DrawSplat is an Atlanta World-guided implicit Gaussian SLAM framework designed for indoor reconstruction. It first sketches structural layout using explicit point-line geometry, then refines appearance and geometry with implicit neural Gaussians. The method jointly tracks camera poses, sparse geometry, and gravity, infers structural planes, and allocates Gaussian capacity to informative regions, achieving robust tracking, high-fidelity rendering, and strong geometric reconstruction while balancing quality, efficiency, and model size.
By Fan Zhu, Zhisong Xu, Ziyu Chen, Zhenjun Zhao, Takayuki Okatani
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.
VGGT-GS SLAM is a monocular 3D Gaussian Splatting SLAM system that operates on uncalibrated videos. It uses feed‑forward VGGT pose and depth priors to perform submap differentiable bundle adjustment, jointly refining camera poses, a 3D Gaussian map, and submap‑shared intrinsics and distortion parameters via analytic calibration Jacobians. The method introduces Gaussian‑native alignment for camera‑anchored scale refinement between submaps and loop‑closure verification, achieving improved localization accuracy and rendering quality on standard indoor benchmarks.
By Yuhang Han, Hao Wang, Jiaxi Cao, Xingyu Liu
arXiv:2609.17387v1 Announce Type: new
Abstract: Real-time dense SLAM is a core capability for robotics applications that require robust localization and high- quality mapping in dynamic or fast-chang...
By Yongqi Mao, Hao Shi, Yufan Zhang, Zhonghua Yi, Xiangfei Guo, Kaiwei Wang
The paper introduces LingGS, an online monocular RGB Gaussian splatting SLAM system that decouples feed‑forward geometry prediction from camera tracking. By leveraging a frozen streaming 3D foundation model as a virtual RGB‑D sensor for depth estimation and an explicit geometric solver for camera motion via feature reprojection, LingGS achieves higher tracking accuracy than learned predictions. Experiments on indoor and outdoor datasets demonstrate significant improvements in reconstruction quality, achieving about 7 FPS on a single RTX 4090 and 2–3× the throughput of prior SLAM systems.
By Da Li, Yihan Zang, Ivan Viola, Zeyu Wang, Dominik Engel
arXiv:2609.25746v1 Announce Type: cross
Abstract: ICP-based 3D Gaussian Splatting (3DGS) SLAM tracks in real time by registering incoming frames against map Gaussians, using each primitive's covarian...
By Edward Beng Wai Tan, Siew-Kei Lam
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
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
arXiv:2606.29494v3 Announce Type: replace
Abstract: Semantic 3D Gaussian SLAM commonly fuses 2D semantic priors into a persistent 3D map using uniform optimization weights. However, such priors are n...
By Raman Jha, Shuaihang Yuan, Yi Fang
RawSLAM introduces the first online Gaussian SLAM framework that operates directly on single‑exposure 16‑bit linear HDR images, overcoming the limitations of traditional 8‑bit LDR SLAM systems in extreme lighting. The approach combines an HDR Gaussian splatting module with a Reinhard‑compressed photometric objective and structure‑guided spatial weighting, achieving superior trajectory and reconstruction accuracy compared to a direct HDR adaptation of MonoGS. The same formulation also improves performance on standard 8‑bit inputs and can be transferred to other SLAM systems such as SplaTAM, Gaussian SLAM, and DROID‑W, eliminating tracking failures in challenging illumination sequences. Additionally, RawSLAM provides a new dataset of 10 real‑world indoor sequences with 16‑bit RAW imagery, depth, IMU, and OptiTrack poses.
By Marina Orozco Gonz\'alez, Luis Merino
arXiv:2609.21347v1 Announce Type: new
Abstract: Recent progress in 3D Gaussian Splatting (3DGS) has enabled dense visual SLAM with pinhole cameras, yet most pipelines are not designed for panoramic i...
By Xiangfei Guo, Hao Shi, Yufan Zhang, Zhonghua Yi, Yongqi Mao, Xiaoting Yin, Kaiwei Wang
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