Recent advances in machine learning have led to increased interest in solving visual
computing problems using methods that employ coordinate‐based neural networks. These …

We present a novel method for reconstructing a 3D implicit surface from a large-scale,
sparse, and noisy point cloud. Our approach builds upon the recently introduced Neural …

This article does not describe a working system. Instead, it presents a single idea about
representation that allows advances made by several different groups to be combined into …

We study the problem of 3D semantic segmentation from raw point clouds. Unlike existing
methods which primarily rely on a large amount of human annotations for training neural …

Capsule networks emerged as a promising alternative to convolutional neural networks for
learning object-centric representations. The idea is to explicitly model part-whole hierarchies …

Labelling point clouds fully is highly time-consuming and costly. As larger point cloud
datasets with billions of points become more common, we ask whether the full annotation is …

We present RoReg, a novel point cloud registration framework that fully exploits oriented
descriptors and estimated local rotations in the whole registration pipeline. Previous …

In this paper, we propose a novel local descriptor-based framework, called You Only
Hypothesize Once (YOHO), for the registration of two unaligned point clouds. In contrast to …

We propose a novel method that renders point clouds as if they are surfaces. The proposed
method is differentiable and requires no scene-specific optimization. This unique capability …

Abstract We present Neural Kernel Fields: a novel method for reconstructing implicit 3D
shapes based on a learned kernel ridge regression. Our technique achieves state-of-the-art …