To date, various 3D scene understanding tasks still lack practical and generalizable pre-
trained models, primarily due to the intricate nature of 3D scene understanding tasks and …

Recent deep networks that directly handle points in a point set, eg, PointNet, have been
state-of-the-art for supervised learning tasks on point clouds such as classification and …

This study investigates the accuracy of deep learning models for the inference of Reynolds-
averaged Navier–Stokes (RANS) solutions. This study focuses on a modernized U-net …

Learning and analyzing 3D point clouds with deep networks is challenging due to the
sparseness and irregularity of the data. In this paper, we present a data-driven point cloud …

We study the problem of learning conditional generators from noisy labeled samples, where
the labels are corrupted by random noise. A standard training of conditional GANs will not …

We propose SDFDiff, a novel approach for image-based shape optimization using
differentiable rendering of 3D shapes represented by signed distance functions (SDFs) …

Point clouds provide a flexible and natural representation usable in countless applications
such as robotics or self-driving cars. Recently, deep neural networks operating on raw point …

Abstract We present RL-GAN-Net, where a reinforcement learning (RL) agent provides fast
and robust control of a generative adversarial network (GAN). Our framework is applied to …

Generating 3D point clouds is challenging yet highly desired. This work presents a novel
autoregressive model, PointGrow, which can generate diverse and realistic point cloud …

The availability of affordable and portable depth sensors has made scanning objects and
people simpler than ever. However, dealing with occlusions and missing parts is still a …