RLG-TPV: Radar- and LiDAR-Guided Tri-Perspective View Fusion for Camera-Radar 3D Object Detection

• URL: http://arxiv.org/abs/2608.29194v1
• Date: Sat, 29 Aug 2026 11:08:11 GMT
• Title: RLG-TPV: Radar- and LiDAR-Guided Tri-Perspective View Fusion for Camera-Radar 3D Object Detection
• Abstract summary: Tri-Perspective View (TPV) representations describe 3D scene structure through top, side, and front feature planes.<n>We propose RLG-TPV, a framework for camera-radar 3D object detection in which radar and training-time LiDAR provide complementary geometric guidance.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Tri-Perspective View (TPV) representations describe 3D scene structure through top, side, and front feature planes, but existing TPV lifting is primarily camera-based, leaving the depth of sampled image evidence ambiguous along projected camera rays. We propose RLG-TPV, a multimodal TPV framework for camera-radar 3D object detection in which radar and training-time LiDAR provide complementary geometric guidance during representation construction. A ray-guided deformable-attention lift weights sampled image features using LiDAR-supervised camera depth probabilities and radar frustum occupancy, while radar additionally refines the depth distribution before lifting. Because conventional radar provides limited elevation information, LiDAR-derived class-occupancy targets supervise the side and front planes during training; the corresponding heads are removed at inference, so deployment requires only cameras and radar. For temporal aggregation, Doppler-guided temporal fusion aligns past features using a motion field anchored by measured radar radial velocity, with gating that limits warping in regions without supported motion. An RCS-aware radar scatter further allows radar evidence to spread over spatial neighborhoods conditioned on radar cross section. On the nuScenes validation set, RLG-TPV achieves 0.4981 mAP and 0.5959 NDS, reducing orientation and velocity error by 31.9\% and 30.7\% relative to the published CRN baseline. Ablation studies show that ray-level geometric guidance is a major contributor to the final performance.

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