Continuous 3D Perception Model with Persistent State
Not on the map. Why?
Cited by 34 in Multimodal LLMs / vision-language models, but placed by its own abstract, which didn’t fit.
Abstract (arXiv)
We present a unified framework capable of solving a broad range of 3D tasks. Our approach features a stateful recurrent model that continuously updates its state representation with each new observation. Given a stream of images, this evolving state can be used to generate metric-scale pointmaps (per-pixel 3D points) for each new input in an online fashion. These pointmaps reside within a common coordinate system, and can be accumulated into a coherent, dense scene reconstruction that updates as new images arrive. Our model, called CUT3R (Continuous Updating Transformer for 3D Reconstruction), captures rich priors of real-world scenes: not only can it predict accurate pointmaps from image observations, but it can also infer unseen regions of the scene by probing at virtual, unobserved views. Our method is simple yet highly flexible, naturally accepting varying lengths of images that may be either video streams or unordered photo collections, containing both static and dynamic content. We evaluate our method on various 3D/4D tasks and demonstrate competitive or state-of-the-art performance in each. Project Page: https://cut3r.github.io/
Key facts. Cited by 548 papers in the map (data through 14 Oct 2026). Builds on 75 of its 106 references that are in the map. First posted on arXiv 21 Jan 2025.
Builds on
75 of 106 references.
References · 75
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
- 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
- 2021Masked Autoencoders Are Scalable Vision Learnerscited by 7,403
- 2022Photorealistic Text-to-Image Diffusion Models with Deep Language Understandingcited by 6,355
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- 2021Zero-Shot Text-to-Image Generationcited by 4,288
- 2022Instant Neural Graphics Primitives with a Multiresolution Hash Encodingcited by 3,723
- 2017ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenescited by 3,117
- 2022DreamFusion: Text-to-3D using 2D Diffusioncited by 3,025
- 2019Scalability in Perception for Autonomous Driving: Waymo Open Datasetcited by 2,451
- 2021Vision Transformers for Dense Predictioncited by 1,803
38 more
- 2021NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstructioncited by 1,712
- 2021Plenoxels: Radiance Fields without Neural Networkscited by 1,613
- 2020pixelNeRF: Neural Radiance Fields from One or Few Imagescited by 1,584
- 2023DUSt3R: Geometric 3D Vision Made Easycited by 1,567
- 2019Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfercited by 1,495
- 2023Zero-1-to-3: Zero-shot One Image to 3D Objectcited by 1,489
- 2019SuperGlue: Learning Feature Matching with Graph Neural Networkscited by 1,443
- 2024Depth Anything: Unleashing the Power of Large-Scale Unlabeled Datacited by 1,360
- 2017SuperPoint: Self-Supervised Interest Point Detection and Descriptioncited by 1,320
- 2022TensoRF: Tensorial Radiance Fieldscited by 1,280
- 2021Perceiver: General Perception with Iterative Attentioncited by 1,104
- 2015ORB-SLAM: a Versatile and Accurate Monocular SLAM Systemcited by 988
- 2021LoFTR: Detector-Free Local Feature Matching with Transformerscited by 984
- 2024Grounding Image Matching in 3D with MASt3Rcited by 983
- 2019Scene Representation Networks: Continuous 3D-Structure-Aware Neural Scene Representationscited by 864
- 2018MVSNet: Depth Inference for Unstructured Multi-view Stereocited by 788
- 2018MegaDepth: Learning Single-View Depth Prediction from Internet Photoscited by 737
- 2021DROID-SLAM: Deep Visual SLAM for Monocular, Stereo, and RGB-D Camerascited by 687
- 2023Mip-Splatting: Alias-free 3D Gaussian Splattingcited by 667
- 2023ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depthcited by 657
- 2021Common Objects in 3D: Large-Scale Learning and Evaluation of Real-life 3D Category Reconstructioncited by 652
- 2021NICE-SLAM: Neural Implicit Scalable Encoding for SLAMcited by 589
- 20163D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstructioncited by 588
- 2023DL3DV-10K: A Large-Scale Scene Dataset for Deep Learning-based 3D Visioncited by 537
- 2020TartanAir: A Dataset to Push the Limits of Visual SLAMcited by 506
- 2024MonST3R: A Simple Approach for Estimating Geometry in the Presence of Motioncited by 442
- 2024CAT3D: Create Anything in 3D with Multi-View Diffusion Modelscited by 432
- 2016Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Agecited by 411
- 2024Depth Pro: Sharp Monocular Metric Depth in Less Than a Secondcited by 404
- 2024Metric3Dv2: A Versatile Monocular Geometric Foundation Model for Zero-shot Metric Depth and Surface Normal Estimationcited by 395
- 2024UniDepth: Universal Monocular Metric Depth Estimationcited by 382
- 2021Light Field Networks: Neural Scene Representations with Single-Evaluation Renderingcited by 332
- 2019D2-Net: A Trainable CNN for Joint Detection and Description of Local Featurescited by 326
- 2023ReconFusion: 3D Reconstruction with Diffusion Priorscited by 318
- 2021Neural RGB-D Surface Reconstructioncited by 313
- 2023RealFusion: 360deg Reconstruction of Any Object from a Single Imagecited by 306
- 2024MoGe: Unlocking Accurate Monocular Geometry Estimation for Open-Domain Images with Optimal Training Supervisioncited by 299
- 20243D Reconstruction with Spatial Memorycited by 277
50 most cited of 75 All 75 (JSON)
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Citing papers per year: 2025: 203; 2026: 345.
548 papers.
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50 most cited of 548 All 548 (JSON)
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