1 code implementation • 24 Sep 2023 • Cho-Ying Wu, Quankai Gao, Chin-Cheng Hsu, Te-Lin Wu, Jing-Wen Chen, Ulrich Neumann
To facilitate our investigation for robustness and address limitations of previous works, we collect InSpaceType, a high-quality and high-resolution RGBD dataset for general indoor environments.
Indoor Monocular Depth Estimation Monocular Depth Estimation
no code implementations • 12 May 2023 • Cho-Ying Wu, Yiqi Zhong, Junying Wang, Ulrich Neumann
We instead propose fine-grained task that treats each RGB-D pair as a task in our meta-optimization.
1 code implementation • CVPR 2022 • Cho-Ying Wu, Chin-Cheng Hsu, Ulrich Neumann
This work digs into a root question in human perception: can face geometry be gleaned from one's voices?
Ranked #1 on 3D Face Modelling on Voxceleb-3D
1 code implementation • CVPR 2022 • Cho-Ying Wu, Jialiang Wang, Michael Hall, Ulrich Neumann, Shuochen Su
The majority of prior monocular depth estimation methods without groundtruth depth guidance focus on driving scenarios.
Ranked #1 on Monocular Depth Estimation on VA (Virtual Apartment)
4 code implementations • 19 Oct 2021 • Cho-Ying Wu, Qiangeng Xu, Ulrich Neumann
Our synergy process leverages a representation cycle for 3DMM parameters and 3D landmarks.
Ranked #1 on Face Alignment on AFLW
1 code implementation • 21 Apr 2021 • Cho-Ying Wu, Ke Xu, Chin-Cheng Hsu, Ulrich Neumann
This work focuses on the analysis that whether 3D face models can be learned from only the speech inputs of speakers.
1 code implementation • 16 Apr 2021 • Cho-Ying Wu, Qiangeng Xu, Ulrich Neumann
This work focuses on complete 3D facial geometry prediction, including 3D facial alignment via 3D face modeling and face orientation estimation using the proposed multi-task, multi-modal, and multi-representation landmark refinement network (M$^3$-LRN).
1 code implementation • 14 Jun 2020 • Cho-Ying Wu, Xiaoyan Hu, Michael Happold, Qiangeng Xu, Ulrich Neumann
Mask regression is based on 2D, 2. 5D, and 3D ROI using the pseudo-lidar and image-based representations.
Ranked #16 on Instance Segmentation on Cityscapes val (using extra training data)
1 code implementation • 15 Mar 2020 • Cho-Ying Wu, Ulrich Neumann
Recent sparse depth completion for lidars only focuses on the lower scenes and produces irregular estimations on the upper because existing datasets, such as KITTI, do not provide groundtruth for upper areas.
1 code implementation • CVPR 2020 • Qiangeng Xu, Xudong Sun, Cho-Ying Wu, Panqu Wang, Ulrich Neumann
Compared with popular sampling methods such as Farthest Point Sampling (FPS) and Ball Query, CAGQ achieves up to 50X speed-up.
1 code implementation • NeurIPS 2019 • Yiqi Zhong, Cho-Ying Wu, Suya You, Ulrich Neumann
Such a transformation enables CFCNet to predict features and reconstruct data of missing depth measurements according to their corresponding, transformed RGB features.
no code implementations • 6 Jun 2019 • Cho-Ying Wu, Jian-Jiun Ding
We first propose a novel nonconvex rank surrogate on the general rank minimization problem and apply this to the corrupted image completion problem.
1 code implementation • 6 Jun 2019 • Cho-Ying Wu, Ulrich Neumann
Also, we propose to create building masks from semantic segmentation using an encoder-decoder network.
1 code implementation • 6 Jun 2019 • Cho-Ying Wu, Jian-Jiun Ding
In this paper, a very effective method to solve the contiguous face occlusion recognition problem is proposed.