1 code implementation • 16 Apr 2024 • Siqiao Xue, Danrui Qi, Caigao Jiang, Wenhui Shi, Fangyin Cheng, Keting Chen, Zhiping Zhang, Jianshan He, Hongyang Zhang, Ganglin Wei, Wang Zhao, Fan Zhou, Hong Yi, Shaodong Liu, Hongjun Yang, Faqiang Chen
The recent breakthroughs in large language models (LLMs) are positioned to transition many areas of software.
1 code implementation • 4 Oct 2023 • Yuze He, Yushi Bai, Matthieu Lin, Wang Zhao, Yubin Hu, Jenny Sheng, Ran Yi, Juanzi Li, Yong-Jin Liu
Recent methods in text-to-3D leverage powerful pretrained diffusion models to optimize NeRF.
no code implementations • 30 Sep 2023 • Yuze He, Peng Wang, Yubin Hu, Wang Zhao, Ran Yi, Yong-Jin Liu, Wenping Wang
In this paper, we explore the potential of MPI and show that MPI can synthesize high-quality novel views of complex scenes with diverse camera distributions and view directions, which are not only limited to simple forward-facing scenes.
1 code implementation • 14 Sep 2023 • Sheng Ye, Yubin Hu, Matthieu Lin, Yu-Hui Wen, Wang Zhao, Yong-Jin Liu, Wenping Wang
To enhance the normal priors, we introduce a simple yet effective image sharpening and denoising technique, coupled with a network that estimates the pixel-wise uncertainty of the predicted surface normal vectors.
1 code implementation • 18 Aug 2023 • Yubin Hu, Sheng Ye, Wang Zhao, Matthieu Lin, Yuze He, Yu-Hui Wen, Ying He, Yong-Jin Liu
In this paper, we propose a novel framework, empowered by a 2D diffusion-based in-painting model, to reconstruct complete surfaces for the hidden parts of objects.
1 code implementation • 19 Jul 2022 • Wang Zhao, Shaohui Liu, Hengkai Guo, Wenping Wang, Yong-Jin Liu
In addition, our method is able to retain reasonable accuracy of camera poses on fully static scenes, which consistently outperforms strong state-of-the-art dense correspondence based methods with end-to-end deep learning, demonstrating the potential of dense indirect methods based on optical flow and point trajectories.
1 code implementation • ICCV 2021 • Wang Zhao, Shaohui Liu, Yi Wei, Hengkai Guo, Yong-Jin Liu
Experimental results on ScanNet and RGB-D Scenes V2 demonstrate state-of-the-art performance of the proposed deep MVS system on multi-view depth estimation, with our proposed solver consistently improving the depth quality over both conventional and deep learning based MVS pipelines.
1 code implementation • ICCV 2021 • Yi Wei, Shaohui Liu, Yongming Rao, Wang Zhao, Jiwen Lu, Jie zhou
In this work, we present a new multi-view depth estimation method that utilizes both conventional reconstruction and learning-based priors over the recently proposed neural radiance fields (NeRF).
4 code implementations • CVPR 2020 • Wang Zhao, Shaohui Liu, Yezhi Shu, Yong-Jin Liu
In this work, we tackle the essential problem of scale inconsistency for self-supervised joint depth-pose learning.
no code implementations • 17 Nov 2019 • Yiheng Han, Wang Zhao, Jia Pan, Zipeng Ye, Ran Yi, Yong-Jin Liu
Motion planning for robots of high degrees-of-freedom (DOFs) is an important problem in robotics with sampling-based methods in configuration space C as one popular solution.
no code implementations • CVPR 2019 • Yi Wei, Shaohui Liu, Wang Zhao, Jiwen Lu, Jie zhou
In this paper, we present a new perspective towards image-based shape generation.
1 code implementation • CVPR 2019 • Li Yi, Wang Zhao, He Wang, Minhyuk Sung, Leonidas Guibas
We introduce a novel 3D object proposal approach named Generative Shape Proposal Network (GSPN) for instance segmentation in point cloud data.
Ranked #26 on 3D Object Detection on ScanNetV2