Hugging Face Trending Papers August 20, 2026

CVSD-Reg: Cross-Modal Visual Semantic Prior Distillation for Robust LiDAR Registration

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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. We propose CVSD-Reg, a robust global LiDAR registration framework that distills visual semantic priors from a vision foundation model into LiDAR representations.

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arXiv AI

1d ago

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.

By Eunsoo Im, Junghun Suh, Gyeonggwan Lee, Seunghwan Hong

llms rag efficiency multimodal benchmarks safety

arXiv AI

Jul 13

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.

By Yu Han, Zhiwei Huang, Yanting Zhang, Fangjun Ding, Shen Cai, Xiaoyu Tang, Yanchao Dong, Rui Fan

agents robotics efficiency multimodal benchmarks

arXiv AI

Jun 24

HilDA: Hierarchical Distillation with Diffusion for Advancing Self-Supervised LiDAR Pre-training

arXiv:2606. 20189v3 Announce Type: replace-cross Abstract: 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).

By Maciej Wozniak, Jesper Ericsson, Hariprasath Govindarajan, Truls Nyberg, Thomas Gustafsson, Patric Jensfelt, Olov Andersson

agents diffusion computer-vision robotics efficiency multimodal benchmarks safety

arXiv Machine Learning

Jul 14

TOLiD: Bridging the Architecture Gap in Vision Foundation Model to LiDAR Pretraining via Token Lifting for Distillation

arXiv:2607. 10762v1 Announce Type: cross Abstract: Cross-modal distillation from Vision Foundation Models (VFMs) to LiDAR backbones has recently emerged as a self-supervised pretraining strategy that reduces reliance on dense point-wise annotation for 3D scene understanding.

By Sutharsan Mahendran, Darshana Priyasad, Kaushik Roy, Tharindu Fernando, Sridha Sridharan, Clinton Fookes, Peyman Moghadam

llms rag computer-vision efficiency multimodal

arXiv AI

Jun 19

HilDA: Hierarchical Distillation with Diffusion for Advancing Self-Supervised LiDAR Pre-trainin

arXiv:2606. 20189v1 Announce Type: cross Abstract: 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).

By Maciej Wozniak, Jesper Ericsson, Hariprasath Govindarajan, Truls Nyberg, Thomas Gustafsson, Patric Jensfelt, Olov Andersson

agents diffusion computer-vision robotics efficiency multimodal benchmarks safety

arXiv Machine Learning

Aug 11

Unsupervised Point Cloud Registration with Self-Distillation

arXiv:2409. 07558v2 Announce Type: replace-cross Abstract: Rigid point cloud registration is a fundamental problem and highly relevant in robotics and autonomous driving.

By Christian L\"owens, Thorben Funke, Andr\'e Wagner, Alexandru Paul Condurache

agents robotics efficiency benchmarks

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CVSD-Reg: Cross-Modal Visual Semantic Prior Distillation for Robust LiDAR Registration

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