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SLAM3R: Real-Time Dense Scene Reconstruction from Monocular RGB Videos

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arxiv 2412.09401 v3 pith:3QEDOQZ3 submitted 2024-12-12 cs.CV

Yuzheng Liu , Siyan Dong , Shuzhe Wang , Yingda Yin , Yanchao Yang , Qingnan Fan , Baoquan Chen This is my paper
classification cs.CV
keywords slam3rreconstructionreal-timedenselocalpointmapsscenesystem
verification ladder T0 review T1 audit T2 compute T3 formal
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In this paper, we introduce SLAM3R, a novel and effective system for real-time, high-quality, dense 3D reconstruction using RGB videos. SLAM3R provides an end-to-end solution by seamlessly integrating local 3D reconstruction and global coordinate registration through feed-forward neural networks. Given an input video, the system first converts it into overlapping clips using a sliding window mechanism. Unlike traditional pose optimization-based methods, SLAM3R directly regresses 3D pointmaps from RGB images in each window and progressively aligns and deforms these local pointmaps to create a globally consistent scene reconstruction - all without explicitly solving any camera parameters. Experiments across datasets consistently show that SLAM3R achieves state-of-the-art reconstruction accuracy and completeness while maintaining real-time performance at 20+ FPS. Code available at: https://github.com/PKU-VCL-3DV/SLAM3R.

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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. Rig3R: Rig-Aware Conditioning for Learned 3D Reconstruction

    cs.CV 2025-06 conditional novelty 7.0 of 10

    Rig3R conditions learned 3D reconstruction on optional rig metadata and predicts rig-relative raymaps, enabling state-of-the-art pose estimation and rig calibration discovery from images.

  2. STream3R: Scalable Sequential 3D Reconstruction with Causal Transformer

    cs.CV 2025-08 conditional novelty 6.0 of 10

    A decoder-only Transformer with causal attention and cached past-frame features performs incremental 3D reconstruction from streaming images, beating the RNN-based CUT3R on several benchmark metrics.

  3. 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.

  4. Test3R: Learning to Reconstruct 3D at Test Time

    cs.CV 2025-06 conditional novelty 6.0 of 10

    Test3R improves 3D reconstruction by optimizing visual prompts at test time so that pointmaps from different image pairs are geometrically consistent.

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