The paper introduces a reliability-regulated trajectory optimization framework for progressive COLMAP‑free 3D Gaussian Splatting (3DGS). It uses a self‑supervised bidirectional cycle‑consistency mechanism to control camera trajectory estimation through forward motion propagation and retrospective trajectory correction, thereby reducing error compounding without external priors. Experiments on Tanks and Temples and CO3D‑V2 demonstrate improved camera trajectory accuracy and novel‑view rendering quality compared to existing unposed baselines.
By Zijian Wu, Jinliang Wang, Zidian Lin, Ying Song, Ziqian Lu, Hanjie Ma, Zhen Ye, Mingfeng Jiang
arXiv:2605.25059v4 Announce Type: replace
Abstract: Crucial for autonomous exploration, online 3D occupancy prediction and mapping incrementally construct dense spatial representations on the fly. Em...
By Ruoyu Wang, Yong Liu, Jiahan Li, Sheng Tao, Yuhang Lin, Yukai Ma
arXiv:2608.29003v1 Announce Type: cross
Abstract: In dynamic and unstructured environments, conventional SLAM systems generally suffer from significant accuracy degeneration due to their static assum...
By Wenting Wang, Jiaxin Guo, Wenzhen Dong, Yun-Hui Liu, Charlie C. L. Wang, Yeung Yam
Existing Gaussian-splatting-based monocular Simultaneous Localization and Mapping (SLAM) systems are either tailored to short sequences, are not real-time, or suffer from prohibitive GPU memory requirements, limiting their applicability in realistic, long-horizon scenarios. To address this, we present GLAM-SLAM, a real-time, decoupled Gaussian-splatting SLAM system designed for large-scale outdoor scenes.
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.
arXiv:2609.14634v1 Announce Type: new
Abstract: Recently, 3D Gaussian Splatting SLAM (3DGS-SLAM) has gained significant momentum in simultaneous localization and 3DGS scene reconstruction. In real-wo...
By Kumaran Karthik, Pramat Shastri Jois, Suresh Sundaram
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:2509.04600v2 Announce Type: replace
Abstract: Reconstructing global human motion from monocular video is fundamental to VR, graphics, and robotics, yet remains ill-posed due to depth ambiguity,...
By Zhongyuan Hu, Qijun Ying, Jiazhi Shu, Ronghui Li, Yu Lu, Zijiao Zeng, Xiu Li
GS‑VLA introduces a lightweight, plug‑and‑play framework that uses a 4 M‑parameter 3D‑Gaussian canonicalizer to adapt frozen Vision‑Language‑Action (VLA) policies to viewpoint shifts without retraining the policy. By treating viewpoint changes as a localized novel‑view synthesis problem under a locality assumption, the method normalizes observations through a scene‑ and policy‑independent disocclusion task. Experiments on the LIBERO benchmark demonstrate that GS‑VLA recovers a large portion of performance lost due to camera displacement, improving results across different policy architectures, unseen task suites, and perturbation scales.
whyItMatters":"The approach offers a computationally efficient alternative to costly fine‑tuning or generative augmentation, enabling robust VLA deployment in real‑world settings where camera configurations may vary."
By Yechan Park, HyunJin Kim
The paper introduces Adaptive World Memory 3D Foundation Model (AWM-3DFM), a memory‑centric 3D foundation model that scales to large‑scale robotic localization, reconstruction, and Gaussian rendering. It employs transformer‑based gated updates, test‑time temporal‑spatial regulation, and local submap organization to maintain persistent memory, accuracy, and consistency across long image sequences. A Gaussian reconstruction head unifies pose estimation, dense point‑cloud reconstruction, and photorealistic rendering, achieving superior trajectory accuracy, reconstruction completeness, and rendering quality on public benchmarks and diverse robotic datasets.
By Tianchen Deng, Guole Shen, Yilin Shen, Wenhua Wu, Yilin Fang, Ziqi Ma, Tianjun Zhang, Shenghai Yuan, Wolfram Burgard, Hesheng Wang
Know-Your-Scene (KYS)-SLAM extends ORB‑SLAM3 by replacing binary feature rejection with continuous correspondence modulation based on semantic, panoptic, and motion priors. Each keypoint is augmented with hierarchical compatibility scores that down‑weight features on independently moving objects while preserving static structure, using a training‑free depth‑aware ego‑motion model and self‑calibrating thresholds. Across 21 stereo sequences, KYS‑SLAM achieves a 17.4% ATE RMSE reduction on outdoor KITTI, 27.7% on indoor EuRoC, and significant improvements on dynamic and synthetic datasets without per‑sequence tuning.
By Preeti Chatterjee, Jin Lu, Jin Sun, Suchendra M. Bhandarkar
arXiv:2609.16864v1 Announce Type: cross
Abstract: Vision-language-action (VLA) models have achieved impressive performance in quasi-static manipulation, but struggle in dynamic manipulation tasks bec...
By Zhenyang Feng, Jimin Heo, Erik B. Sudderth, Unnat Jain