"10.1109/3DV.2016.50","DOIUrl":"https://doi.org/10.1109/3DV.2016.50","url":null,"abstract":"In this paper, we develop a novel self-calibration method for single view 3D reconstruction using a mirror sphere. Unlike other mirror sphere based reconstruction methods, our method needs neither the intrinsic parameters of the camera, nor the position and radius of the sphere be known. Based on eigen decomposition of the matrix representing the conic image of the sphere and enforcing a repeated eignvalue constraint, we derive an analytical solution for recovering the focal length of the camera given its principal point. We then introduce a robust algorithm for estimating both the principal point and the focal length of the camera by minimizing the differences between focal lengths estimated from multiple images of the sphere. We also present a novel approach for estimating both the principal point and focal length of the camera in the case of just one single image of the sphere. With the estimated camera intrinsic parameters, the position(s) of the sphere can be readily retrieved from the eigen decomposition(s) and a scaled 3D reconstruction follows. Experimental results on both synthetic and real data are presented, which demonstrate the feasibility and accuracy of our approach.","PeriodicalId":425304,"journal":{"name":"2016 Fourth International Conference on 3D Vision (3DV)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127057287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Depth from defocus (DFD) requires estimating the depth dependent defocus blur at every pixel. Several approaches for accomplishing this have been proposed over the years. For a pair of images this is done by modeling the defocus relationship between the two differently defocused images and for single defocused images by relying on the the properties of the point spread function and the characteristics of the latent sharp image. We propose depth discriminative filters for DFD that can represent many of the widely used models such as the relative blur, Blur Equalization Technique, deconvolution based depth estimation, and subspace projection methods. We show that by optimizing the parameters of this general model we can obtain state-of-the-art result on synthetic and real defocused images with single or multiple defocused images with different apertures.
{"title":"Discriminative Filters for Depth from Defocus","authors":"Fahim Mannan, M. Langer","doi":"10.1109/3DV.2016.67","DOIUrl":"https://doi.org/10.1109/3DV.2016.67","url":null,"abstract":"Depth from defocus (DFD) requires estimating the depth dependent defocus blur at every pixel. Several approaches for accomplishing this have been proposed over the years. For a pair of images this is done by modeling the defocus relationship between the two differently defocused images and for single defocused images by relying on the the properties of the point spread function and the characteristics of the latent sharp image. We propose depth discriminative filters for DFD that can represent many of the widely used models such as the relative blur, Blur Equalization Technique, deconvolution based depth estimation, and subspace projection methods. We show that by optimizing the parameters of this general model we can obtain state-of-the-art result on synthetic and real defocused images with single or multiple defocused images with different apertures.","PeriodicalId":425304,"journal":{"name":"2016 Fourth International Conference on 3D Vision (3DV)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127406703","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ian Cherabier, Christian Häne, Martin R. Oswald, M. Pollefeys
Techniques that jointly perform dense 3D reconstruction and semantic segmentation have recently shown very promising results. One major restriction so far is that they can often only handle a very low number of semantic labels. This is mostly due to their high memory consumption caused by the necessity to store indicator variables for every label and transition. We propose a way to reduce the memory consumption of existing methods. Our approach is based on the observation that many semantic labels are only present at very localized positions in the scene, such as cars. Therefore this label does not need to be active at every location. We exploit this observation by dividing the scene into blocks in which generally only a subset of labels is active. By determining early on in the reconstruction process which labels need to be active in which block the memory consumption can be significantly reduced. In order to recover from mistakes we propose to update the set of active labels during the iterative optimization procedure based on the current solution. We also propose a way to initialize the set of active labels using a boosted classifier. In our experimental evaluation we show the reduction of memory usage quantitatively. Eventually, we show results of joint semantic 3D reconstruction and semantic segmentation with significantly more labels than previous approaches were able to handle.
