RGB-D object discovery via multi-scene analysis

We introduce an algorithm for object discovery from RGB-D (color plus depth) data, building on recent progress in using RGB-D cameras for 3-D reconstruction. A set of 3-D maps are built from multiple visits to the same scene. We introduce a multi-scene MRF model to detect objects that moved between...

Full description

Saved in:
Bibliographic Details
Main Authors: Herbst, Evan, Ren, Xiaofeng, Fox, Dieter
Format: Conference Proceeding
Language:English
Subjects:
Image color analysis
Image segmentation
Iterative closest point algorithm
Motion segmentation
Shape
Vectors
Visualization
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:We introduce an algorithm for object discovery from RGB-D (color plus depth) data, building on recent progress in using RGB-D cameras for 3-D reconstruction. A set of 3-D maps are built from multiple visits to the same scene. We introduce a multi-scene MRF model to detect objects that moved between visits, combining shape, visibility, and color cues. We measure similarities between candidate objects using both 2-D and 3-D matching, and apply spectral clustering to infer object clusters from noisy links. Our approach can robustly detect objects and their motion between scenes even when objects are textureless or have the same shape as other objects.
ISSN:2153-0858
2153-0866
DOI:10.1109/IROS.2011.6095116