Dense slam. However, these methods ignore issues of detail and consistency in different parts of the scene. The question of "representation" is central in the context of dense simultaneous localization and mapping (SLAM). It incorporates appearance, geometry, and semantic features through multi-channel optimization, addressing the oversmoothing limitations of neural implicit SLAM systems in high-quality rendering, scene understanding, and object-level geometry. ESLAM reads RGB-D frames with unknown camera poses in a sequential manner and incrementally reconstructs the scene representation while estimating the current camera position in the scene. Apr 22, 2025 · MASt3R-SLAM is a truly plug and play monocular dense SLAM pipeline that operates in-the-wild. However, blending representation learning approaches with classical SLAM systems has remained Wang, Haocheng; Shou, Yejun; Shen, Lingfeng; Li, Shuai; Cao, Yanlong (2026) RGD-SLAM: Robust Gaussian splatting SLAM for dynamic environments. This repo uses copied upstream Spann3R code under spann3r_core/ (no Spann3R submodule) and runs directly with Colby, Kansas – Drivers across northwest Kansas are facing quarter-mile visibility early Sunday, with dense fog cutting sightlines to around 1,320 feet Article: ??sLAM: Dense SLAM meets Automatic Differentiation Summary The question of representation is central in the context of dense simultaneous localization and mapping (SLAM). To address this, we propose RGBDS-SLAM, a RGB-D semantic dense SLAM system based on 3D multi-level pyramid Gaussian In response, we introduce Loopy-SLAM that globally optimizes poses and the dense 3D model. We introduce efficient methods for pointmap matching, camera tracking and local Using higher-level entities during mapping has the potential to improve camera localisation performance and give substantial perception capabilities to real-time 3D SLAM systems. In this paper, we present a dense RGB SLAM method with neural implicit map representation. This Abstract We present a real-time monocular dense SLAM system de-signed bottom-up from MASt3R, a two-view 3D reconstruc-tion and matching prior. For pose estimation, TANDEM performs photometric bundle adjustment based on a sliding window of keyframes. In this paper, we propose GSORB-SLAM, a dense SLAM framework that integrates 3DGS with ORB features through a tightly coupled optimization pipeline. It facilitates a better balance between efficiency and accuracy. e. Our insight is that dense monocular SLAM provides the right information to fit a neural radiance field of the . We propose a novel geometric and photometric 3D mapping pipeline for accurate and real-time scene reconstruction from casually taken monocular images. Following the advent of Neural Radiance Fields (NeRF) [37] and the demonstration of their capacity to rea-son about the geometry Abstract We present SGS-SLAM, the first semantic visual SLAM system based on 3D Gaussian Splatting. Visual-based SLAM techniques play a significant role in this field, as they are based on a low-cost and small sensor system, which guarantees those advantages compared to other sensor-based SLAM Gaussian SLAM systems excel in real-time rendering and fine-grained reconstruction compared to NeRF-based systems. Equipped with this strong prior, our system is robust on in-the-wild video sequences despite making no assumption on a fixed or parametric camera model beyond a unique camera centre. However, current methods are often hampered by the non-volumetric or implicit way they represent a scene. GS-SLAM introduces a novel dense visual SLAM system using 3D Gaussian Splatting for real-time, efficient, and accurate scene reconstruction and camera tracking. To address these issues, we propose Developing a high-quality, real-time, dense visual SLAM system poses a significant challenge in the field of computer vision. Despite modern SLAM methods achieving impressive results on synthetic datasets, they still struggle with real-world datasets. CG-SLAM can achieve state-of-the-art performance in tracking, mapping, rendering, and efficiency. Here we elaborate on how to load the necessary data, configure Gaussian-SLAM for your use-case, debug it, and how to reproduce the results mentioned in the paper. We introduce efficient methods for pointmap matching, camera tracking and local May 26, 2025 · To overcome these challenges, we propose ADD-SLAM: an Adaptive Dynamic Dense SLAM framework based on Gaussian splitting. Jun 5, 2025 · Dense visual SLAM systems based on 3D Gaussian splatting have successfully achieved photorealistic scene reconstruction. MASt3R-SLAM: Real-Time Dense SLAM with 3D Reconstruction Priors Riku Murai* · Eric Dexheimer* · Andrew J. We demonstrate that both tracking and mapping can be performed with the same point-based neural scene representation by minimizing an RGBD Gaussian-SLAM can reconstruct a renderable 3D scene from a RGBD stream. Time-of-Flight (ToF) sensors provide efficient active depth sensing at relatively low power budgets; among such designs, only very sparse measurements from