Object Level Depth Reconstruction for Category Level 6D Object Pose Estimation From Monocular RGB Image

• URL: http://arxiv.org/abs/2204.01586v1
• Date: Mon, 4 Apr 2022 15:33:28 GMT
• Title: Object Level Depth Reconstruction for Category Level 6D Object Pose Estimation From Monocular RGB Image
• Authors: Zhaoxin Fan, Zhenbo Song, Jian Xu, Zhicheng Wang, Kejian Wu, Hongyan Liu and Jun He
• Abstract summary: We propose a novel approach named Object Level Depth reconstruction Network (OLD-Net) taking only RGB images as input for category-level 6D object pose estimation. We propose to directly predict object-level depth from a monocular RGB image by deforming the category-level shape prior into object-level depth and the canonical NOCS representation. Experiments on the challenging CAMERA25 and REAL275 datasets indicate that our model achieves state-of-the-art performance.
• Score: 12.382992538846896
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, RGBD-based category-level 6D object pose estimation has achieved promising improvement in performance, however, the requirement of depth information prohibits broader applications. In order to relieve this problem, this paper proposes a novel approach named Object Level Depth reconstruction Network (OLD-Net) taking only RGB images as input for category-level 6D object pose estimation. We propose to directly predict object-level depth from a monocular RGB image by deforming the category-level shape prior into object-level depth and the canonical NOCS representation. Two novel modules named Normalized Global Position Hints (NGPH) and Shape-aware Decoupled Depth Reconstruction (SDDR) module are introduced to learn high fidelity object-level depth and delicate shape representations. At last, the 6D object pose is solved by aligning the predicted canonical representation with the back-projected object-level depth. Extensive experiments on the challenging CAMERA25 and REAL275 datasets indicate that our model, though simple, achieves state-of-the-art performance.

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