10 citations · 14 across the 13 of their papers we have counts for
5 papers · 1 filter
Weakly Supervised Point Cloud Segmentation via Conservative Propagation of Scene-level Labels
Shaobo Xia, Jun Yue, Kacper Kania +4
We propose a weakly supervised semantic segmentation method for point clouds that predicts "per-point" labels from just "whole-scene" annotations. The key challenge here is the dis…
PointNeRF++: A multi-scale, point-based Neural Radiance Field
Weiwei Sun, Eduard Trulls, Yang-Che Tseng +4
Point clouds offer an attractive source of information to complement images in neural scene representations, especially when few images are available. Neural rendering methods base…
Volumetric Rendering with Baked Quadrature Fields
Gopal Sharma, Daniel Rebain, Kwang Moo Yi +1
We propose a novel Neural Radiance Field (NeRF) representation for non-opaque scenes that enables fast inference by utilizing textured polygons. Despite the high-quality novel view…
Unsupervised Keypoints from Pretrained Diffusion Models
Eric Hedlin, Gopal Sharma, Shweta Mahajan +5
Unsupervised learning of keypoints and landmarks has seen significant progress with the help of modern neural network architectures, but performance is yet to match the supervised…
Accelerating Neural Field Training via Soft Mining
Shakiba Kheradmand, Daniel Rebain, Gopal Sharma +4
We present an approach to accelerate Neural Field training by efficiently selecting sampling locations. While Neural Fields have recently become popular, it is often trained by uni…