IEEE

Clustering based Point Cloud Representation Learning for 3D Analysis

Abstract: Point cloud analysis (such as 3D segmentation and detection) is a challenging task, because of not only the irregular geometries of many millions of unordered points, but also the great ...
IEEE

Exploring Self-Supervised Learning for 3D Point Cloud Registration

Abstract: Self-supervised learning has achieved significant success in various fields such as point cloud detection and segmentation. However, self-supervised learning for point cloud registration is ...
IEEE

Toward Visual Interaction: Hand Segmentation by Combining 3-D Graph Deep Learning and Laser Point Cloud for Intelligent Rehabilitation

Abstract: Against the backdrop of the increasing trend of aging population in China and even globally, the demand for hand function rehabilitation is growing day by day, and human-machine interaction ...
IEEE

Deep Learning-Based Pedestrian Detection Using RGB Images and Sparse LiDAR Point Clouds

Abstract: One of the fundamental tasks in autonomous driving is environment perception for pedestrian detection, where the fused pedestrian detection using camera and light detection and ranging ...
IEEE

LLM-Guided Cross-Modal Point Cloud Quality Assessment: A Graph Learning Approach

Abstract: This paper addresses the critical need for accurate and reliable point cloud quality assessment (PCQA) in various applications, such as autonomous driving, robotics, virtual reality, and 3D ...
IEEE

PointOBB: Learning Oriented Object Detection via Single Point Supervision

Abstract: Single point-supervised object detection is gaining attention due to its cost-effectiveness. However, existing approaches focus on generating horizontal bounding boxes (HBBs) while ignoring ...
IEEE

GraphI2P: Image-to-Point Cloud Registration with Exploring Pattern of Correspondence via Graph Learning

Abstract: Although the fusion of images and LiDAR point clouds is crucial to many applications in computer vision, the relative poses of cameras and LiDAR scanners are often unknown. However, due to ...
IEEE

Parameter-efficient Prompt Learning for 3D Point Cloud Understanding

Abstract: This paper presents a parameter-efficient prompt tuning method, named PPT, to adapt a large multi-modal model for 3D point cloud understanding. Existing strategies are quite expensive in ...
IEEE

Hard-Soft Pseudo Labels Guided Semi-Supervised Learning for Point Cloud Classification

Point clouds are widely applied in 3D visual sensing and perception. However, manually annotating point clouds is much more tedious and time-consuming than that for 2D images. Fortunately, ...