Hugging Face Trending Papers
Neural Implicit Energy Optimization for LiDAR Mapping
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arXiv Machine Learning
Oct 21
Neural Implicit Energy Optimization for LiDAR Mapping
arXiv:2610.28598v1 Announce Type: cross Abstract: Neural implicit representations provide memory-efficient signed distance field (SDF) parameterizations for large-scale LiDAR scenes, but the geometry...
arXiv Computer Vision
Aug 25
As-Rigid-As-Possible Regularization for Implicit Surfaces
arXiv:2608.15933v1 Announce Type: cross Abstract: Implicit surface representations have regained popularity because of their use in machine learning. A common component in optimization is regularizat...
Hugging Face Trending Papers
Sep 21
ZVeC: A Zero-Shot Framework for Instance-Level Vehicle Extraction and Generative Point Cloud Completion
LiDAR point clouds acquired in underground environments exhibit severe geometric incompleteness due to occlusions and limited sensor viewpoints, making reliable point cloud completion challenging with...
arXiv Computer Vision
Aug 28
NeuDonatello: Uncertainty-Aware Framework for Accurate Neural SDF Learning
NeuDonatello is a new framework for neural signed distance function (SDF) learning that explicitly models spatially varying uncertainty using Monte Carlo sampling. By incorporating this uncertainty into an adaptive regularization scheme and an uncertainty-aware SDF-to-density conversion, the method selectively strengthens geometric constraints where RGB supervision is unreliable, thereby improving surface reconstruction accuracy. Experiments show that NeuDonatello achieves state‑of‑the‑art results on diverse scenes using only posed RGB images.
arXiv Computer Vision
6d ago
Surface Reconstruction from Range Scans Under Visibility Constraints
arXiv:2610.18005v2 Announce Type: replace Abstract: Surface reconstruction from range scans is challenging in the presence of noise and outliers, as fidelity to observations must be balanced against...
arXiv Computer Vision
Sep 22
ZVeC: A Zero-Shot Framework for Instance-Level Vehicle Extraction and Generative Point Cloud Completion
arXiv:2609.24825v1 Announce Type: new Abstract: LiDAR point clouds acquired in underground environments exhibit severe geometric incompleteness due to occlusions and limited sensor viewpoints, making...