190 citations · 233 across the 4 of their papers we have counts for
7 papers · 1 filter
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…
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…
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…