ViP-DeepLab: Learning Visual Perception with Depth-aware Video Panoptic Segmentation

• URL: http://arxiv.org/abs/2012.05258v1
• Date: Wed, 9 Dec 2020 19:00:35 GMT
• Title: ViP-DeepLab: Learning Visual Perception with Depth-aware Video Panoptic Segmentation
• Authors: Siyuan Qiao, Yukun Zhu, Hartwig Adam, Alan Yuille, Liang-Chieh Chen
• Abstract summary: We present ViP-DeepLab, a unified model attempting to tackle the long-standing and challenging inverse projection problem in vision. ViP-DeepLab approaches it by jointly performing monocular depth estimation and video panoptic segmentation. On the individual sub-tasks, ViP-DeepLab achieves state-of-the-art results, outperforming previous methods by 5.1% VPQ on Cityscapes-VPS, ranking 1st on the KITTI monocular depth estimation benchmark, and 1st on KITTI MOTS pedestrian.
• Score: 31.078913193966585
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we present ViP-DeepLab, a unified model attempting to tackle the long-standing and challenging inverse projection problem in vision, which we model as restoring the point clouds from perspective image sequences while providing each point with instance-level semantic interpretations. Solving this problem requires the vision models to predict the spatial location, semantic class, and temporally consistent instance label for each 3D point. ViP-DeepLab approaches it by jointly performing monocular depth estimation and video panoptic segmentation. We name this joint task as Depth-aware Video Panoptic Segmentation, and propose a new evaluation metric along with two derived datasets for it, which will be made available to the public. On the individual sub-tasks, ViP-DeepLab also achieves state-of-the-art results, outperforming previous methods by 5.1% VPQ on Cityscapes-VPS, ranking 1st on the KITTI monocular depth estimation benchmark, and 1st on KITTI MOTS pedestrian. The datasets and the evaluation codes are made publicly available.

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