401 citations · 880 across the 29 of their papers we have counts for
30 papers
Inter-LPCM: Learning-based Inter-Frame Predictive Coding for LiDAR Point Cloud Compression
Chang Sun, Hui Yuan, Shiqi Jiang +3
Because LiDAR sensors acquire point clouds with a fixed angular resolution, the resulting data can be systematically parameterized and efficiently compressed in the spherical coord…
DUGAE: Unified Geometry and Attribute Enhancement via Spatiotemporal Correlations for G-PCC Compressed Dynamic Point Clouds
Pan Zhao, Hui Yuan, Chang Sun +3
Existing post-decoding quality enhancement methods for point clouds are designed for static data and typically process each frame independently. As a result, they cannot effectivel…
Blind Quality Enhancement for G-PCC Compressed Dynamic Point Clouds
Tian Guo, Hui Yuan, Chang Sun +3
Point cloud compression often introduces noticeable reconstruction artifacts, which makes quality enhancement necessary. Existing approaches typically assume prior knowledge of the…
Fast Attention-Based Simplification of LiDAR Point Clouds for Object Detection and Classification
Z. Rozsa, Á. Madaras, Q. Wei +5
LiDAR point clouds are widely used in autonomous driving and consist of large numbers of 3D points captured at high frequency to represent surrounding objects such as vehicles, ped…
Point Cloud Feature Coding for Object Detection over an Error-Prone Cloud-Edge Collaborative System
Chongzhen Tian, Hui Yuan, Pan Zhao +3
Cloud-edge collaboration enhances machine perception by combining the strengths of edge and cloud computing. Edge devices capture raw data (e.g., 3D point clouds) and extract salie…
A Three-Level Alignment Framework for Large-Scale 3D Retrieval and Controlled 4D Generation
Philip Xu
We introduce Uni4D, a unified framework for large scale open vocabulary 3D retrieval and controlled 4D generation based on structured three level alignment across text, 3D models,…
UGAE: Unified Geometry and Attribute Enhancement for G-PCC Compressed Point Clouds
Pan Zhao, Hui Yuan, Chongzhen Tian +3
Lossy compression of point clouds reduces storage and transmission costs; however, it inevitably leads to irreversible distortion in geometry structure and attribute information. T…
PC-JND: Subjective Study and Dataset on Just Noticeable Difference for Point Clouds in 6DoF Virtual Reality
Chunling Fan, Yun Zhang, Dietmar Saupe +2
The Just Noticeable Difference (JND) accounts for the minimum distortion at which humans can perceive a difference between a pristine stimulus and its distorted version. The JND co…
STQE: Spatial-Temporal Attribute Quality Enhancement for G-PCC Compressed Dynamic Point Clouds
Tian Guo, Hui Yuan, Xiaolong Mao +3
Very few studies have addressed quality enhancement for compressed dynamic point clouds. In particular, the effective exploitation of spatial-temporal correlations between point cl…
LPCM: Learning-based Predictive Coding for LiDAR Point Cloud Compression
Chang Sun, Hui Yuan, Shiqi Jiang +3
Since the data volume of LiDAR point clouds is very huge, efficient compression is necessary to reduce their storage and transmission costs. However, existing learning-based compre…
EdgeRegNet: Edge Feature-based Multimodal Registration Network between Images and LiDAR Point Clouds
Yuanchao Yue, Hui Yuan, Qinglong Miao +3
Cross-modal data registration has long been a critical task in computer vision, with extensive applications in autonomous driving and robotics. Accurate and robust registration met…
PCE-GAN: A Generative Adversarial Network for Point Cloud Attribute Quality Enhancement based on Optimal Transport
Tian Guo, Hui Yuan, Qi Liu +3
Point cloud compression significantly reduces data volume but sacrifices reconstruction quality, highlighting the need for advanced quality enhancement techniques. Most existing ap…
Interleaved Block-based Learned Image Compression with Feature Enhancement and Quantization Error Compensation
Shiqi Jiang, Hui Yuan, Shuai Li +3
In recent years, learned image compression (LIC) methods have achieved significant performance improvements. However, obtaining a more compact latent representation and reducing th…
CS-Net:Contribution-based Sampling Network for Point Cloud Simplification
Tian Guo, Chen Chen, Hui Yuan +3
Point cloud sampling plays a crucial role in reducing computation costs and storage requirements for various vision tasks. Traditional sampling methods, such as farthest point samp…
SPAC: Sampling-based Progressive Attribute Compression for Dense Point Clouds
Xiaolong Mao, Hui Yuan, Tian Guo +3
We propose an end-to-end attribute compression method for dense point clouds. The proposed method combines a frequency sampling module, an adaptive scale feature extraction module…
Feature Compression for Cloud-Edge Multimodal 3D Object Detection
Chongzhen Tian, Zhengxin Li, Hui Yuan +3
Machine vision systems, which can efficiently manage extensive visual perception tasks, are becoming increasingly popular in industrial production and daily life. Due to the challe…
3DAttGAN: A 3D Attention-based Generative Adversarial Network for Joint Space-Time Video Super-Resolution
Congrui Fu, Hui Yuan, Liquan Shen +2
In many applications, including surveillance, entertainment, and restoration, there is a need to increase both the spatial resolution and the frame rate of a video sequence. The ai…
PCAC-GAN: A Sparse-Tensor-Based Generative Adversarial Network for 3D Point Cloud Attribute Compression
Xiaolong Mao, Hui Yuan, Xin Lu +2
Learning-based methods have proven successful in compressing geometric information for point clouds. For attribute compression, however, they still lag behind non-learning-based me…
Enhancing octree-based context models for point cloud geometry compression with attention-based child node number prediction
Chang Sun, Hui Yuan, Xiaolong Mao +2
In point cloud geometry compression, most octreebased context models use the cross-entropy between the onehot encoding of node occupancy and the probability distribution predicted…
Enhancing context models for point cloud geometry compression with context feature residuals and multi-loss
Chang Sun, Hui Yuan, Shuai Li +2
In point cloud geometry compression, context models usually use the one-hot encoding of node occupancy as the label, and the cross-entropy between the one-hot encoding and the prob…
Global Spatial-Temporal Information-based Residual ConvLSTM for Video Space-Time Super-Resolution
Congrui Fu, Hui Yuan, Shiqi Jiang +3
By converting low-frame-rate, low-resolution videos into high-frame-rate, high-resolution ones, space-time video super-resolution techniques can enhance visual experiences and faci…
Relaxed forced choice improves performance of visual quality assessment methods
Mohsen Jenadeleh, Johannes Zagermann, Harald Reiterer +3
In image quality assessment, a collective visual quality score for an image or video is obtained from the individual ratings of many subjects. One commonly used format for these ex…
GQE-Net: A Graph-based Quality Enhancement Network for Point Cloud Color Attribute
Jinrui Xing, Hui Yuan, Raouf Hamzaoui +2
In recent years, point clouds have become increasingly popular for representing three-dimensional (3D) visual objects and scenes. To efficiently store and transmit point clouds, co…
Progressive Knowledge Transfer Based on Human Visual Perception Mechanism for Perceptual Quality Assessment of Point Clouds
Qi Liu, Yiyun Liu, Honglei Su +2
With the wide applications of colored point cloud in many fields, point cloud perceptual quality assessment plays a vital role in the visual communication systems owing to the exis…