{"title":"Multi-Label Semantic 3D Reconstruction Using Voxel Blocks","authors":"Ian Cherabier, Christian Häne, Martin R. Oswald, M. Pollefeys","doi":"10.1109/3DV.2016.68","DOIUrl":"https://doi.org/10.1109/3DV.2016.68","url":null,"abstract":"Techniques that jointly perform dense 3D reconstruction and semantic segmentation have recently shown very promising results. One major restriction so far is that they can often only handle a very low number of semantic labels. This is mostly due to their high memory consumption caused by the necessity to store indicator variables for every label and transition. We propose a way to reduce the memory consumption of existing methods. Our approach is based on the observation that many semantic labels are only present at very localized positions in the scene, such as cars. Therefore this label does not need to be active at every location. We exploit this observation by dividing the scene into blocks in which generally only a subset of labels is active. By determining early on in the reconstruction process which labels need to be active in which block the memory consumption can be significantly reduced. In order to recover from mistakes we propose to update the set of active labels during the iterative optimization procedure based on the current solution. We also propose a way to initialize the set of active labels using a boosted classifier. In our experimental evaluation we show the reduction of memory usage quantitatively. Eventually, we show results of joint semantic 3D reconstruction and semantic segmentation with significantly more labels than previous approaches were able to handle.","PeriodicalId":425304,"journal":{"name":"2016 Fourth International Conference on 3D Vision (3DV)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129324696","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper deals with the problem of estimating camera motion in the context of structure-from-motion. We describe a pipeline that consumes relative orientations and produces absolute orientations (i.e. camera position and attitude in an absolute reference frame). This pipeline exploits the concept of "group synchronization" in most of its stages, all of which entail direct solutions such as eigenvalue decompositions or linear least squares. A comprehensive introduction to the group synchronization problem is provided, and the proposed pipeline is evaluated on standard real datasets.
{"title":"Camera Motion from Group Synchronization","authors":"F. Arrigoni, Andrea Fusiello, B. Rossi","doi":"10.1109/3DV.2016.64","DOIUrl":"https://doi.org/10.1109/3DV.2016.64","url":null,"abstract":"This paper deals with the problem of estimating camera motion in the context of structure-from-motion. We describe a pipeline that consumes relative orientations and produces absolute orientations (i.e. camera position and attitude in an absolute reference frame). This pipeline exploits the concept of \"group synchronization\" in most of its stages, all of which entail direct solutions such as eigenvalue decompositions or linear least squares. A comprehensive introduction to the group synchronization problem is provided, and the proposed pipeline is evaluated on standard real datasets.","PeriodicalId":425304,"journal":{"name":"2016 Fourth International Conference on 3D Vision (3DV)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123713257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Kaan Yücer, Changil Kim, A. Sorkine-Hornung, O. Sorkine-Hornung
Objects with thin features and fine details are challenging for most multi-view stereo techniques, since such features occupy small volumes and are usually only visible in a small portion of the available views. In this paper, we present an efficient algorithm to reconstruct intricate objects using densely sampled light fields. At the heart of our technique lies a novel approach to compute per-pixel depth values by exploiting local gradient information in densely sampled light fields. This approach can generate accurate depth values for very thin features, and can be run for each pixel in parallel. We assess the reliability of our depth estimates using a novel two-sided photoconsistency measure, which can capture whether the pixel lies on a texture or a silhouette edge. This information is then used to propagate the depth estimates at high gradient regions to smooth parts of the views efficiently and reliably using edge-aware filtering. In the last step, the per-image depth values and color information are aggregated in 3D space using a voting scheme, allowing the reconstruction of a globally consistent mesh for the object. Our approach can process large video datasets very efficiently and at the same time generates high quality object reconstructions that compare favorably to the results of state-of-the-art multi-view stereo methods.
{"title":"Depth from Gradients in Dense Light Fields for Object Reconstruction","authors":"Kaan Yücer, Changil Kim, A. Sorkine-Hornung, O. Sorkine-Hornung","doi":"10.1109/3DV.2016.33","DOIUrl":"https://doi.org/10.1109/3DV.2016.33","url":null,"abstract":"Objects with thin features and fine details are challenging for most multi-view stereo techniques, since such features occupy small volumes and are usually only visible in a small portion of the available views. In this paper, we present an efficient algorithm to reconstruct intricate objects using densely sampled light fields. At the heart of our technique lies a novel approach to compute per-pixel depth values by exploiting local gradient information in densely sampled light fields. This approach can generate accurate depth values for very thin features, and can be run for each pixel in parallel. We assess the reliability of our depth estimates using a novel two-sided photoconsistency measure, which can capture whether the pixel lies on a texture or a silhouette edge. This information is then used to propagate the depth estimates at high gradient regions to smooth parts of the views efficiently and reliably using edge-aware filtering. In the last step, the per-image depth values and color information are aggregated in 3D space using a voting scheme, allowing the reconstruction of a globally consistent mesh for the object. Our approach can process large video datasets very efficiently and at the same time generates high quality object reconstructions that compare favorably to the results of state-of-the-art multi-view stereo methods.","PeriodicalId":425304,"journal":{"name":"2016 Fourth International Conference on 3D Vision (3DV)","volume":"297 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125151147","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The revolution of RGB-D sensors is advancing towards mobile platforms for robotics, autonomous vehicles and consumer hand-held devices. Strong pressures on power consumption and system price require new powerful algorithms that can robustly handle very low quality raw data. In this paper we demonstrate the ability to reliably recover depth measurements from a variety of highly degraded depth modalities, coupled with standard RGB imagery. The method is based on a regularizer which fuses super-pixel information with the total-generalized-variation (TGV) functional. We examine our algorithm on several different degradations, including new Intel's RealSense hand-held device, LiDAR-type data and ultra-sparse random sampling. In all modalities which are heavily degraded, our robust algorithm achieves superior performance over the state-ofthe-art. Additionally, a robust error measure based on Tukey's biweight metric is suggested, which is better at ranking algorithm performance since it does not reward blurry non-physical depth results.
