IEEE

Learning Point Cloud Completion without Complete Point Clouds: A Pose-Aware Approach

Abstract: Point cloud completion is to restore complete 3D scenes and objects from incomplete observations or limited sensor data. Existing fully-supervised methods rely on paired datasets of ...
IEEE

Deep Learning based Fusion of LiDAR Point Cloud and Multispectral Imagery for Crop Classification Sensitive to Nitrogen Level

Abstract: Deep learning techniques have been evolving at a faster pace offering a common framework for developing models for various applications using remote sensing data. Availability of high ...
IEEE

Deep Learning Based Point Cloud Processing Techniques

Abstract: In this study, deep learning techniques and algorithms used in point cloud processing have been analysed. Methods, technical properties and algorithms developed for 3D Object Classification ...
IEEE

Contrastive Learning for Joint Normal Estimation and Point Cloud Filtering

Abstract: Point cloud filtering and normal estimation are two fundamental research problems in the 3D field. Existing methods usually perform normal estimation and filtering separately and often show ...
IEEE

JIMR: Joint Semantic and Geometry Learning for Point Scene Instance Mesh Reconstruction

Abstract: Point scene instance mesh reconstruction is a challenging task since it requires both scene-level instance segmentation and instance-level mesh reconstruction from partial observations ...
IEEE

Optimizing Access Point Placement in Industrial IoT: A Deep Reinforcement Learning Approach With Q-Learning Verification and Signal Heatmap Visualization

Abstract: Optimal access point (AP) placement inside an industrial layout is important to ensure excellent connectivity. However, wireless fidelity (Wi-Fi) AP placement is complicated because ...
IEEE

Learning Implicit Fields for Point Cloud Filtering

Abstract: Since point clouds acquired by scanners inevitably contain noise, recovering a clean version from a noisy point cloud is essential for further 3D geometry processing applications. Several ...
IEEE

Weakly Supervised Learning for Point Cloud Semantic Segmentation With Dual Teacher

Abstract: Point cloud semantic segmentation has achieved considerable progress in the past decade. To alleviate expensive data annotation efforts, weakly supervised learning methods are preferable, ...
IEEE

Hybrid-Fusion Mamba for Multitask Point Cloud Learning With Visual Perception Sensors

Abstract: Mobile robots and autonomous vehicles rely on 3-D point cloud technology for environmental perception, which often employ various visual perception sensors within their Internet of Things ...
IEEE

Deep Learning Analysis of UAV Lidar Point Cloud for Individual Tree Detecting

Abstract: In this paper, we address the important task of Individual Tree Detection (ITD) in forest environments for enabling tree parameter estimation including tree count, height, volume, and crown ...
IEEE

Learning Robust Point Representation for 3D Non-Rigid Shape Retrieval

Abstract: Content-based 3D object retrieval is a challenging problem in computer vision and graphics, especially for non-rigid 3D shapes. This article proposes a multiview-based robust point ...
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 ...