193 citations · 298 across the 15 of their papers we have counts for
15 papers
GeodesicPSIM: Predicting the Quality of Static Mesh with Texture Map via Geodesic Patch Similarity
Qi Yang, Joel Jung, Xiaozhong Xu +1
Static meshes with texture maps have attracted considerable attention in both industrial manufacturing and academic research, leading to an urgent requirement for effective and rob…
Once-Training-All-Fine: No-Reference Point Cloud Quality Assessment via Domain-relevance Degradation Description
Yipeng Liu, Qi Yang, Yujie Zhang +4
The visual quality of point clouds plays a crucial role in the development and broadcasting of immersive media. Therefore, investigating point cloud quality assessment (PCQA) is in…
A Tiny Machine Learning Model for Point Cloud Object Classification
Min Zhang, Jintang Xue, Pranav Kadam +3
The design of a tiny machine learning model, which can be deployed in mobile and edge devices, for point cloud object classification is investigated in this work. To achieve this o…
PointFlowHop: Green and Interpretable Scene Flow Estimation from Consecutive Point Clouds
Pranav Kadam, Jiahao Gu, Shan Liu +1
An efficient 3D scene flow estimation method called PointFlowHop is proposed in this work. PointFlowHop takes two consecutive point clouds and determines the 3D flow vectors for ev…
S3I-PointHop: SO(3)-Invariant PointHop for 3D Point Cloud Classification
Pranav Kadam, Hardik Prajapati, Min Zhang +3
Many point cloud classification methods are developed under the assumption that all point clouds in the dataset are well aligned with the canonical axes so that the 3D Cartesian po…
gpcgc: a green point cloud geometry coding method
Qingyang Zhou, Shan Liu, C. -C. Jay Kuo
A low-complexity point cloud compression method called the Green Point Cloud Geometry Codec (GPCGC), is proposed to encode the 3D spatial coordinates of static point clouds efficie…
GPA-Net:No-Reference Point Cloud Quality Assessment with Multi-task Graph Convolutional Network
Ziyu Shan, Qi Yang, Rui Ye +4
With the rapid development of 3D vision, point cloud has become an increasingly popular 3D visual media content. Due to the irregular structure, point cloud has posed novel challen…
Point Cloud Quality Assessment using 3D Saliency Maps
Zhengyu Wang, Yujie Zhang, Qi Yang +3
Point cloud quality assessment (PCQA) has become an appealing research field in recent days. Considering the importance of saliency detection in quality assessment, we propose an e…
Learning Knowledge Representation with Meta Knowledge Distillation for Single Image Super-Resolution
Han Zhu, Zhenzhong Chen, Shan Liu
Knowledge distillation (KD), which can efficiently transfer knowledge from a cumbersome network (teacher) to a compact network (student), has demonstrated its advantages in some co…
Neural Texture Extraction and Distribution for Controllable Person Image Synthesis
Yurui Ren, Xiaoqing Fan, Ge Li +2
We deal with the controllable person image synthesis task which aims to re-render a human from a reference image with explicit control over body pose and appearance. Observing that…
PCRP: Unsupervised Point Cloud Object Retrieval and Pose Estimation
Pranav Kadam, Qingyang Zhou, Shan Liu +1
An unsupervised point cloud object retrieval and pose estimation method, called PCRP, is proposed in this work. It is assumed that there exists a gallery point cloud set that conta…
OctAttention: Octree-Based Large-Scale Contexts Model for Point Cloud Compression
Chunyang Fu, Ge Li, Rui Song +2
In point cloud compression, sufficient contexts are significant for modeling the point cloud distribution. However, the contexts gathered by the previous voxel-based methods decrea…
GreenPCO: An Unsupervised Lightweight Point Cloud Odometry Method
Pranav Kadam, Min Zhang, Jiahao Gu +2
Visual odometry aims to track the incremental motion of an object using the information captured by visual sensors. In this work, we study the point cloud odometry problem, where o…
GSIP: Green Semantic Segmentation of Large-Scale Indoor Point Clouds
Min Zhang, Pranav Kadam, Shan Liu +1
An efficient solution to semantic segmentation of large-scale indoor scene point clouds is proposed in this work. It is named GSIP (Green Segmentation of Indoor Point clouds) and i…
R-PointHop: A Green, Accurate, and Unsupervised Point Cloud Registration Method
Pranav Kadam, Min Zhang, Shan Liu +1
Inspired by the recent PointHop classification method, an unsupervised 3D point cloud registration method, called R-PointHop, is proposed in this work. R-PointHop first determines…