IEEE

A 28nm 1.644TFLOPS/W Floating-Point Computation SRAM Macro with Variable Precision for Deep Neural Network Inference and Training

Abstract: This paper presents a digital compute-in-memory (CIM) macro for accelerating deep neural networks. The macro provides high-precision computation required for training deep neural networks ...
IEEE

BICANet: LiDAR Point Cloud Classification Network Based on Coordinate Attention and Blueprint Separation Involution Neural Network

Abstract: With the advent of the era of Industry 4.0 and the continuous development of point cloud data acquisition technology, point cloud data have been widely used in the unmanned distribution of ...
IEEE

Mask-SL RCNN: Feature-Enhanced 3D Object Detection Network for Point Clouds

Abstract: At present, with the original point cloud as input, most of the object detectors use Pointnet++ to extract features of the point cloud based on the Farthest Point Sampling (FPS). However, ...
IEEE

PointCT: Point Central Transformer Network for Weakly-supervised Point Cloud Semantic Segmentation

Abstract: Although point cloud segmentation has a principal role in 3D understanding, annotating fully large-scale scenes for this task can be costly and time-consuming. To resolve this issue, we ...
IEEE

RID-Net: A Hybrid MLP-Transformer Network for Robust Point Cloud Registration

Abstract: The robustness of correspondence-based point cloud registration relies on transformation invariance and intrinsic distinctiveness of the descriptors computed for registration. However, for ...
IEEE

DF-Net: Dynamic and Folding Network for 3D Point Cloud Completion

Abstract: The development of 3D sensors encourages researchers to process point cloud data directly. Point cloud data requires less memory but conveys more detailed 3D shape information. However, ...
IEEE

PCGNet: Point Cloud Generation Network for 3D Perception Using Monocular Images and Radar

Abstract: Millimeter-wave (mmWave) radar is widely used in autonomous driving thanks to its robustness under harsh weather conditions. However, compared to LiDAR point clouds, mmWave radar point ...
IEEE

MFE-Net: A Multi-Layer Feature Extraction Network for No-Reference Quality Assessment of 3-D Point Clouds

Abstract: As an important data form, the point cloud (PC) can present real-world objects in a 3D and realistic way. In order to measure the performance of acquisition, processing, and compression ...
IEEE

Extracting 3-D Structural Lines of Building From ALS Point Clouds Using Graph Neural Network Embedded With Corner Information

Abstract: The representation quantifies the geometric shape and topology of a building is a necessary procedure for many urban planning applications. A sharp line framework is a high-level structural ...
IEEE

CenFormer: Transformer-Based Network From Centroid Generation for Point Cloud Completion

Abstract: Point clouds captured from 3-D scanners are often sparse and incomplete due to occlusions, limited viewpoints, and sensor constraints. These limitations hinder applications in robotics, ...
IEEE

The Emerging of Named Data Networking: Architecture, Application, and Technology

Abstract: Named Data Networking (NDN) is developed to accommodate future-oriented internet traffic. In recent years, NDN’s popularity has grown due to the evolution of the Internet of Things, ...
IEEE

No-Reference 3D Point Cloud Quality Assessment Using Multi-View Projection and Deep Convolutional Neural Network

Abstract: Digital representation of 3D content in the form of 3D point clouds (PC) has gained increasing interest and has emerged in various computer vision applications. However, various degradation ...