2 code implementations • 24 Mar 2023 • Zhengzhe Liu, Peng Dai, Ruihui Li, Xiaojuan Qi, Chi-Wing Fu
The core of our approach is a two-stage feature-space alignment strategy that leverages a pre-trained single-view reconstruction (SVR) model to map CLIP features to shapes: to begin with, map the CLIP image feature to the detail-rich 3D shape space of the SVR model, then map the CLIP text feature to the 3D shape space through encouraging the CLIP-consistency between rendered images and the input text.
no code implementations • 1 Feb 2023 • Jingyu Hu, Ka-Hei Hui, Zhengzhe Liu, Ruihui Li, Chi-Wing Fu
This paper presents a new approach for 3D shape generation, inversion, and manipulation, through a direct generative modeling on a continuous implicit representation in wavelet domain.
1 code implementation • 19 Sep 2022 • Ka-Hei Hui, Ruihui Li, Jingyu Hu, Chi-Wing Fu
This paper presents a new approach for 3D shape generation, enabling direct generative modeling on a continuous implicit representation in wavelet domain.
2 code implementations • 9 Sep 2022 • Zhengzhe Liu, Peng Dai, Ruihui Li, Xiaojuan Qi, Chi-Wing Fu
Text-guided 3D shape generation remains challenging due to the absence of large paired text-shape data, the substantial semantic gap between these two modalities, and the structural complexity of 3D shapes.
1 code implementation • CVPR 2022 • Ka-Hei Hui, Ruihui Li, Jingyu Hu, Chi-Wing Fu
This paper introduces a novel framework called DTNet for 3D mesh reconstruction and generation via Disentangled Topology.
1 code implementation • 28 Feb 2022 • Ruihui Li, Xianzhi Li, Tien-Tsin Wong, Chi-Wing Fu
To achieve a learnable self-embedding scheme, we design a novel framework with two jointly-trained networks: one to encode the input point set into its self-embedded sparse point set and the other to leverage the embedded information for inverting the original point set back.
1 code implementation • 20 Sep 2021 • Chen Long, Wenxiao Zhang, Ruihui Li, Hao Wang, Zhen Dong, Bisheng Yang
Point cloud upsampling is to densify a sparse point set acquired from 3D sensors, providing a denser representation for the underlying surface.
1 code implementation • 10 Aug 2021 • Ruihui Li, Xianzhi Li, Ka-Hei Hui, Chi-Wing Fu
We present SP-GAN, a new unsupervised sphere-guided generative model for direct synthesis of 3D shapes in the form of point clouds.
3 code implementations • CVPR 2021 • Ruihui Li, Xianzhi Li, Pheng-Ann Heng, Chi-Wing Fu
Point clouds produced by 3D scanning are often sparse, non-uniform, and noisy.
1 code implementation • 16 Mar 2020 • Xianzhi Li, Ruihui Li, Guangyong Chen, Chi-Wing Fu, Daniel Cohen-Or, Pheng-Ann Heng
Recently, many deep neural networks were designed to process 3D point clouds, but a common drawback is that rotation invariance is not ensured, leading to poor generalization to arbitrary orientations.
no code implementations • 14 Mar 2020 • Chao Huang, Ruihui Li, Xianzhi Li, Chi-Wing Fu
This paper presents a novel non-local part-aware deep neural network to denoise point clouds by exploring the inherent non-local self-similarity in 3D objects and scenes.
1 code implementation • CVPR 2020 • Ruihui Li, Xianzhi Li, Pheng-Ann Heng, Chi-Wing Fu
We present PointAugment, a new auto-augmentation framework that automatically optimizes and augments point cloud samples to enrich the data diversity when we train a classification network.
Ranked #2 on 3D Point Cloud Data Augmentation on ModelNet40
3 code implementations • ICCV 2019 • Ruihui Li, Xianzhi Li, Chi-Wing Fu, Daniel Cohen-Or, Pheng-Ann Heng
Point clouds acquired from range scans are often sparse, noisy, and non-uniform.