# 4RC: 4D Reconstruction via Conditional Querying Anytime and Anywhere

arXiv: https://arxiv.org/abs/2602.10094 · HTML: https://mlsanity.com/paper/2602.10094/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 55 of its 74 references that are in the map.
- First posted on arXiv 10 Feb 2026.

## Builds on (most cited first, 30 of 55)

- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes](https://mlsanity.com/paper/1702.04405/) (2017)
- [Scalability in Perception for Autonomous Driving: Waymo Open Dataset](https://mlsanity.com/paper/1912.04838/) (2019)
- [FiLM: Visual Reasoning with a General Conditioning Layer](https://mlsanity.com/paper/1709.07871/) (2017)
- [DUSt3R: Geometric 3D Vision Made Easy](https://mlsanity.com/paper/2312.14132/) (2023)
- [VGGT: Visual Geometry Grounded Transformer](https://mlsanity.com/paper/2503.11651/) (2025)
- [Depth Anything V2](https://mlsanity.com/paper/2406.09414/) (2024)
- [Perceiver: General Perception with Iterative Attention](https://mlsanity.com/paper/2103.03206/) (2021)
- [Grounding Image Matching in 3D with MASt3R](https://mlsanity.com/paper/2406.09756/) (2024)
- [PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume](https://mlsanity.com/paper/1709.02371/) (2017)
- [ScanNet++: A High-Fidelity Dataset of 3D Indoor Scenes](https://mlsanity.com/paper/2308.11417/) (2023)
- [Depth Anything 3: Recovering the Visual Space from Any Views](https://mlsanity.com/paper/2511.10647/) (2025)
- [DROID-SLAM: Deep Visual SLAM for Monocular, Stereo, and RGB-D Cameras](https://mlsanity.com/paper/2108.10869/) (2021)
- [Continuous 3D Perception Model with Persistent State](https://mlsanity.com/paper/2501.12387/) (2025)
- [DL3DV-10K: A Large-Scale Scene Dataset for Deep Learning-based 3D Vision](https://mlsanity.com/paper/2312.16256/) (2023)
- [MonST3R: A Simple Approach for Estimating Geometry in the Presence of Motion](https://mlsanity.com/paper/2410.03825/) (2024)
- [Kubric: A scalable dataset generator](https://mlsanity.com/paper/2203.03570/) (2022)
- [Fast3R: Towards 3D Reconstruction of 1000+ Images in One Forward Pass](https://mlsanity.com/paper/2501.13928/) (2025)
- [Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation](https://mlsanity.com/paper/2312.02145/) (2023)
- [π3: Permutation-Equivariant Visual Geometry Learning](https://mlsanity.com/paper/2507.13347/) (2025)
- [Neural RGB-D Surface Reconstruction](https://mlsanity.com/paper/2104.04532/) (2021)
- [TAPIR: Tracking Any Point with per-frame Initialization and temporal Refinement](https://mlsanity.com/paper/2306.08637/) (2023)
- [DeepMVS: Learning Multi-view Stereopsis](https://mlsanity.com/paper/1804.00650/) (2018)
- [3D Reconstruction with Spatial Memory](https://mlsanity.com/paper/2408.16061/) (2024)
- [PointOdyssey: A Large-Scale Synthetic Dataset for Long-Term Point Tracking](https://mlsanity.com/paper/2307.15055/) (2023)
- [TAP-Vid: A Benchmark for Tracking Any Point in a Video](https://mlsanity.com/paper/2211.03726/) (2022)
- [Recurrent MVSNet for High-resolution Multi-view Stereo Depth Inference](https://mlsanity.com/paper/1902.10556/) (2019)
- [Shape of Motion: 4D Reconstruction from a Single Video](https://mlsanity.com/paper/2407.13764/) (2024)
- [Particle Video Revisited: Tracking Through Occlusions Using Point Trajectories](https://mlsanity.com/paper/2204.04153/) (2022)
- [DepthCrafter: Generating Consistent Long Depth Sequences for Open-world Videos](https://mlsanity.com/paper/2409.02095/) (2024)