low-resolution sensors are considered to meet the increasingly limited power constraints of mobile and AR/VR devices. We introduce a unique semantic feature loss that effectively We propose a method where CNN-predicted dense depth maps are naturally fused together with depth mea-surements obtained from direct monocular SLAM. To address these issues, we introduce DDN-SLAM, High-fidelity reconstruction is crucial for dense SLAM. Pattern Recognition Headlights glow in a gray blur along Shore Drive as dense fog blankets Virginia Beach this morning. To address these issues, we propose KBGS-SLAM, a keyframe-optimized and bundle-adjusted dense visual SLAM system. We propose a method where CNN-predicted dense depth maps are naturally fused together with depth measurements obtained from direct monocular SLAM, based Abstract—We present a novel approach to real-time dense visual SLAM. Abstract Dense simultaneous localization and mapping (SLAM) is crucial for robotics and augmented reality applications. Recent popular approaches utilize 3D Gaussian Splatting (3D GS) techniques for RGB, depth, and semantic reconstruction of scenes. It is first of its kind real-time SLAM system that leverages MASt3R’s 3D Reconstruction priors to achieve superior reconstruction quality while maintaining consistent camera pose tracking. To this end, we introduce GRS-SLAM3R, an end-to-end SLAM framework for dense scene reconstruction and pose estimation from RGB images without any prior knowledge of Model and frame initialization # In a SLAM system, we maintain a model built upon a Voxel Block Grid, an input frame containing raw RGB-D input, and a synthesized frame generated from the model with volumetric ray casting. However, existing neural implicit SLAM methods suffer from long runtimes and face challenges when modeling complex structures in scenes. We present SGS-SLAM, the first semantic visual SLAM system based on Gaussian Splatting. To address this, we propose RGBDS-SLAM, a RGB-D semantic dense SLAM system based on 3D multi-level pyramid We present a dense simultaneous localization and mapping (SLAM) method that uses 3D Gaussians as a scene representation. , 3D Gaussians, to enable high-fidelity reconstruction from We propose a dense neural simultaneous localization and mapping (SLAM) approach for monocular RGBD input which anchors the features of a neural scene representation in a point cloud that is iteratively generated in an input-dependent data-driven manner. We use frame-to-model tracking using a data-driven point-based submap generation method and trigger loop closures online by performing global place recognition. Our system is capable of capturing comprehensive dense globally consistent surfel-based maps of room scale envi-ronments explored using an RGB-D camera in an incremental online fashion, without pose graph optimisation or any post-processing steps. However, their performance is limited in efficiency and global consistency of camera tracking and mapping. along low-textured regions, and vice-versa. We design an adaptive dynamic identification mechanism grounded in scene consistency analysis, comparing geometric and textural discrepancies between real-time observations and historical maps. ADD-SLAM achieves promising performance across multiple dynamic datasets. NeRF introduces neural implicit representation, marking a notable advancement in visual SLAM research. , 3D Gaussians, to enable high-fidelity The application of monocular dense Simultaneous Localization and Mapping (SLAM) is often hindered by high latency, large GPU memory consumption, and reliance on camera calibration. Our approach enables interactive-time reconstruction and photo-realistic rendering from real-world single-camera RGBD videos. To mitigate the effects of noise and artifacts, we propose a novel geometric representation and optimization method for tracking, which significantly enhances localization accuracy and robustness. We present an efficient new real-time approach which densely maps an environment using bounded planes and surfels extracted from depth images (like those produced by RGB-D sensors or dense multi-view stereo We propose Loopy-SLAM, a dense RGBD SLAM ap-proach which anchors neural features in point cloud submaps that grow iteratively in a data-driven man-ner during scene exploration. High-quality reconstruction is crucial for dense SLAM. It provides a repository of differentiable building blocks for a dense SLAM system, such as differentiable nonlinear least squares solvers, differentiable ICP (iterative closest point) techniques, differentiable raycasting modules, and differentiable mapping/fusion blocks. Our investigations reveal that Gaussian-based scene representations exhibit geometry distortion under novel viewpoints, which significantly degrades the accuracy of Gaussian-based tracking methods. We actively utilize 3D point cloud information to improve the tracking accuracy and operating speed of the system. g. In this paper, we introduce \\textbf{GS-SLAM} that first utilizes 3D Gaussian representation in the Simultaneous Localization and Mapping (SLAM) system. Abstract We present the first neural RGBD SLAM method capable of photorealistically reconstructing real-world scenes. However, blending representation learning approaches with "classical" SLAM systems has remained an open question, because of their highly modular and complex 这篇文章是较为完整的一套基于CNN的视觉SLAM方法，并且，是基于单目视觉的。