58 citations · 84 across the 7 of their papers we have counts for
8 papers
CACFG: Curvature-Aware Classifier-Free Guidance and Optimal Control
Max Collins, Dasith de Silva Edirimuni, Jordan Vice +2
Diffusion models generate samples by learning to reverse a fixed corruption process, and classifier-free guidance (CFG) is the standard mechanism for conditioning this process on a…
Class-Partitioned VQ-VAE and Latent Flow Matching for Point Cloud Scene Generation
Dasith de Silva Edirimuni, Ajmal Saeed Mian
Most 3D scene generation methods are limited to only generating object bounding box parameters while newer diffusion methods also generate class labels and latent features. Using o…
A Survey of Deep Learning-based Point Cloud Denoising
Jinxi Wang, Ben Fei, Dasith de Silva Edirimuni +3
Accurate 3D geometry acquisition is essential for a wide range of applications, such as computer graphics, autonomous driving, robotics, and augmented reality. However, raw point c…
Hybrid Long and Short Range Flows for Point Cloud Filtering
Dasith de Silva Edirimuni, Xuequan Lu, Ajmal Saeed Mian +4
Point cloud capture processes are error-prone and introduce noisy artifacts that necessitate filtering/denoising. Recent filtering methods often suffer from point clustering or noi…
StraightPCF: Straight Point Cloud Filtering
Dasith de Silva Edirimuni, Xuequan Lu, Gang Li +3
Point cloud filtering is a fundamental 3D vision task, which aims to remove noise while recovering the underlying clean surfaces. State-of-the-art methods remove noise by moving no…
IterativePFN: True Iterative Point Cloud Filtering
Dasith de Silva Edirimuni, Xuequan Lu, Zhiwen Shao +3
The quality of point clouds is often limited by noise introduced during their capture process. Consequently, a fundamental 3D vision task is the removal of noise, known as point cl…
Contrastive Learning for Joint Normal Estimation and Point Cloud Filtering
Dasith de Silva Edirimuni, Xuequan Lu, Gang Li +1
Point cloud filtering and normal estimation are two fundamental research problems in the 3D field. Existing methods usually perform normal estimation and filtering separately and o…
Deep Point Cloud Normal Estimation via Triplet Learning
Weijia Wang, Xuequan Lu, Dasith de Silva Edirimuni +2
Normal estimation on 3D point clouds is a fundamental problem in 3D vision and graphics. Current methods often show limited accuracy in predicting normals at sharp features (e.g.,…