BundleTrack: 6D Pose Tracking for Novel Objects without Instance or Category-Level 3D Models

• URL: http://arxiv.org/abs/2108.00516v1
• Date: Sun, 1 Aug 2021 18:14:46 GMT
• Title: BundleTrack: 6D Pose Tracking for Novel Objects without Instance or Category-Level 3D Models
• Authors: Bowen Wen and Kostas Bekris
• Abstract summary: This work proposes BundleTrack, a general framework for 6D pose tracking of objects. An efficient implementation of the framework provides a real-time performance of 10Hz for the entire framework.
• Score: 1.14219428942199
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Tracking the 6D pose of objects in video sequences is important for robot manipulation. Most prior efforts, however, often assume that the target object's CAD model, at least at a category-level, is available for offline training or during online template matching. This work proposes BundleTrack, a general framework for 6D pose tracking of novel objects, which does not depend upon 3D models, either at the instance or category-level. It leverages the complementary attributes of recent advances in deep learning for segmentation and robust feature extraction, as well as memory-augmented pose graph optimization for spatiotemporal consistency. This enables long-term, low-drift tracking under various challenging scenarios, including significant occlusions and object motions. Comprehensive experiments given two public benchmarks demonstrate that the proposed approach significantly outperforms state-of-art, category-level 6D tracking or dynamic SLAM methods. When compared against state-of-art methods that rely on an object instance CAD model, comparable performance is achieved, despite the proposed method's reduced information requirements. An efficient implementation in CUDA provides a real-time performance of 10Hz for the entire framework. Code is available at: https://github.com/wenbowen123/BundleTrack

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