arXiv Computer Vision
Surface Reconstruction from Range Scans Under Visibility Constraints
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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
Oct 22
Register Anything Model for Generalizable and Robust Point Cloud Registration
The paper introduces the Register Anything Model (RAM), a unified approach for 3D point cloud registration that can robustly estimate 6‑DoF transformations across a wide range of domains, including indoor, outdoor, and object scenes. RAM overcomes domain‑specific challenges by employing a geometry‑aware normalization that voxelizes and standardizes point clouds based on their geometric properties, followed by a hierarchical coarse‑to‑fine pipeline with a geometric transformer that focuses on local consistency. Experiments on 12 benchmarks show that RAM achieves state‑of‑the‑art inlier ratios and registration recall, outperforming methods trained on individual domains and demonstrating strong generalization.
arXiv AI
Jul 7
Double-Helix Active Geometry: LiDAR-Anchored Multi-View Depth with Selective Abstention
arXiv:2607. 02561v1 Announce Type: cross Abstract: Consumer depth sensors such as the LiDAR scanner on recent iPhones provide metric range, but their useful range is short and their returns are sparse.
arXiv Machine Learning
Sep 25
M-plicits: Neural Implicit Surfaces via Nested Multiscale Residuals
M-plicits introduces a multiscale framework for neural implicit surfaces that models a surface as a residual sum of MLPs trained on nested neighborhoods. By localizing supervision to narrow bands around previous zero-level sets, the method achieves robustness to noisy input, avoids costly mesh extraction, and enables a multiscale sphere-tracing algorithm with analytical normal computation. Experiments on Stanford and Thingi32 show superior Chamfer distance and IoU metrics compared to existing methods while using far fewer parameters.
arXiv AI
Jul 21
Depth Estimators Are Implicit Neural Fields for 3D Scene Geometry Inpainting and Reconstruction
arXiv:2607. 16286v1 Announce Type: cross Abstract: The 3D geometry of real-world scene data is often incomplete.
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...