arXiv Computer Vision

Unleashing the Potential of Mamba: Boosting a LiDAR 3D Sparse Detector by Using Cross-Model Knowledge Distillation

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 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

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 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...

By Ziying Song, Lin Liu, Hongyu Pan, Shaoqing Xu, Lei Yang, Mingzhe Guo, Caiyan Jia

agents computer-vision robotics multimodal benchmarks

Hugging Face Trending Papers

Aug 20

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

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.

llms rag efficiency multimodal benchmarks safety

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.

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

llms rag efficiency multimodal benchmarks safety

arXiv Computer Vision

Oct 15

Into the LidarVerse: A Real-Time Unified LiDAR World Model Across Tasks and Sensors

arXiv:2610.19201v1 Announce Type: new Abstract: We present LidarVerse, a real-time unified LiDAR world model for sensor simulation across tasks and sensors. Existing LiDAR simulators largely follow o...

By Henry Che, Xuanchi Ren, Sanja Fidler, Shenlong Wang, Laura Leal-Taix\'e, Shengyu Huang

diffusion nlp benchmarks

arXiv AI

Aug 21

Open-Vocabulary 3D Object Detection with Co-Distillation Discovery and Dual Guidance Robust Training

arXiv:2608. 19973v1 Announce Type: cross Abstract: Recently, open-vocabulary 3D object detection (3D-OVD) has gained increasing attention for its ability to detect unseen objects in 3D scenes.

By Shangbo Yuan, Jie Xu, Xiaofeng Zhu, Na Zhao

llms computer-vision efficiency benchmarks safety

Hugging Face Trending Papers

Oct 13

Into the LidarVerse: A Real-Time Unified LiDAR World Model Across Tasks and Sensors

We present LidarVerse, a real-time unified LiDAR world model for sensor simulation across tasks and sensors. Existing LiDAR simulators largely follow one of two complementary paradigms: reconstruction...

diffusion nlp benchmarks

arXiv Computer Vision

Sep 15

Unsupervised Point Cloud Registration via Training-Time Semantic Guidance

arXiv:2609.15228v1 Announce Type: new Abstract: Unsupervised registration of large-scale LiDAR point clouds remains challenging due to the geometric ambiguity inherent in outdoor scenes, which degrad...

By Kezheng Xiong, Shiyun Xu, Sheng Ao, Siqi Shen, Cheng Wang, Chenglu Wen

computer-vision efficiency benchmarks

arXiv Computer Vision

Sep 21

Refine Then Fusion: Training-Free 3D Point Cloud Adaptation with Priority Refinement and Multi-Modal Knowledge Fusion

arXiv:2609.21522v1 Announce Type: new Abstract: Recent pre-trained foundation models provide rich multi-modal priors for downstream 3D vision tasks. However, the effectiveness of these representation...

By Hang Cheng, Yan Chen, Mingyu Fan, Long Zeng

multimodal benchmarks

arXiv AI

Jun 3

Towards Compact Autonomous Driving Perception with Balanced Learning and Multi-sensor Fusion

arXiv:2606. 02979v1 Announce Type: cross Abstract: We present a novel compact deep multi-task learning model to handle various autonomous driving perception tasks in one forward pass.

By Oskar Natan, Jun Miura

agents computer-vision robotics benchmarks

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