{"title":"Robust Recovery of Heavily Degraded Depth Measurements","authors":"Gilad Drozdov, Yevgengy Shapiro, Guy Gilboa","doi":"10.1109/3DV.2016.15","DOIUrl":"https://doi.org/10.1109/3DV.2016.15","url":null,"abstract":"The revolution of RGB-D sensors is advancing towards mobile platforms for robotics, autonomous vehicles and consumer hand-held devices. Strong pressures on power consumption and system price require new powerful algorithms that can robustly handle very low quality raw data. In this paper we demonstrate the ability to reliably recover depth measurements from a variety of highly degraded depth modalities, coupled with standard RGB imagery. The method is based on a regularizer which fuses super-pixel information with the total-generalized-variation (TGV) functional. We examine our algorithm on several different degradations, including new Intel's RealSense hand-held device, LiDAR-type data and ultra-sparse random sampling. In all modalities which are heavily degraded, our robust algorithm achieves superior performance over the state-ofthe-art. Additionally, a robust error measure based on Tukey's biweight metric is suggested, which is better at ranking algorithm performance since it does not reward blurry non-physical depth results.","PeriodicalId":425304,"journal":{"name":"2016 Fourth International Conference on 3D Vision (3DV)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128467286","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Guislain, Julie Digne, R. Chaine, Dimitri Kudelski, Pascal Lefebvre-Albaret, Lefebvre-Albaret. Detecting
LiDAR (Light Detection And Ranging) acquisition is a widespread method for measuring urban scenes, be it a small town neighborhood or an entire city. It is even more interesting when this acquisition is coupled with a collection of pictures registered with the data, permitting to recover the color information of the points. Yet, this added color can be perturbed by shadows that are very dependent on the sun direction and weather conditions during the acquisition. In this paper, we focus on the problem of automatically detecting and correcting the shadows from the LiDAR data by exploiting both the images and the point set laser reflectance. Building on the observation that shadow boundaries are characterized by both a significant color change and a stable laser reflectance, we propose to first detect shadow boundaries in the point set and then segment ground shadows using graph cuts in the image. Finally using a simplified illumination model we correct the shadows directly on the colored point sets. This joint exploitation of both the laser point set and the images renders our approach robust and efficient, avoiding user interaction.
{"title":"Detecting and Correcting Shadows in Urban Point Clouds and Image Collections","authors":"M. Guislain, Julie Digne, R. Chaine, Dimitri Kudelski, Pascal Lefebvre-Albaret, Lefebvre-Albaret. Detecting","doi":"10.1109/3DV.2016.63","DOIUrl":"https://doi.org/10.1109/3DV.2016.63","url":null,"abstract":"LiDAR (Light Detection And Ranging) acquisition is a widespread method for measuring urban scenes, be it a small town neighborhood or an entire city. It is even more interesting when this acquisition is coupled with a collection of pictures registered with the data, permitting to recover the color information of the points. Yet, this added color can be perturbed by shadows that are very dependent on the sun direction and weather conditions during the acquisition. In this paper, we focus on the problem of automatically detecting and correcting the shadows from the LiDAR data by exploiting both the images and the point set laser reflectance. Building on the observation that shadow boundaries are characterized by both a significant color change and a stable laser reflectance, we propose to first detect shadow boundaries in the point set and then segment ground shadows using graph cuts in the image. Finally using a simplified illumination model we correct the shadows directly on the colored point sets. This joint exploitation of both the laser point set and the images renders our approach robust and efficient, avoiding user interaction.","PeriodicalId":425304,"journal":{"name":"2016 Fourth International Conference on 3D Vision (3DV)","volume":"26 10","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120922864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：