no code implementations • 11 Feb 2024 • Jiahao Pang, Kevin Bui, Dong Tian
The universality of the point cloud format enables many 3D applications, making the compression of point clouds a critical phase in practice.
no code implementations • 29 Aug 2023 • Eric Lei, Muhammad Asad Lodhi, Jiahao Pang, Junghyun Ahn, Dong Tian
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.
1 code implementation • 9 Sep 2022 • Jiahao Pang, Muhammad Asad Lodhi, Dong Tian
Specifically, a point-based network is applied to convert the erratic local details to latent features residing on the coarse point cloud.
no code implementations • 9 Nov 2021 • Xue Zhang, Gene Cheung, Jiahao Pang, Yash Sanghvi, Abhiram Gnanasambandam, Stanley H. Chan
Specifically, we model depth formation as a combined process of signal-dependent noise addition and non-uniform log-based quantization.
1 code implementation • CVPR 2021 • HaiYan Wang, Jiahao Pang, Muhammad A. Lodhi, YingLi Tian, Dong Tian
Scene flow depicts the dynamics of a 3D scene, which is critical for various applications such as autonomous driving, robot navigation, AR/VR, etc.
no code implementations • 5 Aug 2020 • Wei Hu, Jiahao Pang, Xian-Ming Liu, Dong Tian, Chia-Wen Lin, Anthony Vetro
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.
1 code implementation • ICCV 2019 • Di Qiu, Jiahao Pang, Wenxiu Sun, Chengxi Yang
Recently, it is increasingly popular to equip mobile RGB cameras with Time-of-Flight (ToF) sensors for active depth sensing.
1 code implementation • 26 Sep 2018 • Ruichao Xiao, Wenxiu Sun, Jiahao Pang, Qiong Yan, Jimmy Ren
Our method is evaluated on both real-istic and synthetic stereo image pairs, and produces supe-rior results compared to the calibrated rectification or otherself-rectification approaches
1 code implementation • 31 Jul 2018 • Jin Zeng, Jiahao Pang, Wenxiu Sun, Gene Cheung
In this work, we combine the robustness merit of model-based approaches and the learning power of data-driven approaches for real image denoising.
no code implementations • 20 Mar 2018 • Jin Zeng, Gene Cheung, Michael Ng, Jiahao Pang, Cheng Yang
Due to discrete observations of the patches on the manifold, we approximate the manifold dimension computation defined in the continuous domain with a patch-based graph Laplacian regularizer and propose a new discrete patch distance measure to quantify the similarity between two same-sized surface patches for graph construction that is robust to noise.
1 code implementation • CVPR 2018 • Jiahao Pang, Wenxiu Sun, Chengxi Yang, Jimmy Ren, Ruichao Xiao, Jin Zeng, Liang Lin
By feeding real stereo pairs of different domains to stereo models pre-trained with synthetic data, we see that: i) a pre-trained model does not generalize well to the new domain, producing artifacts at boundaries and ill-posed regions; however, ii) feeding an up-sampled stereo pair leads to a disparity map with extra details.
1 code implementation • CVPR 2018 • Yue Luo, Jimmy Ren, Mude Lin, Jiahao Pang, Wenxiu Sun, Hongsheng Li, Liang Lin
The resulting model outperforms all the previous monocular depth estimation methods as well as the stereo block matching method in the challenging KITTI dataset by only using a small number of real training data.
Ranked #43 on Monocular Depth Estimation on KITTI Eigen split
1 code implementation • CVPR 2018 • Yue Luo, Jimmy Ren, Zhouxia Wang, Wenxiu Sun, Jinshan Pan, Jianbo Liu, Jiahao Pang, Liang Lin
Such suboptimal results are mainly attributed to the inability of imposing sequential geometric consistency, handling severe image quality degradation (e. g. motion blur and occlusion) as well as the inability of capturing the temporal correlation among video frames.
Ranked #3 on Pose Estimation on J-HMDB
1 code implementation • 30 Aug 2017 • Jiahao Pang, Wenxiu Sun, Jimmy SJ. Ren, Chengxi Yang, Qiong Yan
As opposed to directly learning the disparity at the second stage, we show that residual learning provides more effective refinement.
no code implementations • 30 May 2017 • Jimmy Ren, ZHIYANG YU, Jianbo Liu, Rui Zhang, Wenxiu Sun, Jiahao Pang, Xiaohao Chen, Qiong Yan
Recent advances in visual tracking showed that deep Convolutional Neural Networks (CNN) trained for image classification can be strong feature extractors for discriminative trackers.
2 code implementations • CVPR 2017 • Jimmy Ren, Xiaohao Chen, Jianbo Liu, Wenxiu Sun, Jiahao Pang, Qiong Yan, Yu-Wing Tai, Li Xu
In this paper, we proposed a novel single stage end-to-end trainable object detection network to overcome this limitation.
no code implementations • 27 Apr 2016 • Jiahao Pang, Gene Cheung
Focusing on image denoising, we derive an optimal metric space assuming non-local self-similarity of pixel patches, leading to an optimal graph Laplacian regularizer for denoising in the discrete domain.