190 citations · 244 across the 9 of their papers we have counts for
13 papers
Neural Kernel Surface Reconstruction
Jiahui Huang, Zan Gojcic, Matan Atzmon +3
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 Kernel…
Neural LiDAR Fields for Novel View Synthesis
Shengyu Huang, Zan Gojcic, Zian Wang +5
We present Neural Fields for LiDAR (NFL), a method to optimise a neural field scene representation from LiDAR measurements, with the goal of synthesizing realistic LiDAR scans from…
LION: Latent Point Diffusion Models for 3D Shape Generation
Xiaohui Zeng, Arash Vahdat, Francis Williams +4
Denoising diffusion models (DDMs) have shown promising results in 3D point cloud synthesis. To advance 3D DDMs and make them useful for digital artists, we require (i) high generat…
Learning Smooth Neural Functions via Lipschitz Regularization
Hsueh-Ti Derek Liu, Francis Williams, Alec Jacobson +2
Neural implicit fields have recently emerged as a useful representation for 3D shapes. These fields are commonly represented as neural networks which map latent descriptors and 3D…
Neural Fields as Learnable Kernels for 3D Reconstruction
Francis Williams, Zan Gojcic, Sameh Khamis +4
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 results wh…
Symmetry Breaking in Symmetric Tensor Decomposition
Yossi Arjevani, Joan Bruna, Michael Field +3
In this note, we consider the highly nonconvex optimization problem associated with computing the rank decomposition of symmetric tensors. We formulate the invariance properties of…
Human 3D keypoints via spatial uncertainty modeling
Francis Williams, Or Litany, Avneesh Sud +2
We introduce a technique for 3D human keypoint estimation that directly models the notion of spatial uncertainty of a keypoint. Our technique employs a principled approach to model…
Neural Splines: Fitting 3D Surfaces with Infinitely-Wide Neural Networks
Francis Williams, Matthew Trager, Joan Bruna +1
We present Neural Splines, a technique for 3D surface reconstruction that is based on random feature kernels arising from infinitely-wide shallow ReLU networks. Our method achieves…
VoronoiNet: General Functional Approximators with Local Support
Francis Williams, Daniele Panozzo, Kwang Moo Yi +1
Voronoi diagrams are highly compact representations that are used in various Graphics applications. In this work, we show how to embed a differentiable version of it -- via a novel…
Gradient Dynamics of Shallow Univariate ReLU Networks
Francis Williams, Matthew Trager, Claudio Silva +3
We present a theoretical and empirical study of the gradient dynamics of overparameterized shallow ReLU networks with one-dimensional input, solving least-squares interpolation. We…
Unwind: Interactive Fish Straightening
Francis Williams, Alexander Bock, Harish Doraiswamy +5
The ScanAllFish project is a large-scale effort to scan all the world's 33,100 known species of fishes. It has already generated thousands of volumetric CT scans of fish species wh…
ABC: A Big CAD Model Dataset For Geometric Deep Learning
Sebastian Koch, Albert Matveev, Zhongshi Jiang +6
We introduce ABC-Dataset, a collection of one million Computer-Aided Design (CAD) models for research of geometric deep learning methods and applications. Each model is a collectio…
Deep Geometric Prior for Surface Reconstruction
Francis Williams, Teseo Schneider, Claudio Silva +3
The reconstruction of a discrete surface from a point cloud is a fundamental geometry processing problem that has been studied for decades, with many methods developed. We propose…