3D Gaussian Splatting (3D-GS) has emerged as a significant advancement in the field of
Computer Graphics, offering explicit scene representation and novel view synthesis without …

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 Neural Descriptor Fields (NDFs), an object representation that encodes both
points and relative poses between an object and a target (such as a robot gripper or a rack …

Signed Distance Fields (SDFs) parameterized by neural networks have recently gained
popularity as a fundamental geometric representation. However, editing the shape encoded …

Point cloud sampling is crucial for efficient large-scale point cloud analysis, where learning-
to-sample methods have recently received increasing attention from the community for …

Deep implicit functions have shown remarkable shape modeling ability in various 3D
computer vision tasks. One drawback is that it is hard for them to represent a 3D shape as …

We introduce GEM3D 1–a new deep, topology-aware generative model of 3D shapes. The
key ingredient of our method is a neural skeleton-based representation encoding …

In this paper, we present a simple yet effective formulation called Coverage Axis for 3D
shape skeletonization. Inspired by the set cover problem, our key idea is to cover all the …

In this paper, we address the challenging problem of 3D concept grounding (ie, segmenting
and learning visual concepts) by looking at RGBD images and reasoning about paired …

Abstract Implicit Neural Representations are powerful tools for representing 3D shapes.
They encode an implicit field in the parameters of a Neural Network, leveraging the power of …