Leveraging Vision Foundation Models (VFMs) for camera-to-LiDAR knowledge distillation offers a promising solution to the scarcity of annotated data needed to represent the immense geometric and kinematic diversity of real-world autonomous driving (AD). However, current approaches typically treat VFMs as black-box teachers, relying exclusively on frame-wise feature similarity. Consequently, they do not fully exploit the teacher's layer-wise semantic structure and global context, as well as the rich spatiotemporal information inherent in LiDAR sequences. We propose HilDA, a self-supervised pretraining framework for LiDAR backbones that better captures the semantic what and geometric where needed for driving tasks. HilDA combines hierarchical distillation comprising multi-layer distillation for progressive semantic alignment and global context distillation for scene-level semantics, with a temporal occupancy diffusion objective promoting spatiotemporal consistency. Models pre-trained with HilDA achieve state-of-the-art results on cross-modal distillation benchmarks and outperform models trained via prior distillation approaches on 3D object detection, scene flow, and semantic occupancy prediction. Code available at: https://maxiuw.github.io/hilda.
ROS 2-Based LiDAR Perception Framework for Mobile Robots in Dynamic Production Environments, Utilizing Synthetic Data Generation, Transformation-Equivariant 3D Detection and Multi-Object Tracking
Chair of Intelligence in Quality Sensing (IQS), Laboratory for Machine Tools and Production Engineering (WZL), RWTH Aachen University（亚琛工业大学机床与生产工程实验室质量感知智能教席）；The Sirindhorn International Thai-German Graduate School of Engineering (TGGS), King Mongkut’s University of Technology North Bangkok (KMUTNB)（北曼谷先皇技术大学泰德国际工程研究生院）
University of Hong Kong（香港大学）；Harbin Institute of Technology（哈尔滨工业大学）
The emergence of low-cost, small form factor and light-weight solid-state LiDAR sensors have brought new opportunities for autonomous unmanned aerial vehicles (UAVs) by advancing navigation safety and computation efficiency. Yet the successful developments of LiDAR-based UAVs must rely on extensive simulations. Existing simulators can hardly perform simulations of real-world environments due to the requirements of dense mesh maps that are difficult to obtain. In this paper, we develop a point-realistic simulator of real-world scenes for LiDAR-based UAVs. The key idea is the underlying point re…
Clemson University（克莱姆森大学）；The City University of New York（纽约市立大学）；Carnegie Mellon University（卡内基梅隆大学）
Self-supervised monocular depth prediction provides a cost-effective solution to obtain the 3D location of each pixel. However, the existing approaches usually lead to unsatisfactory accuracy, which is critical for autonomous robots. In this paper, we propose FusionDepth, a novel two-stage network to advance the self-supervised monocular dense depth learning by leveraging low-cost sparse (e.g. 4-beam) LiDAR. Unlike the existing methods that use sparse LiDAR mainly in a manner of time-consuming iterative post-processing, our model fuses monocular image features and sparse LiDAR features to pred…
Towards Railway Domain Adaptation for LiDAR-based 3D Detection: Road-to-Rail and Sim-to-Real via SynDRA-BBox
Mobility Technologies（移动技术）；Department of Excellence in Robotics & AI, SETLabs Research GmbH（机器人与人工智能卓越部门，SETLabs Research GmbH）；Scuola Superiore Sant’Anna（圣安娜高等学院）
EvidenceMoE: A Physics-Guided Mixture-of-Experts with Evidential Critics for Advancing Fluorescence Light Detection and Ranging in Scattering Media
Center for Modeling, Simulation, & Imaging in Medicine（建模、仿真与成像医学中心）；Rensselaer Polytechnic Institute（伦斯勒理工学院）；University at Albany（阿尔巴尼大学）；IBM T.J. Watson Research Center（IBM 沃森研究中心）
ForestFormer3D: A Unified Framework for End-to-End Segmentation of Forest LiDAR 3D Point Clouds
Norwegian Institute of Bioeconomy Research (NIBIO)（挪威生物经济研究所）；Silva Tarouca Research Institute for Landscape and Ornamental Gardening（Silva Tarouca景观与园艺研究 institute）
The Road Ahead in Autonomous Driving: The KITScenes Multimodal Dataset
FZI Research Center for Information Technology（弗劳恩霍夫信息技术研究中心）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；University Charles III of Madrid（马德里第三大学）；Delft University of Technology（代尔夫特理工大学）
