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MASt3R-SLAM: Real-Time Dense SLAM with 3D Reconstruction Priors

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arxiv 2412.12392 v2 pith:ZCC5MGQQ submitted 2024-12-16 cs.CV cs.RO

Riku Murai , Eric Dexheimer , Andrew J. Davison This is my paper
classification cs.CVcs.RO
keywords systemcameradenseslammatchingmonocularpriorreal-time
verification ladder T0 review T1 audit T2 compute T3 formal
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We present a real-time monocular dense SLAM system designed bottom-up from MASt3R, a two-view 3D reconstruction and matching prior. 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. We introduce efficient methods for pointmap matching, camera tracking and local fusion, graph construction and loop closure, and second-order global optimisation. With known calibration, a simple modification to the system achieves state-of-the-art performance across various benchmarks. Altogether, we propose a plug-and-play monocular SLAM system capable of producing globally-consistent poses and dense geometry while operating at 15 FPS.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Puzzles: Unbounded Video-Depth Augmentation for Scalable End-to-End 3D Reconstruction

    cs.CV 2025-06 conditional novelty 6.0 of 10

    Puzzles synthesizes posed video-depth clips from single images and keyframes, letting 3D reconstruction models match full-data accuracy using only 10% of the data.

  2. GLidE-SLAM: GL-Accelerated Indirect-Direct Embedded SLAM

    cs.RO 2026-07 conditional novelty 5.0 of 10

    GLidE-SLAM moves pose-only photometric tracking to OpenGL ES compute shaders, reporting up to 9x faster frame rates than ORB-SLAM2 on embedded platforms with comparable ATE on TUM and EuRoC sequences.

  3. Unifying Scale-Aware Depth Prediction and Perceptual Priors for Monocular Endoscope Pose Estimation and Tissue Reconstruction

    cs.CV 2025-08 conditional novelty 4.0 of 10

    A monocular endoscopy framework fuses Depth Pro and Depth Anything depth with RAFT-LPIPS temporal refinement and dog-leg pose optimization to reconstruct tissue surfaces and camera trajectories.

  4. Reconstructing 4D Spatial Intelligence: A Survey

    cs.CV 2025-07 accept novelty 4.0 of 10

    A review that classifies 4D scene reconstruction methods into five progressive levels: low-level cues, scene components, dynamic scenes, interactions, and physics.

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