189 citations · 434 across the 14 of their papers we have counts for
14 papers
WrappingNet: Mesh Autoencoder via Deep Sphere Deformation
Eric Lei, Muhammad Asad Lodhi, Jiahao Pang +2
There have been recent efforts to learn more meaningful representations via fixed length codewords from mesh data, since a mesh serves as a complete model of underlying 3D shape co…
Concavity-Induced Distance for Unoriented Point Cloud Decomposition
Ruoyu Wang, Yanfei Xue, Bharath Surianarayanan +2
We propose Concavity-induced Distance (CID) as a novel way to measure the dissimilarity between a pair of points in an unoriented point cloud. CID indicates the likelihood of two p…
GRASP-Net: Geometric Residual Analysis and Synthesis for Point Cloud Compression
Jiahao Pang, Muhammad Asad Lodhi, Dong Tian
Point cloud compression (PCC) is a key enabler for various 3-D applications, owing to the universality of the point cloud format. Ideally, 3D point clouds endeavor to depict object…
FESTA: Flow Estimation via Spatial-Temporal Attention for Scene Point Clouds
Haiyan Wang, Jiahao Pang, Muhammad A. Lodhi +2
Scene flow depicts the dynamics of a 3D scene, which is critical for various applications such as autonomous driving, robot navigation, AR/VR, etc. Conventionally, scene flow is es…
Graph Signal Processing for Geometric Data and Beyond: Theory and Applications
Wei Hu, Jiahao Pang, Xianming Liu +3
Geometric data acquired from real-world scenes, e.g., 2D depth images, 3D point clouds, and 4D dynamic point clouds, have found a wide range of applications including immersive tel…
TearingNet: Point Cloud Autoencoder to Learn Topology-Friendly Representations
Jiahao Pang, Duanshun Li, Dong Tian
Topology matters. Despite the recent success of point cloud processing with geometric deep learning, it remains arduous to capture the complex topologies of point cloud data with a…
3D Point Cloud Enhancement using Graph-Modelled Multiview Depth Measurements
Xue Zhang, Gene Cheung, Jiahao Pang +1
A 3D point cloud is often synthesized from depth measurements collected by sensors at different viewpoints. The acquired measurements are typically both coarse in precision and cor…
Deep Unsupervised Learning of 3D Point Clouds via Graph Topology Inference and Filtering
Siheng Chen, Chaojing Duan, Yaoqing Yang +3
We propose a deep autoencoder with graph topology inference and filtering to achieve compact representations of unorganized 3D point clouds in an unsupervised manner. Many previous…
FoldingNet: Point Cloud Auto-encoder via Deep Grid Deformation
Yaoqing Yang, Chen Feng, Yiru Shen +1
Recent deep networks that directly handle points in a point set, e.g., PointNet, have been state-of-the-art for supervised learning tasks on point clouds such as classification and…
Mining Point Cloud Local Structures by Kernel Correlation and Graph Pooling
Yiru Shen, Chen Feng, Yaoqing Yang +1
Unlike on images, semantic learning on 3D point clouds using a deep network is challenging due to the naturally unordered data structure. Among existing works, PointNet has achieve…
Fast Resampling of 3D Point Clouds via Graphs
Siheng Chen, Dong Tian, Chen Feng +2
To reduce cost in storing, processing and visualizing a large-scale point cloud, we consider a randomized resampling strategy to select a representative subset of points while pres…
Guided Signal Reconstruction Theory
Andrew Knyazev, Akshay Gadde, Hassan Mansour +1
An axiomatic approach to signal reconstruction is formulated, involving a sample consistent set and a guiding set, describing desired reconstructions. New frame-less reconstruction…
Guided Signal Reconstruction with Application to Image Magnification
Akshay Gadde, Andrew Knyazev, Dong Tian +1
We study the problem of reconstructing a signal from its projection on a subspace. The proposed signal reconstruction algorithms utilize a guiding subspace that represents desired…
Chebyshev and Conjugate Gradient Filters for Graph Image Denoising
Dong Tian, Hassan Mansour, Andrew Knyazev +1
In 3D image/video acquisition, different views are often captured with varying noise levels across the views. In this paper, we propose a graph-based image enhancement technique th…