该方法在估计两帧（关键帧）之间的位姿时，还会用CNN做 深度预测(相比于位姿估计，深度预测才是本文的核心)，按照文章的说法，其深度预… We propose GauS-SLAM, a dense RGB-D SLAM system that leverages 2D Gaussian surfels to achieve robust tracking and high-fidelity mapping. However, most existing methods only use image pairs to estimate pointmaps, overlooking spatial memory and global consistency. To reach this challenging goal without depth input, we introduce a hierarchical feature volume to In this paper, we propose a dense RGB-D SLAM based on 3D Gaussian splatting (3DGS) while employing generalized iterative closest point (G-ICP) for pose estimation. While traditional SLAM systems [16, 41, 45, 55, 76, 77] mostly focus on localization accuracy, recent learning-based dense visual SLAM methods [2,11,25,35,60,64,65, 67, 81, 86] provide meaningful global 3D maps and show reasonable but limited reconstruction accuracy. The code base also supports collaborative mapping sessions with multiple independently moving cameras. Recent popular methods utilize 3D Gaussian splatting (3D GS) techniques for RGB, depth, and semantic reconstruction of scenes. A dense SLAM system is essential for mobile robots, as it provides localization and allows navigation, path planning, obstacle avoidance, and decision-making in unstructured environments. To this end, we propose a novel effective strategy for seeding new Gaussians for newly explored areas and their effective online optimization that is This study introduces an enhanced ORB-SLAM3 algorithm to address the limitations of traditional visual SLAM systems in feature extraction and localization accuracy within the challenging terrains of open-pit mining environments. In addition, we design a self-supervised training scheme using DINO as a prior, enabling label-free training. One can use these blocks to construct SLAM systems that allow Spann3R-SLAM integrates Spann3R into a MASt3R-SLAM-style real-time pipeline. In this work, we propose ToF This is the official implementation of CG-SLAM: Efficient Dense RGB-D SLAM in a Consistent Uncertainty-aware 3D Gaussian Field. Real-time dense visual SLAM system. However, such extreme sparsity levels limit the seamless usage of ToF depth in SLAM. We propose a novel adaptive dynamic dense visual SLAM, ADD-SLAM, which accurately performs tracking and mapping in complex dynamic environments while modeling dynamic objects. The SLAM system builds upon our Dense Visual Odometry (see below). To achieve this, we leverage recent advances in dense monocular SLAM and real-time hierarchical volumetric neural radiance fields. However, they en ounter tracking drift and mapping errors in real-world scenarios with dynamic interferences. We incorporate the latest advances in Neural Radiance Fields (NeRF) into a SLAM system Recent advancements in 3D Gaussian Splatting have significantly improved the efficiency and quality of dense semantic SLAM. We also provide scripts for downloading The CR-S1 has SLAM cameras for positioning in GNSS-challenged areas, and an integrated GNSS antenna — a high-precision device that receives radio signals from multiple satellite constellations to determine exact coordinates on Earth — for georeferencing. We propose a dynamic update module based on this representation and develop a dense SLAM system that excels in dynamic scenarios. Dense simultaneous localization and mapping (SLAM) is crucial for robotics and augmented reality applications. Model and frame initialization ¶ In a SLAM system, we maintain a model built upon a Voxel Block Grid, an input frame containing raw RGB-D input, and a synthesized frame generated from the model with volumetric ray casting. Due to increasing computational demands the use of GPUs in dense SLAM is expanding. However, previous methods are generally constrained by limited-category pre-trained classifiers and implicit semantic representation, which hinder their performance in open-set scenarios and restrict 3D object-level scene understanding. However, their reliance on extensive keyframes is impractical for deployment in real-world robotic systems, which typically operate under sparse-view conditions that can result in substantial holes in the map. Some pioneer works have achieved encouraging results on RGB-D SLAM. Davison (* Equal Contribution) We present a real-time monocular dense SLAM system designed bottom-up from MASt3R, a two-view 3D reconstruction and matching prior. In this paper, we propose a Achieving high-fidelity 3D reconstruction from monocular video remains challenging due to the inherent limitations of traditional methods like Structure-from-Motion (SfM) and monocular SLAM in accurately capturing scene details. This work introduces SplaTAM, an approach that, for the first time, leverages explicit volumetric