Search Results for author: Zhiwen Fan

Found 12 papers, 1 papers with code

Unified Implicit Neural Stylization

no code implementations5 Apr 2022 Zhiwen Fan, Yifan Jiang, Peihao Wang, Xinyu Gong, Dejia Xu, Zhangyang Wang

Representing visual signals by implicit representation (e. g., a coordinate based deep network) has prevailed among many vision tasks.

Neural Stylization Novel View Synthesis

SinNeRF: Training Neural Radiance Fields on Complex Scenes from a Single Image

no code implementations2 Apr 2022 Dejia Xu, Yifan Jiang, Peihao Wang, Zhiwen Fan, Humphrey Shi, Zhangyang Wang

Despite the rapid development of Neural Radiance Field (NeRF), the necessity of dense covers largely prohibits its wider applications.

Novel View Synthesis

Reasoning With Hierarchical Symbols: Reclaiming Symbolic Policies For Visual Reinforcement Learning

no code implementations29 Sep 2021 Wenqing Zheng, S P Sharan, Zhiwen Fan, Zhangyang Wang

Deep vision models are nowadays widely integrated into visual reinforcement learning (RL) to parameterize the policy networks.


FloorPlanCAD: A Large-Scale CAD Drawing Dataset for Panoptic Symbol Spotting

no code implementations ICCV 2021 Zhiwen Fan, Lingjie Zhu, Honghua Li, Xiaohao Chen, Siyu Zhu, Ping Tan

The proposed CNN-GCN method achieved state-of-the-art (SOTA) performance on the task of semantic symbol spotting, and help us build a baseline network for the panoptic symbol spotting task.

Vector Graphics

MeshMVS: Multi-View Stereo Guided Mesh Reconstruction

no code implementations17 Oct 2020 Rakesh Shrestha, Zhiwen Fan, Qingkun Su, Zuozhuo Dai, Siyu Zhu, Ping Tan

Deep learning based 3D shape generation methods generally utilize latent features extracted from color images to encode the semantics of objects and guide the shape generation process.

3D Shape Generation

Cascade Cost Volume for High-Resolution Multi-View Stereo and Stereo Matching

4 code implementations CVPR 2020 Xiaodong Gu, Zhiwen Fan, Zuozhuo Dai, Siyu Zhu, Feitong Tan, Ping Tan

The deep multi-view stereo (MVS) and stereo matching approaches generally construct 3D cost volumes to regularize and regress the output depth or disparity.

3D Reconstruction Point Clouds +1

Residual-Guide Feature Fusion Network for Single Image Deraining

no code implementations20 Apr 2018 Zhiwen Fan, Huafeng Wu, Xueyang Fu, Yue Hunag, Xinghao Ding

Single image rain streaks removal is extremely important since rainy images adversely affect many computer vision systems.

Single Image Deraining

A Deep Information Sharing Network for Multi-contrast Compressed Sensing MRI Reconstruction

no code implementations10 Apr 2018 Liyan Sun, Zhiwen Fan, Yue Huang, Xinghao Ding, John Paisley

In multi-contrast magnetic resonance imaging (MRI), compressed sensing theory can accelerate imaging by sampling fewer measurements within each contrast.

MRI Reconstruction

A Divide-and-Conquer Approach to Compressed Sensing MRI

no code implementations27 Mar 2018 Liyan Sun, Zhiwen Fan, Xinghao Ding, Congbo Cai, Yue Huang, John Paisley

Compressed sensing (CS) theory assures us that we can accurately reconstruct magnetic resonance images using fewer k-space measurements than the Nyquist sampling rate requires.

A Deep Error Correction Network for Compressed Sensing MRI

no code implementations23 Mar 2018 Liyan Sun, Zhiwen Fan, Yue Huang, Xinghao Ding, John Paisley

Existing CS-MRI algorithms can serve as the template module for guiding the reconstruction.

MRI Reconstruction

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