arXiv Computer Vision
PRI-Net: A Lightweight Multimodal Framework for 3D UAV Localization
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arXiv Computer Vision
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FastBEVFusion: Simple Multi-Modal Fusion for 3D Object Detection at 50 FPS
FastBEVFusion is a lightweight fusion system for real‑time 3D object detection that combines LiDAR and camera data without depth estimation. It projects modality‑specific features into semi‑aligned BEV spaces and fuses them with a dynamic cross‑attention module, achieving over 50 FPS on the nuScenes dataset. The method also demonstrates strong robustness to calibration errors.
arXiv Computer Vision
Sep 22
Towards robust multimodal 3D object detection via visual foundation models
arXiv:2609.23541v1 Announce Type: new Abstract: Multimodal 3D object detection is fundamental to robust perception in autonomous driving because it integrates complementary information from LiDAR and...
arXiv Computer Vision
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RAHF-Net: Range-Aware Heterogeneous Fusion for LiDAR-Assisted RGB Semantic Segmentation
arXiv:2610.25400v2 Announce Type: replace Abstract: This paper studies LiDAR-assisted RGB semantic segmentation, where LiDAR point-cloud features improve dense pixel-wise predictions in the RGB image...
arXiv AI
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Single-Frame Point-Pixel Registration via Supervised Cross-Modal Feature Matching
arXiv:2506. 22784v2 Announce Type: replace-cross Abstract: Point-pixel registration between LiDAR point clouds and camera images is a fundamental yet challenging task in autonomous driving and robotic perception.
arXiv Computer Vision
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Leveraging Vision-Based Point Cloud Map Priors for Camera-Based 3D Object Detection and Online Vectorized HD Mapping
The paper presents a framework that builds a static point cloud prior map from past camera traversals, augmenting each point with DINOv3 semantic features. During runtime, a local prior patch is retrieved, encoded with a sparse voxel backbone, and fused with lifted multi‑view camera features in bird’s‑eye view. This fused representation is then used by sparse transformer heads to predict 3D objects and vectorized map elements, achieving improved performance on Argoverse 2 without requiring LiDAR for prior‑map construction or online inference.
arXiv Machine Learning
Sep 10
DAGLFNet: Deep Feature Attention Guided Global and Local Feature Fusion for Pseudo-Image Point Cloud Segmentation
arXiv:2510.10471v3 Announce Type: replace-cross Abstract: Environmental perception systems are crucial for high-precision mapping and autonomous navigation, with LiDAR serving as a core sensor provid...