CMR3D: Contextualized Multi-Stage Refinement for 3D Object Detection
Authors: 
Dhanalaxmi Gaddam, Jean Lahoud, Fahad Shahbaz Khan, Rao Muhammad Anwer, Hisham CholakkalAuthors Info & Claims
Article No.: 27, Pages 1 - 8
Abstract
Existing deep learning-based 3D object detectors typically rely on the appearance of individual objects and do not explicitly pay attention to the rich contextual information of the scene. In this work, we propose Contextualized Multi-Stage Refinement for 3D Object Detection (CMR3D) framework, which takes a 3D scene as an input and strives to explicitly integrate useful contextual information of the scene at multiple levels to predict a set of object bounding-boxes along with their corresponding semantic labels. To this end, we propose to utilize a context enhancement network that captures the contextual information at different levels of granularity followed by a multi-stage refinement module to progressively refine the box positions and class predictions. Extensive experiments on the large-scale ScanNetV2 benchmark reveals the benefits of our proposed method, leading to an absolute improvement of 2.0% over the baseline. In addition to 3D object detection, we investigate the effectiveness of our CMR3D framework for the problem of 3D object counting. Our source code is available at https://github.com/Dhanalaxmi17/CMR3D.
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Index Terms
- CMR3D: Contextualized Multi-Stage Refinement for 3D Object Detection
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Published In
December 2022
296 pages
ISBN:9781450394789
DOI:10.1145/3551626
- Conference Chair:
- Shuqiang Jiang,
- General Chairs:
- Kiyoharu Aizawa,
- Phoebe Chen,
- Keiji Yanai
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Published: 13 December 2022
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