5 accepted papers
Current 3D Gaussian Splatting methods often overlook structured information, leading to disorganized 3D Gaussians that compromise memory and structure efficiency, particularly in Level-of-Detail (LOD) management. This inefficiency results in excessive memory use, limiting their application in resour…
This paper investigates the problem of temporally interpolating dynamic 3D point clouds with large non-rigid deformation. We formulate the problem as estimation of point-wise trajectories (i.e., smooth curves) and further reason that temporal irregularity and under-sampling are two major challenges.…
We propose WarpingGAN, an effective and efficient 3D point cloud generation network. Unlike existing methods that generate point clouds by directly learning the mapping functions between latent codes and 3D shapes, WarpingGAN learns a unified local-warping function to warp multiple identical pre-def…
Motivated by the intuition that one can transform two aligned point clouds to each other more easily and meaningfully than a misaligned pair, we propose CorrNet3D -the first unsupervised and end-to-end deep learning-based framework - to drive the learning of dense correspondence between 3D shapes by…
In this paper, we propose a novel deep neural network based method, called PUGeo-Net, for upsampling 3D point clouds. PUGeo-Net incorporates discrete differential geometry into deep learning elegantly by learning the first and second fundamental forms that are able to fully represent the local geome…