6 papers · 1 filter
HAD: Hallucination-Aware Diffusion Priors for 3D Reconstruction
Xi Liu, Weiwei Sun, Zhou Ren +3
Diffusion priors have recently demonstrated strong capability in enhancing the quality of sparse-view 3D reconstruction by augmenting training views at novel viewpoints, but they i…
Cross-view Domain Generalization via Geometric Consistency for LiDAR Semantic Segmentation
Jindong Zhao, Yuan Gao, Yang Xia +4
Domain-generalized LiDAR semantic segmentation (LSS) seeks to train models on source-domain point clouds that generalize reliably to multiple unseen target domains, which is essent…
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…
NoKSR: Kernel-Free Neural Surface Reconstruction via Point Cloud Serialization
Zhen Li, Weiwei Sun, Shrisudhan Govindarajan +4
We present a novel approach to large-scale point cloud surface reconstruction by developing an efficient framework that converts an irregular point cloud into a signed distance fie…
3D Gaussian Splatting as Markov Chain Monte Carlo
Shakiba Kheradmand, Daniel Rebain, Gopal Sharma +6
While 3D Gaussian Splatting has recently become popular for neural rendering, current methods rely on carefully engineered cloning and splitting strategies for placing Gaussians, w…
Lagrangian Hashing for Compressed Neural Field Representations
Shrisudhan Govindarajan, Zeno Sambugaro, Akhmedkhan +7
We present Lagrangian Hashing, a representation for neural fields combining the characteristics of fast training NeRF methods that rely on Eulerian grids (i.e.~InstantNGP), with th…