Name
Abstract | ||
|---|---|---|
3D volumetric reasoning is important for truly understanding a scene. Humans are able to both segment each object in an image, and perceive a rich 3D interpretation of the scene, e.g., the space an object occupies, which objects support other objects, and which objects would, if moved, cause other objects to fall. We propose a new approach for parsing RGB-D images using 3D block units for volumetric reasoning. The algorithm fits image segments with 3D blocks, and iteratively evaluates the scene based on block interaction properties. We produce a 3D representation of the scene based on jointly optimizing over segmentations, block fitting, supporting relations, and object stability. Our algorithm incorporates the intuition that a good 3D representation of the scene is the one that fits the data well, and is a stable, self-supporting (i.e., one that does not topple) arrangement of objects. We experiment on several datasets including controlled and real indoor scenarios. Results show that our stability-reasoning framework improves RGB-D segmentation and scene volumetric representation. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/CVPR.2013.8 | CVPR |
Keywords | Field | DocType |
support,image representation,image segment,3d volumetric reasoning,scene volumetric representation,blocks,volumetric reasoning,3d based reasoning,image segmentation,block fitting,3d representation,rgb-d segmentation,rgb-d images,3d block units,object stability,3d interpretation,block unit,parsing rgb-d image,stability reasoning framework,stability,new approach,block interaction property,gravity,cognition,stability analysis,solid modeling,feature extraction | Computer vision,Scale-space segmentation,Pattern recognition,Computer science,Segmentation,Image representation,Intuition,Image segmentation,Artificial intelligence,RGB color model,Parsing | Conference |
Volume | Issue | ISSN |
2013 | 1 | 1063-6919 |
Citations | PageRank | References |
49 | 1.35 | 21 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| zhaoyin jia | 1 | 281 | 9.50 |
| Andrew Gallagher | 2 | 409 | 16.03 |
| Ashutosh Saxena | 3 | 4575 | 227.88 |
| Tsuhan Chen | 4 | 4763 | 346.32 |