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
arXiv Computer Vision
Sep 24

DAVIO: Dense Monocular-Inertial SLAM with Feed-Forward Initialization and Pose-Conditioned Mapping

DAVIO is a dense monocular‑inertial SLAM system that leverages a single multi‑view depth model (Depth Anything 3) for both initialization and mapping. It starts up quickly by solving a feature‑free linear system from a five‑image window and IMU pre‑integration, then uses a VIO filter whose metric poses condition the depth model during tracking. The system corrects residual scale along viewing rays, preserves metric baselines, and refines the map with a gravity‑preserving sub‑map graph, achieving earlier start‑up, lower localization error, and more accurate dense maps than state‑of‑the‑art feed‑forward mappers on both EuRoC and building‑scale ORI datasets.

By Jaafar Mahmoud, Arthur Movsesyan, Mikhail Iumanov, Sergey Kolyubin
Hugging Face Trending Papers
Sep 17

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.