271 citations · 499 across the 17 of their papers we have counts for
18 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…
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…
Graph-Based Depth Denoising & Dequantization for Point Cloud Enhancement
Xue Zhang, Gene Cheung, Jiahao Pang +3
A 3D point cloud is typically constructed from depth measurements acquired by sensors at one or more viewpoints. The measurements suffer from both quantization and noise corruption…
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 End-to-End Alignment and Refinement for Time-of-Flight RGB-D Module
Di Qiu, Jiahao Pang, Wenxiu Sun +1
Recently, it is increasingly popular to equip mobile RGB cameras with Time-of-Flight (ToF) sensors for active depth sensing. However, for off-the-shelf ToF sensors, one must tackle…
DSR: Direct Self-rectification for Uncalibrated Dual-lens Cameras
Ruichao Xiao, Wenxiu Sun, Jiahao Pang +2
With the developments of dual-lens camera modules,depth information representing the third dimension of thecaptured scenes becomes available for smartphones. It isestimated by ster…
Deep Graph Laplacian Regularization for Robust Denoising of Real Images
Jin Zeng, Jiahao Pang, Wenxiu Sun +1
Recent developments in deep learning have revolutionized the paradigm of image restoration. However, its applications on real image denoising are still limited, due to its sensitiv…
Zoom and Learn: Generalizing Deep Stereo Matching to Novel Domains
Jiahao Pang, Wenxiu Sun, Chengxi Yang +4
Despite the recent success of stereo matching with convolutional neural networks (CNNs), it remains arduous to generalize a pre-trained deep stereo model to a novel domain. A major…
Single View Stereo Matching
Yue Luo, Jimmy Ren, Mude Lin +4
Previous monocular depth estimation methods take a single view and directly regress the expected results. Though recent advances are made by applying geometrically inspired loss fu…
3D Point Cloud Denoising using Graph Laplacian Regularization of a Low Dimensional Manifold Model
Jin Zeng, Gene Cheung, Michael Ng +2
3D point cloud - a new signal representation of volumetric objects - is a discrete collection of triples marking exterior object surface locations in 3D space. Conventional imperfe…
LSTM Pose Machines
Yue Luo, Jimmy Ren, Zhouxia Wang +5
We observed that recent state-of-the-art results on single image human pose estimation were achieved by multi-stage Convolution Neural Networks (CNN). Notwithstanding the superior…
Cascade Residual Learning: A Two-stage Convolutional Neural Network for Stereo Matching
Jiahao Pang, Wenxiu Sun, Jimmy SJ. Ren +2
Leveraging on the recent developments in convolutional neural networks (CNNs), matching dense correspondence from a stereo pair has been cast as a learning problem, with performanc…
Robust Tracking Using Region Proposal Networks
Jimmy Ren, Zhiyang Yu, Jianbo Liu +5
Recent advances in visual tracking showed that deep Convolutional Neural Networks (CNN) trained for image classification can be strong feature extractors for discriminative tracker…
Accurate Single Stage Detector Using Recurrent Rolling Convolution
Jimmy Ren, Xiaohao Chen, Jianbo Liu +5
Most of the recent successful methods in accurate object detection and localization used some variants of R-CNN style two stage Convolutional Neural Networks (CNN) where plausible…
Graph Laplacian Regularization for Image Denoising: Analysis in the Continuous Domain
Jiahao Pang, Gene Cheung
Inverse imaging problems are inherently under-determined, and hence it is important to employ appropriate image priors for regularization. One recent popular prior---the graph Lapl…