## Built on by (most cited first, 22 of 22)

- [Track4World: Feedforward World-centric Dense 3D Tracking of All Pixels](https://mlsanity.com/paper/2603.02573/) (2026)
- [Feed-Forward 3D Scene Modeling: A Problem-Driven Perspective](https://mlsanity.com/paper/2604.14025/) (2026)
- [D'ej`a View: Looping Transformers for Multi-View 3D Reconstruction](https://mlsanity.com/paper/2605.30215/) (2026)
- [SM4RT: Learning Structured Motion Geometry for 4D Reconstruction](https://mlsanity.com/paper/2607.22534/) (2026)
- [Syn4D: A Multiview Synthetic 4D Dataset](https://mlsanity.com/paper/2605.05207/) (2026)
- [OmniX: Any-view and Any-time 4D Reconstruction via Feed-forward Trajectory Fields](https://mlsanity.com/paper/2607.10840/) (2026)
- [Instant NuRec: Feed-Forward 3D Gaussian Reconstruction for Driving Scene Simulation](https://mlsanity.com/paper/2607.14203/) (2026)
- [Lift4D: Harmonizing Single-View 3D Estimation for 4D Reconstruction In-the-Wild](https://mlsanity.com/paper/2606.23688/) (2026)
- [Fast 4D Mesh Generation by Spatio-Temporal Attention Chains](https://mlsanity.com/paper/2605.19786/) (2026)
- [UniQuery4R: Unified 4D Scene Reconstruction from a Single Query](https://mlsanity.com/paper/2608.17283/) (2026)
- [TAPVid-MV: A Benchmark for Tracking Any Point in 3D Across Multiple Views](https://mlsanity.com/paper/2609.01899/) (2026)
- [Point4D: Long-range 4D Motion Reconstruction](https://mlsanity.com/paper/2609.09145/) (2026)
- [Self-Supervised Learning of Structured Dynamics from Videos](https://mlsanity.com/paper/2607.21576/) (2026)
- [Beyond Pixels: From Video Priors to 4D Worlds](https://mlsanity.com/paper/2608.10744/) (2026)
- [USR-Drive: Unified Driving Scene Representation via Joint Denoising of 3D Gaussians and Boxes](https://mlsanity.com/paper/2608.19036/) (2026)
- [Gallileo-4D: Frozen Backbone Ensemble for Dynamic 4D Reconstruction](https://mlsanity.com/paper/2608.19743/) (2026)
- [GAUGE: Group-Wise View-Inconsistency Rectification for Feed-Forward 4D Tracking](https://mlsanity.com/paper/2609.32596/) (2026)
- [ARROW: Arbitrary Reconstruction and Tracking of 4D Observations in the Wild](https://mlsanity.com/paper/2610.01314/) (2026)
- [SCOPE-4D: Endoscopic 4D Geometry Foundation Models](https://mlsanity.com/paper/2610.02343/) (2026)
- [MoSE3: Learning World-Space SE(3) at Every Pixel](https://mlsanity.com/paper/2610.03716/) (2026)
- [UniMetric: Universal Metric Scale Recovery for Visual Geometry Foundation Models](https://mlsanity.com/paper/2610.16945/) (2026)
- [4LC: 4D Latent Cache for Efficient Conditioning in Dynamic World Modeling](https://mlsanity.com/paper/2610.17625/) (2026)

## Abstract (arXiv)

We present 4RC, a unified feed-forward framework for 4D reconstruction from monocular videos. Unlike existing approaches that typically decouple motion from geometry or produce limited 4D attributes such as sparse trajectories or two-view scene flow, 4RC learns a holistic 4D representation that jointly captures dense scene geometry and motion dynamics. At its core, 4RC introduces a novel encode-once, query-anywhere and anytime paradigm: a transformer backbone encodes the entire video into a compact spatio-temporal latent space, from which a conditional decoder can efficiently query 3D geometry and motion for any query frame at any target timestamp. To facilitate learning, we represent per-view 4D attributes in a minimally factorized form by decomposing them into base geometry and time-dependent relative motion. Extensive experiments demonstrate that 4RC outperforms prior and concurrent methods across a wide range of 4D reconstruction tasks.