MultiScan: Scalable RGBD scanning for 3D environments with articulated objects
Yongsen Mao, Yiming Zhang, Hanxiao Jiang, Angel X Chang, Manolis Savva
Abstract
We introduce MultiScan, a scalable RGBD dataset construction pipeline leveraging commodity mobile devices to scan indoor scenes with articulated objects and web-based semantic annotation interfaces to efficiently annotate object and part semantics and part mobility parameters. We use this pipeline to collect 273 scans of 117 indoor scenes containing 10957 objects and 5129 parts. The resulting MultiScan dataset provides RGBD streams with per-frame camera poses, textured 3D surface meshes, richly annotated part-level and object-level semantic labels, and part mobility parameters. We validate our dataset on instance segmentation and part mobility estimation tasks and benchmark methods for these tasks from prior work. Our experiments show that part segmentation and mobility estimation in real 3D scenes remain challenging despite recent progress in 3D object segmentation.
BibTeX
@inproceedings{
mao2022multiscan,
title={MultiScan: Scalable {RGBD} scanning for 3D environments with articulated objects},
author={Yongsen Mao and Yiming Zhang and Hanxiao Jiang and Angel X Chang and Manolis Savva},
booktitle={Advances in Neural Information Processing Systems},
editor={Alice H. Oh and Alekh Agarwal and Danielle Belgrave and Kyunghyun Cho},
year={2022},
url={https://openreview.net/forum?id=YxUdazpgweG}
}