Accurate detection of drivable area is essential for autonomous driving and advanced driver assistance systems. While state-of-the-art methods focus on general traversability estimation, they prove inadequate for snow-covered roads where following existing wheel tracks offers crucial advantages: improved traction, predictable vehicle dynamics, and verified path stability. To address this, we propose a low-cost, self-supervised approach detecting safety-critical wheel tracks using synchronized RGB-I (RGB + Long-Wave Infrared) imagery. Our method automatically generates training labels by projecting future wheel trajectories from recorded GNSS data. A neural network is designed to identify wheel tracks with the 4-channel RGB-I images from challenging, white-out road surfaces. Experimental results demonstrate the effectiveness of our approach in visually monotonous and featureless adverse environment, offering a robust and cost-effective alternative for winter autonomy. The newly introduced ThermalTrack dataset is available at: https://digitalcommons.mtu.edu/thermaltrack/.
The Hong Kong University of Science and Technology（香港科技大学）
The segmentation of drivable areas and road anomalies are critical capabilities to achieve autonomous navigation for robotic wheelchairs. The recent progress of semantic segmentation using deep learning techniques has presented effective results. However, the acquisition of large-scale datasets with hand-labeled ground truth is time-consuming and labor-intensive, making the deep learning-based methods often hard to implement in practice. We contribute to the solution of this problem for the task of drivable area and road anomaly segmentation by proposing a self-supervised learning approach. We…
Words to Wheels: Vision-Based Autonomous Driving Understanding Human Language Instructions Using Foundation Models
Korea Advanced Institute of Science and Technologies (KAIST)（韩国科学技术院（KAIST））；Electronics and Telecommunications Research Institute (ETRI)（电子通信研究院（ETRI））
Invisible Gas Detection: An RGB-Thermal Cross Attention Network and A New Benchmark
Southern University of Science and Technology（南方科技大学）；Shenzhen Technology University（深圳技术大学）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）
DepthVanish: Optimizing Adversarial Interval Structures for Stereo-Depth-Invisible Patches
University of Alberta（阿尔伯塔大学）；VCIP, CS, Nankai University（VCIP、计算机科学系、南开大学）；NKIARI, Shenzhen Futian（NKIARI、深圳福田）；The University of Tokyo（东京大学）；CFAR and IHPC, Agency for Science, Technology and Research (A*STAR)（CFAR和IHPC、科技研究局（A*STAR））；Nanyang Technological University（南洋理工大学）
HybridOcc: NeRF Enhanced Transformer-based Multi-Camera 3D Occupancy Prediction
Academy for Engineering and Technology, Fudan University（复旦大学工程与技术学院）；China FAW Group Corp., Ltd.（中国第一汽车集团有限公司）；Engineering Research Center of AI and Robotics, Ministry of Education（教育部人工智能与机器人工程研究中心）；Jilin Provincial Key Laboratory of Intelligence Science and Engineering（吉林省智能科学与工程重点实验室）
Crowd counting aims to estimate the number of persons in a scene. Most state-of-the-art crowd counting methods based on color images can't work well in poor illumination conditions due to invisible objects. With the widespread use of infrared cameras, crowd counting based on color and thermal images is studied. Existing methods only achieve multi-modal fusion without count objective constraint. To better excavate multi-modal information, we use count-guided multi-modal fusion and modal-guided count enhancement to achieve the impressive performance. The proposed count-guided multi-modal fusion …
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；EarthSense Inc.（EarthSense公司）；DEVCOM Army Research Lab（DEVCOM陆军研究实验室）
We present a self-supervised approach for learning to predict traversable paths for wheeled mobile robots that require good traction to navigate. Our algorithm, termed WayFAST (Waypoint Free Autonomous Systems for Traversability), uses RGB and depth data, along with navigation experience, to autonomously generate traversable paths in outdoor unstructured environments. Our key inspiration is that traction can be estimated for rolling robots using kinodynamic models. Using traction estimates provided by an online receding horizon estimator, we are able to train a traversability prediction neural…
A self-supervised and adversarial approach to hyperspectral demosaicking and RGB reconstruction in surgical imaging
School of Biomedical Engineering & Imaging Sciences King’s College London London, UK（生物医学工程与成像科学学院 英国伦敦国王学院）；Department of Neurosurgery King’s College Hospital NHS Foundation Trust, London, UK（神经外科部门 英国伦敦国王学院医院 NHS基金会信托）
We propose POse-guided SElective Fusion (POSEFusion), a single-view human volumetric capture method that leverages tracking-based methods and tracking-free inference to achieve high-fidelity and dynamic 3D reconstruction. By contributing a novel reconstruction framework which contains pose-guided keyframe selection and robust implicit surface fusion, our method fully utilizes the advantages of both tracking-based methods and tracking-free inference methods, and finally enables the high-fidelity reconstruction of dynamic surface details even in the invisible regions. We formulate the keyframe s…
See the past: Time-Reversed Scene Reconstruction from Thermal Traces Using Visual Language Models
Physics School, Universidad Industrial de Santander, Colombia（圣安德烈大学物理系，哥伦比亚）；Department of Computer Science, Universidad Industrial de Santander, Colombia（圣安德烈大学计算机科学系，哥伦比亚）；GIPSA-Lab, Université Grenoble Alpes, CNRS, Grenoble INP, Grenoble, France（格拉斯实验室，格勒诺布尔阿尔卑斯大学，CNRS，格勒诺布尔INP，法国）；Institut Universitaire de France (IUF), France（法国国家科学院（IUF））
