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

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 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 ...
IEEE

Contextual Sequence-to-Point Deep Learning for Household Energy Disaggregation

Abstract: This paper examines a contextual paradigm for energy disaggregation using Non-Intrusive Load Monitoring (NILM). Due to numerous issues including low sampling rates, missing data, misaligned ...
IEEE

Deep Scene Flow Learning: From 2D Images to 3D Point Clouds

Abstract: Scene flow describes the 3D motion in a scene. It can be modeled as a single task or as a composite of the auxiliary tasks of depth, camera motion, and optical flow estimation. Deep learning ...
IEEE

An Improved Sequence-to-Point Learning for Non-Intrusive Load Monitoring Based on Discrete Wavelet Transform

Abstract: Nonintrusive load monitoring (NILM) is a process that monitors the aggregated power consumption data of customers measured by a single sensor and decomposes the real-time power consumption ...
IEEE

Fixed Point Laplacian Mapping: A Geometrically Correct Manifold Learning Algorithm

Abstract: Dimensionality reduction (DR) and manifold learning (ManL) have been applied extensively in many machine learning tasks, including computer vision, image analysis and pattern recognition ...