Multi-body Depth-Map Fusion with Non-intersection Constraints
نویسندگان
چکیده
Depthmap fusion is the problem of computing dense 3D reconstructions from a set of depthmaps. Whereas this problem has received a lot of attention for purely rigid scenes, there is remarkably little prior work for dense reconstructions of scenes consisting of several moving rigid bodies or parts. This paper therefore explores this multi-body depthmap fusion problem. A first observation in the multi-body setting is that when treated naively, ghosting artifacts will emerge, ie. the same part will be reconstructed multiple times at different positions. We therefore introduce non-intersection constraints which resolve these issues: at any point in time, a point in space can only be occupied by at most one part. Interestingly enough, these constraints can be expressed as linear inequalities and as such define a convex set. We therefore propose to phrase the multi-body depthmap fusion problem in a convex voxel labeling framework. Experimental evaluation shows that our approach succeeds in computing artifact-free dense reconstructions of the individual parts with a minimal overhead due to the non-intersection constraints.
منابع مشابه
Sdf-GAN: Semi-supervised Depth Fusion with Multi-scale Adversarial Networks
Fusing disparity maps from different algorithms to exploit their complementary advantages is still challenging. Uncertainty estimation and complex disparity relationships between neighboring pixels limit the accuracy and robustness of the existing methods and there is no common method for depth fusion of different kind of data. In this paper, we introduce a method to incorporate supplementary i...
متن کاملCovariance Scaled Sampling for Monocular 3D Body Tracking
We present a method for recovering 3D human body motion from monocular video sequences using robust image matching, joint limits and non-self-intersection constraints, and a new sample-andrefine search strategy guided by rescaled cost-function covariances. Monocular 3D body tracking is challenging: for reliable tracking at least 30 joint parameters need to be estimated, subject to highly nonlin...
متن کاملDepth Map Super-Resolution by Deep Multi-Scale Guidance
Depth boundaries often lose sharpness when upsampling from low-resolution (LR) depth maps especially at large upscaling factors. We present a new method to address the problem of depth map super resolution in which a high-resolution (HR) depth map is inferred from a LR depth map and an additional HR intensity image of the same scene. We propose a Multi-Scale Guided convolutional network (MSG-Ne...
متن کاملMulti-view dense depth map estimation
A novel dense depth map estimation algorithm is proposed in order to meet the requirements of N-view plus N-depth representation, which is one of the standardization efforts for the upcoming 3D display technologies. Hence, extraction of multiple depth maps is achieved from multi-view video. Starting from the piecewise planarity assumption of the scene, estimation of 3D structure of the patches,...
متن کاملHybridization of Facial Features and Use of Multi Modal Information for 3D Face Recognition
Despite of achieving good performance in controlled environment, the conventional 3D face recognition systems still encounter problems in handling the large variations in lighting conditions, facial expression and head pose The humans use the hybrid approach to recognize faces and therefore in this proposed method the human face recognition ability is incorporated by combining global and local ...
متن کامل