Aligning 3D Models to RGB-D Images of Cluttered Scenes
Saurabh Gupta, Pablo Arbelaez, Ross Girshick, Jitendra Malik
Abstract
The goal of this work is to represent objects in an RGB-D scene with corresponding 3D models from a library. We approach this problem by first detecting and segmenting object instances in the scene and then using a convolutional neural network (CNN) to predict the pose of the object. This CNN is trained using pixel surface normals in images containing renderings of synthetic objects. When tested on real data, our method outperforms alternative algorithms trained on real data. We then use this coarse pose estimate along with the inferred pixel support to align a small number of prototypical models to the data, and place into the scene the model that fits best. We observe a 48% relative improvement in performance at the task of 3D detection over the current state-of-the-art, while being an order of magnitude faster.
BibTeX
@inproceedings{cvpr2015_aligning3dmodels,
title = {Aligning 3D Models to RGB-D Images of Cluttered Scenes},
author = {Saurabh Gupta and Pablo Arbelaez and Ross Girshick and Jitendra Malik},
booktitle = {CVPR 2015},
year = {2015}
}