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

Learning Digital Terrain Models From Point Clouds: ALS2DTM Dataset and Rasterization-Based GAN

Abstract: Despite the popularity of deep neural networks in various domains, the extraction of digital terrain models (DTMs) from airborne laser scanning (ALS) point clouds is still challenging. This ...
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

End-to-End Learning to Grasp via Sampling From Object Point Clouds

Abstract: The ability to grasp objects is an essential skill that enables many robotic manipulation tasks. Recent works have studied point cloud-based methods for object grasping by starting from ...
IEEE

Forestry Crane Automation using Learning-based Visual Grasping Point Prediction

Abstract: This paper presents an approach to automate the log-grasping of a forestry crane. A common hydraulic actuated log-crane is converted into a robotic device by retrofitting it with various ...