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Poster
MultiScan: Scalable RGBD scanning for 3D environments with articulated objects
Yongsen Mao · Yiming Zhang · Hanxiao Jiang · Angel Chang · Manolis Savva
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.
Author Information
Yongsen Mao (Simon Fraser University)
Yiming Zhang (Simon Fraser University)
Hanxiao Jiang (Simon Fraser University)
Angel Chang (Simon Fraser University)
Manolis Savva (Simon Fraser University)
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