Search Results for author: Ruihui Li

Found 11 papers, 8 papers with code

Neural Wavelet-domain Diffusion for 3D Shape Generation

no code implementations19 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.

3D Shape Generation

ISS: Image as Stepping Stone for Text-Guided 3D Shape Generation

no code implementations9 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.

3D Shape Generation

Neural Template: Topology-aware Reconstruction and Disentangled Generation of 3D Meshes

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.

Point Set Self-Embedding

1 code implementation28 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.

PC$^2$-PU: Patch Correlation and Point Correlation for Effective Point Cloud Upsampling

1 code implementation20 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.

SP-GAN: Sphere-Guided 3D Shape Generation and Manipulation

1 code implementation10 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.

3D Shape Generation

Point Cloud Upsampling via Disentangled Refinement

2 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.

A Rotation-Invariant Framework for Deep Point Cloud Analysis

1 code implementation16 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.

Point Cloud Generation

Non-Local Part-Aware Point Cloud Denoising

no code implementations14 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.

Denoising Graph Attention +1

PointAugment: an Auto-Augmentation Framework for Point Cloud Classification

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.

3D Point Cloud Data Augmentation Classification +2

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