Name
Title | ||
|---|---|---|
3DFS: Deformable Dense Depth Fusion and Segmentation for Object Reconstruction from a Handheld Camera. |
Abstract | ||
|---|---|---|
We propose an approach for 3D reconstruction and segmentation of a single object placed on a flat surface from an input video. Our approach is to perform dense depth map estimation for multiple views using a proposed objective function that preserves detail. The resulting depth maps are then fused using a proposed implicit surface function that is robust to estimation error, producing a smooth surface reconstruction of the entire scene. Finally, the object is segmented from the remaining scene using a proposed 2D-3D segmentation that incorporates image and depth cues with priors and regularization over the 3D volume and 2D segmentations. We evaluate 3D reconstructions qualitatively on our Object-Videos dataset, comparing to fusion, multiview stereo, and segmentation baselines. We also quantitatively evaluate the dense depth estimation using the RGBD Scenes V2 dataset [Henry et al. 2013] and the segmentation using keyframe annotations of the Object-Videos dataset. |
Year | Venue | Field |
|---|---|---|
2016 | arXiv: Computer Vision and Pattern Recognition | Computer vision,Surface reconstruction,Scale-space segmentation,Pattern recognition,Computer science,Segmentation,Segmentation-based object categorization,Regularization (mathematics),Artificial intelligence,Depth perception,Depth map,3D reconstruction |
DocType | Volume | Citations |
Journal | abs/1606.05002 | 1 |
PageRank | References | Authors |
0.36 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tanmay Gupta | 1 | 2 | 2.41 |
| Daeyun Shin | 2 | 7 | 2.13 |
| Naren Sivagnanadasan | 3 | 4 | 0.76 |
| Derek Hoiem | 4 | 4998 | 302.66 |