← Back to all news
Hugging Face Trending Papers

EliGSiR: Continual RGB-D Mapping with Gaussian Splatting under Bounded Compute

EliGSiR is a continual RGB‑D mapping system that extends Gaussian splatting to handle online, bounded‑compute scenarios. It introduces Map‑Guided View Scheduling to filter redundant views, Load‑Adaptive Fidelity to adjust supervision resolution, and Targeted Geometry Growth to add structure only where needed. Experiments on Replica, TUM RGB‑D, ScanNet++ and real sensor data show that EliGSiR outperforms baselines in reconstruction quality while efficiently using the available mapping budget.

Related stories

arXiv Computer Vision
Sep 18

EliGSiR: Continual RGB-D Mapping with Gaussian Splatting under Bounded Compute

EliGSiR is a continual RGB‑D mapping method that extends 3D Gaussian Splatting to online settings by adaptively allocating optimization resources. It introduces Map‑Guided View Scheduling to filter redundant views, Load‑Adaptive Fidelity to adjust supervision resolution, and Targeted Geometry Growth to add geometric capacity only where needed. The approach is evaluated on Replica, TUM RGB‑D, ScanNet++, and real sensor sequences, achieving higher reconstruction quality and faster performance than several baselines.

By Bj\"orn Ellensohn, Elmar Rueckert, Christian Rauch
More like this →
arXiv Computer Vision
Sep 28

Gauss What You Need: Compact Gaussian Splatting Across Scene Scales

Gauss What You Need: Compact Gaussian Splatting Across Scene Scales introduces TangoGS, a method that automatically selects the number of Gaussian primitives for 3D Gaussian Splatting by combining capture-derived model sizing with training-based adaptation. The approach first estimates a learning allowance based on the capture’s total pixels, then adjusts the number of Gaussians during training according to reconstruction quality. On standard benchmarks, TangoGS matches the best baseline’s PSNR while using 48% fewer Gaussians, and on larger captures it scales automatically to achieve the highest mean PSNR with 2.3× more Gaussians.

By Afif Boudaoud, Jiayi Liu, Alexandru Calotoiu, Torsten Hoefler
benchmarks
More like this →
arXiv AI
Jul 24

Robostral Navigate

arXiv:2607. 20785v1 Announce Type: cross Abstract: Deploying navigation systems at scale requires a recipe that minimizes sensor assumptions, generalizes across robot embodiments, and trains efficiently.

By Arjun Majumdar, Avinash Sooriyarachchi, Benjamin Tibi, Chris Bamford, Elliot Chane-Sane, Guillaume Lample, Khyathi Raghavi Chandu, Ludovic Ho Fuh, Mathieu Poiree, Olivier Duchenne, Rosalie Millner, Srijan Mishra, Theo Cachet, Thomas Chabal
llmsreinforcement-learningroboticsmultimodalbenchmarks
More like this →
arXiv Computer Vision
Aug 28

CGS-SLAM: Collaborative Gaussian Splatting based SLAM for Multi-Agent Reconstruction

CGS‑SLAM is a hybrid decentralized/centralized SLAM system that enables multi‑agent 3D Gaussian Splatting reconstruction using only RGB images and inertial data. Each agent locally tracks motion with inertial priors, builds a scaled map via a monocular depth estimator, and shares keyframe encodings to facilitate dynamic keyframing and submap alignment. A central server then aligns submaps with a view‑alignment model, keeping communication costs low while achieving competitive tracking, higher rendering quality, and accurate submap alignment in GNSS‑denied environments.

By Jean-Daniel de Ambrogi, Aladine Chetouani, Vincent Nguyen, Aur\'elien Chateigner
agentsbenchmarkssafety
More like this →