Stereo Neural Vernier Caliper

• URL: http://arxiv.org/abs/2203.11018v1
• Date: Mon, 21 Mar 2022 14:36:07 GMT
• Title: Stereo Neural Vernier Caliper
• Authors: Shichao Li, Zechun Liu, Zhiqiang Shen, Kwang-Ting Cheng
• Abstract summary: We propose a new object-centric framework for learning-based stereo 3D object detection. We tackle a problem of how to predict a refined update given an initial 3D cuboid guess. Our approach achieves state-of-the-art performance on the KITTI benchmark.
• Score: 57.187088191829886
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose a new object-centric framework for learning-based stereo 3D object detection. Previous studies build scene-centric representations that do not consider the significant variation among outdoor instances and thus lack the flexibility and functionalities that an instance-level model can offer. We build such an instance-level model by formulating and tackling a local update problem, i.e., how to predict a refined update given an initial 3D cuboid guess. We demonstrate how solving this problem can complement scene-centric approaches in (i) building a coarse-to-fine multi-resolution system, (ii) performing model-agnostic object location refinement, and (iii) conducting stereo 3D tracking-by-detection. Extensive experiments demonstrate the effectiveness of our approach, which achieves state-of-the-art performance on the KITTI benchmark. Code and pre-trained models are available at https://github.com/Nicholasli1995/SNVC.

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