Search Results for author:
Found 14 papers, 5 papers with code
PIVOT-Net: Heterogeneous Point-Voxel-Tree-based Framework for Point Cloud Compression
The universality of the point cloud format enables many 3D applications, making the compression of point clouds a critical phase in practice.
WrappingNet: Mesh Autoencoder via Deep Sphere Deformation
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 compared to a point cloud.
Concavity-Induced Distance for Unoriented Point Cloud Decomposition
We propose Concavity-induced Distance (CID) as a novel way to measure the dissimilarity between a pair of points in an unoriented point cloud.
GRASP-Net: Geometric Residual Analysis and Synthesis for Point Cloud Compression
Specifically, a point-based network is applied to convert the erratic local details to latent features residing on the coarse point cloud.
FESTA: Flow Estimation via Spatial-Temporal Attention for Scene Point Clouds
Scene flow depicts the dynamics of a 3D scene, which is critical for various applications such as autonomous driving, robot navigation, AR/VR, etc.
Graph Signal Processing for Geometric Data and Beyond: Theory and Applications
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 telepresence, autonomous driving, surveillance, etc.
Graph Based Skeleton Modeling for Human Activity Analysis
Understanding human activity based on sensor information is required in many applications and has been an active research area.
Ranked #4 on
Skeleton Based Action Recognition
on MSR Action3D
Deep Unsupervised Learning of 3D Point Clouds via Graph Topology Inference and Filtering
The experimental results show that (1) the proposed networks outperform the state-of-the-art methods in various tasks; (2) a graph topology can be inferred as auxiliary information without specific supervision on graph topology inference; and (3) graph filtering refines the reconstruction, leading to better performances.
FoldingNet: Point Cloud Auto-encoder via Deep Grid Deformation
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 segmentation.
Ranked #15 on
3D Point Cloud Linear Classification
on ModelNet40
3D Point Cloud Linear Classification
General Classification
+1
Mining Point Cloud Local Structures by Kernel Correlation and Graph Pooling
Unlike on images, semantic learning on 3D point clouds using a deep network is challenging due to the naturally unordered data structure.
Fast Resampling of 3D Point Clouds via Graphs
We use a general feature-extraction operator to represent application-dependent features and propose a general reconstruction error to evaluate the quality of resampling.
Guided Signal Reconstruction Theory
New frame-less reconstruction methods are proposed, based on a novel concept of a reconstruction set, defined as a shortest pathway between the sample consistent set and the guiding set.
Chebyshev and Conjugate Gradient Filters for Graph Image Denoising
In 3D image/video acquisition, different views are often captured with varying noise levels across the views.
