Polar Parametrization for Vision-based Surround-View 3D Detection
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Abstract (arXiv)
3D detection based on surround-view camera system is a critical technique in autopilot. In this work, we present Polar Parametrization for 3D detection, which reformulates position parametrization, velocity decomposition, perception range, label assignment and loss function in polar coordinate system. Polar Parametrization establishes explicit associations between image patterns and prediction targets, exploiting the view symmetry of surround-view cameras as inductive bias to ease optimization and boost performance. Based on Polar Parametrization, we propose a surround-view 3D DEtection TRansformer, named PolarDETR. PolarDETR achieves promising performance-speed trade-off on different backbone configurations. Besides, PolarDETR ranks 1st on the leaderboard of nuScenes benchmark in terms of both 3D detection and 3D tracking at the submission time (Mar. 4th, 2022). Code will be released at urlhttps://github.com/hustvl/PolarDETR.
Key facts. Cited by 53 papers in the map (data through 7 Oct 2026). Builds on 53 of its 63 references that are in the map. First posted on arXiv 22 Jun 2022.
Builds on
53 of 63 references.
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- 2020PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentationcited by 274
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- 2021Depth-conditioned Dynamic Message Propagation for Monocular 3D Object Detectioncited by 84
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50 most cited of 53 All 53 (JSON)
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Citing papers per year: 2022: 12; 2023: 20; 2024: 12; 2025: 7; 2026: 2.
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12 newest of 48 All 48 (JSON)