3DNN: Viewpoint Invariant 3D Geometry Matching for Scene Understanding
Venue
Proceedings of the International Conference on Computer Vision (ICCV) (2013) (to appear)
Publication Year
2013
Authors
Scott Satkin, Martial Hebert
BibTeX
Abstract
We present a new algorithm 3DNN (3D Nearest-Neighbor), which is capable of matching
an image with 3D data, independently of the viewpoint from which the image was
captured. By leveraging rich annotations associated with each image, our algorithm
can automatically produce precise and detailed 3D models of a scene from a single
image. Moreover, we can transfer information across images to accurately label and
segment objects in a scene. The true benefit of 3DNN compared to a traditional 2D
nearest-neighbor approach is that by generalizing across viewpoints, we free
ourselves from the need to have training examples captured from all possible
viewpoints. Thus, we are able to achieve comparable results using orders of
magnitude less data, and recognize objects from never-before-seen viewpoints. In
this work, we describe the 3DNN algorithm and rigorously evaluate its performance
for the tasks of geometry estimation and object detection/segmentation. By
decoupling the viewpoint and the geometry of an image, we develop a scene matching
approach which is truly 100% viewpoint invariant, yielding state-of-the-art
performance on challenging data.
