23 citations · 27 across the 4 of their papers we have counts for
7 papers · 1 filter
Neural Wavelet-domain Diffusion for 3D Shape Generation
Ka-Hei Hui, Ruihui Li, Jingyu Hu +1
This paper presents a new approach for 3D shape generation, enabling direct generative modeling on a continuous implicit representation in wavelet domain. Specifically, we propose…
Point Set Self-Embedding
Ruihui Li, Xianzhi Li, Tien-Tsin Wong +1
This work presents an innovative method for point set self-embedding, that encodes the structural information of a dense point set into its sparser version in a visual but impercep…
SP-GAN: Sphere-Guided 3D Shape Generation and Manipulation
Ruihui Li, Xianzhi Li, Ka-Hei Hui +1
We present SP-GAN, a new unsupervised sphere-guided generative model for direct synthesis of 3D shapes in the form of point clouds. Compared with existing models, SP-GAN is able to…
Point Cloud Upsampling via Disentangled Refinement
Ruihui Li, Xianzhi Li, Pheng-Ann Heng +1
Point clouds produced by 3D scanning are often sparse, non-uniform, and noisy. Recent upsampling approaches aim to generate a dense point set, while achieving both distribution uni…
PointAugment: an Auto-Augmentation Framework for Point Cloud Classification
Ruihui Li, Xianzhi Li, Pheng-Ann Heng +1
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…
Non-Local Part-Aware Point Cloud Denoising
Chao Huang, Ruihui Li, Xianzhi Li +1
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. Different…