LAM3D: Large Image-Point-Cloud Alignment Model for 3D Reconstruction from Single Image

no code implementations • 24 May 2024 • Ruikai Cui, Xibin Song, Weixuan Sun, Senbo Wang, Weizhe Liu, Shenzhou Chen, Taizhang Shang, Yang Li, Nick Barnes, Hongdong Li, Pan Ji

Large Reconstruction Models have made significant strides in the realm of automated 3D content generation from single or multiple input images.

NeuSDFusion: A Spatial-Aware Generative Model for 3D Shape Completion, Reconstruction, and Generation

no code implementations • 27 Mar 2024 • Ruikai Cui, Weizhe Liu, Weixuan Sun, Senbo Wang, Taizhang Shang, Yang Li, Xibin Song, Han Yan, Zhennan Wu, Shenzhou Chen, Hongdong Li, Pan Ji

3D shape generation aims to produce innovative 3D content adhering to specific conditions and constraints.

3D Shape Generation 3D Shape Modeling

Frankenstein: Generating Semantic-Compositional 3D Scenes in One Tri-Plane

no code implementations • 24 Mar 2024 • Han Yan, Yang Li, Zhennan Wu, Shenzhou Chen, Weixuan Sun, Taizhang Shang, Weizhe Liu, Tian Chen, Xiaqiang Dai, Chao Ma, Hongdong Li, Pan Ji

We present Frankenstein, a diffusion-based framework that can generate semantic-compositional 3D scenes in a single pass.

Denoising

BlockFusion: Expandable 3D Scene Generation using Latent Tri-plane Extrapolation

1 code implementation • 30 Jan 2024 • Zhennan Wu, Yang Li, Han Yan, Taizhang Shang, Weixuan Sun, Senbo Wang, Ruikai Cui, Weizhe Liu, Hiroyuki Sato, Hongdong Li, Pan Ji

A variational auto-encoder is employed to compress the tri-planes into the latent tri-plane space, on which the denoising diffusion process is performed.

Denoising Scene Generation

Perceptual Extreme Super Resolution Network with Receptive Field Block

1 code implementation • 26 May 2020 • Taizhang Shang, Qiuju Dai, Shengchen Zhu, Tong Yang, Yandong Guo

Third, we alternately use different upsampling methods in the upsampling stage to reduce the high computation complexity and still remain satisfactory performance.

Image Super-Resolution object-detection +1

NTIRE 2020 Challenge on Perceptual Extreme Super-Resolution: Methods and Results

no code implementations • 3 May 2020 • Kai Zhang, Shuhang Gu, Radu Timofte, Taizhang Shang, Qiuju Dai, Shengchen Zhu, Tong Yang, Yandong Guo, Younghyun Jo, Sejong Yang, Seon Joo Kim, Lin Zha, Jiande Jiang, Xinbo Gao, Wen Lu, Jing Liu, Kwangjin Yoon, Taegyun Jeon, Kazutoshi Akita, Takeru Ooba, Norimichi Ukita, Zhipeng Luo, Yuehan Yao, Zhenyu Xu, Dongliang He, Wenhao Wu, Yukang Ding, Chao Li, Fu Li, Shilei Wen, Jianwei Li, Fuzhi Yang, Huan Yang, Jianlong Fu, Byung-Hoon Kim, JaeHyun Baek, Jong Chul Ye, Yuchen Fan, Thomas S. Huang, Junyeop Lee, Bokyeung Lee, Jungki Min, Gwantae Kim, Kanghyu Lee, Jaihyun Park, Mykola Mykhailych, Haoyu Zhong, Yukai Shi, Xiaojun Yang, Zhijing Yang, Liang Lin, Tongtong Zhao, Jinjia Peng, Huibing Wang, Zhi Jin, Jiahao Wu, Yifu Chen, Chenming Shang, Huanrong Zhang, Jeongki Min, Hrishikesh P. S, Densen Puthussery, Jiji C. V

This paper reviews the NTIRE 2020 challenge on perceptual extreme super-resolution with focus on proposed solutions and results.

Image Super-Resolution