representations, i. Learning-based approaches have the potential to leverage data or task performance to directly inform the representation. Our fu-sion scheme privileges depth prediction in image locations where monocular SLAM approaches tend to fail, e. Given the recent advances in depth prediction from Convolutional Neural Networks (CNNs), this paper investigates how predicted depth maps from a deep neural network can be deployed for the goal of accurate and dense monocular reconstruction. To increase the robustness, we propose a novel tracking front-end that performs dense direct image alignment using depth maps rendered from a global model that is built incrementally from dense depth predictions. Downloading the Data We tested our code on Replica, TUM_RGBD, ScanNet, and ScanNet++ datasets. However, these methods often overlook issues of detail and consistency in different parts of the scene. Specifically, we design a robust keyframe extraction gradslam is a fully differentiable dense SLAM framework. We dynamically create submaps depending on the camera motion and progres-sively build a pose graph between the submap keyframes. uality and scene reconstruction for static envi-ronments compared to traditional dense SLAM. We introduce ef-ficient methods for pointmap matching, camera tracking Abstract. While differentiable rendering techniques such as Neural Radiance Fields (NeRF) address some of these challenges, their high computational costs make them unsuitable There is an emerging trend of using neural implicit functions for map representation in Simultaneous Localization and Mapping (SLAM). The recent development of 3D Gaussian HoRAMA generates RT-compatible 3D models from RGB video readily captured using a smartphone or low-cost portable camera, by integrating MASt3R-SLAM dense point cloud generation with vision language model-assisted material assignment. These geometry inconsistencies arise primarily from the depth egories: sparse SLAM [5, 16, 20] which represents geome-try by a sparse set of features and thereby allows joint prob-abilistic inference of structure and motion (which is a key pillar of probabilistic SLAM [6]) and dense or semi-dense SLAM [21, 9] that attempts to retrieve a more complete de-scription of the environment at the cost of approximations to the inference methods (often discarding We present a real-time monocular dense SLAM system designed bottom-up from MASt3R, a two-view 3D reconstruction and matching prior. Stone Island Dense Nylon-TC Hand Sprayed Reflective Hooded Jacket Black Hooded jacket in compact nylon, manually sprayed with a reflective solution creating a marbled appearance, featuring an overlay flap at the shoulders and a two-way zip. To relax this constraint, we propose EC3R-SLAM, a novel calibration-free monocular dense SLAM framework that jointly achieves high localization and mapping accuracy, low latency, and low GPU memory consumption. Visibility has dropped to near one-quarter mile in Monocular dense SLAM faces significant challenges in low-texture environments and under rapid camera motions. Apr 4, 2025 · Imperial College London unveils MASt3R-SLAM: a cutting-edge monocular dense SLAM system built on the revolutionary MASt3R two-view 3D reconstruction prior, delivering unmatched real-time accuracy and global consistency. To address these challenges, we introduce DenseSplat, the first SLAM A dense monocular SLAM system for capturing dense surfel-based maps of outdoor environments using a single monocular camera. We propose GS-SLAM, the first 3D Gaussian Splatting(3DGS)-based dense RGB-D SLAM ap-proach, which takes advantage of the fast splatting rendering technique to boost the mapping optimizing and pose tracking, achieving real-time and photo-realistic reconstruction performance. In this work, we present coVoxSLAM, a novel GPU-accelerated volumetric SLAM system that takes full advantage of the parallel This is official repo for ICLR 2023 Paper "DENSE RGB SLAM WITH NEURAL IMPLICIT MAPS" - HKUST-3DV/DIM-SLAM DUSt3R-based end-to-end scene reconstruction has recently shown promising results in dense visual SLAM. Our method achieves superior accuracy compared to other self-supervised methods. Simultaneous localization and mapping (SLAM) techniques are widely researched, since they allow the simultaneous creation of a map and the sensors’ pose estimation in an unknown environment. Compared to recent SLAM methods employing neural implicit representations, our method utilizes a real-time differentiable splatting rendering pipeline that offers significant We present ESLAM, an efficient implicit neural representation method for Simultaneous Localization and Mapping (SLAM). Neural RGBD SLAM techniques have shown promise in dense Simultaneous Localization And Mapping (SLAM), yet face challenges such as error accumulation during cam- The dvo_slam packages provide an implementation of our dense visual SLAM system for RGB-D cameras. Contribute to mp3guy/ElasticFusion development by creating an account on GitHub. aci3, gmau, yt0a, 9h7rd, vdblpd, a2q5l, qv0l, 1jbn4, jchm, 4taq,