arXiv Machine Learning
Jun 2
HyperDet: 3D Object Detection with Hyper 4D Radar Point Clouds
arXiv:2602. 11554v3 Announce Type: replace-cross Abstract: How far can 3D object detection go using 4D radar alone?
RLG-TPV: Radar- and LiDAR-Guided Tri-Perspective View Fusion for Camera-Radar 3D Object Detection
RLG-TPV introduces a multimodal Tri-Perspective View framework that fuses camera, radar, and training‑time LiDAR data for 3D object detection. It uses radar and LiDAR to guide a ray‑deformable attention lift, refining depth distributions and providing geometric supervision for side and front planes, while radar cross‑section awareness spreads evidence spatially. On nuScenes, the method attains 0.4981 mAP and 0.5959 NDS, improving orientation and velocity accuracy by about 32 % and 31 % over the CRN baseline.
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arXiv AI
Jul 13
4DR360: State Reasoning for Joint 3D Detection and Occupancy Prediction in 4D Radar-Camera Full-Scene Perception
arXiv:2607. 09629v1 Announce Type: cross Abstract: Reliable autonomous driving requires full-scene perception that couples foreground objects with dense semantic layout.
Hugging Face Trending Papers
Jun 8
ATN3D: Density-Aware LiDAR-Radar Early 3D Object Detection Under Extreme Sparsity
3D object detection is the backbone of perception for automated vehicles (AV) and broader intelligent transportation systems applications. Long-range detection is challenging because sensing evidence is sparse; yet this ``long-range'' scenario is routine in traffic.
arXiv AI
Jul 7
CRISP: A Spatiotemporal Camera-Radar Backbone for Driving via Forecasting-Based World-Model Pretraining
arXiv:2607. 04541v1 Announce Type: cross Abstract: Camera-radar (CR) fusion is a practical sensing configuration for autonomous driving, but existing models are typically trained with task-specific supervision, limiting reusable representation learning.
arXiv AI
Jun 9
ATN3D: Density-Aware LiDAR-Radar Early 3D Object Detection Under Extreme Sparsity
arXiv:2606. 09634v1 Announce Type: cross Abstract: 3D object detection is the backbone of perception for automated vehicles (AV) and broader intelligent transportation systems applications.
arXiv AI
Aug 11
Geometry Beats Estimated Depth: RGB-Only Multi-Camera 3D Tracking under Sim2Real
arXiv:2608. 07579v1 Announce Type: cross Abstract: The AI City Challenge 2026 Track 1 evaluates multi-camera 3D perception in large indoor warehouses under a synthetic-to-real (Sim2Real) setting; depth is available only for training and validation, so inference is RGB-only.
arXiv Computer Vision
1d ago
Stereo 4D Radar for 3D Object Detection: Integrating Geometric Alignment and Absolute Velocity Estimation
arXiv:2609.02560v1 Announce Type: new Abstract: Four-dimensional (4D) Radar is a powerful sensing modality capable of detecting surrounding three-dimensional (3D) objects under diverse weather condit...
arXiv Computer Vision
1d ago
If It Moves, Radar Knows: A Physics-Aware Radar Transformer for Class-Agnostic Moving-Object Detection
The paper introduces the Physics-Aware Radar Transformer (PART), a radar-only detector that predicts moving-object existence, surface points, and ground-plane velocity using Doppler-aware query initialization and physics-guided cross-attention. PART achieves high class-agnostic performance on the nuScenes dataset, excelling in rare categories and adverse conditions such as night, rain, and occlusion. The model is lightweight, with only 1.1 million parameters, and its code and pretrained weights will be released publicly.
arXiv AI
Jul 7
Double-Helix Active Geometry: LiDAR-Anchored Multi-View Depth with Selective Abstention
arXiv:2607. 02561v1 Announce Type: cross Abstract: Consumer depth sensors such as the LiDAR scanner on recent iPhones provide metric range, but their useful range is short and their returns are sparse.
arXiv AI
Aug 21
CVSD-Reg: Cross-Modal Visual Semantic Prior Distillation for Robust LiDAR Registration
arXiv:2608. 19536v1 Announce Type: cross Abstract: Learning-based global point cloud registration has achieved remarkable progress, yet its reliance on geometric representations makes existing methods sensitive to variations in point density, scan pattern, viewpoint, and sensor characteristics.
arXiv AI
Jul 21
Li-ViP3D++: Query-Gated Deformable Camera-LiDAR Fusion for End-to-End Perception and Trajectory Prediction
arXiv:2601. 20720v2 Announce Type: replace-cross Abstract: End-to-end perception and trajectory prediction from raw sensor data is one of the key capabilities for autonomous driving.
arXiv Computer Vision
Aug 24
Vision Foundation Model Driven Foreground-Aware Pseudo-LiDAR Generation for Monocular 3D Object Detection
arXiv:2404.09431v3 Announce Type: replace Abstract: Pseudo-LiDAR has become a promising paradigm for monocular 3D object detection by transforming monocular images into point cloud representations th...