Go with the flow: hand trajectories in 3d via clustered scene flow
ICIAR'12 Proceedings of the 9th international conference on Image Analysis and Recognition - Volume Part I
Robust 3d action recognition with random occupancy patterns
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
A tensor voting approach for multi-view 3d scene flow estimation and refinement
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
Consistent Binocular Depth and Scene Flow with Chained Temporal Profiles
International Journal of Computer Vision
Dense scene flow based on depth and multi-channel bilateral filter
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
Local scene flow by tracking in intensity and depth
Journal of Visual Communication and Image Representation
| Hi-index | 0.00 |
The motion field of a scene can be used for object segmentation and to provide features for classification tasks like action recognition. Scene flow is the full 3D motion field of the scene, and is more difficult to estimate than it's 2D counterpart, optical flow. Current approaches use a smoothness cost for regularisation, which tends to over-smooth at object boundaries. This paper presents a novel formulation for scene flow estimation, a collection of moving points in 3D space, modelled using a particle filter that supports multiple hypotheses and does not oversmooth the motion field. In addition, this paper is the first to address scene flow estimation, while making use of modern depth sensors and monocular appearance images, rather than traditional multi-viewpoint rigs. The algorithm is applied to an existing scene flow dataset, where it achieves comparable results to approaches utilising multiple views, while taking a fraction of the time.