SAIL-VOS 3D: A Synthetic Dataset and Baselines for Object Detection and 3D Mesh Reconstruction from Video Data

• URL: http://arxiv.org/abs/2105.08612v1
• Date: Tue, 18 May 2021 15:42:37 GMT
• Title: SAIL-VOS 3D: A Synthetic Dataset and Baselines for Object Detection and 3D Mesh Reconstruction from Video Data
• Authors: Yuan-Ting Hu, Jiahong Wang, Raymond A. Yeh, Alexander G. Schwing
• Abstract summary: We present SAIL-VOS 3D: a synthetic video dataset with frame-by-frame mesh annotations. We also develop first baselines for reconstruction of 3D meshes from video data via temporal models.
• Score: 124.2624568006391
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Extracting detailed 3D information of objects from video data is an important goal for holistic scene understanding. While recent methods have shown impressive results when reconstructing meshes of objects from a single image, results often remain ambiguous as part of the object is unobserved. Moreover, existing image-based datasets for mesh reconstruction don't permit to study models which integrate temporal information. To alleviate both concerns we present SAIL-VOS 3D: a synthetic video dataset with frame-by-frame mesh annotations which extends SAIL-VOS. We also develop first baselines for reconstruction of 3D meshes from video data via temporal models. We demonstrate efficacy of the proposed baseline on SAIL-VOS 3D and Pix3D, showing that temporal information improves reconstruction quality. Resources and additional information are available at http://sailvos.web.illinois.edu.

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