# Temporally coherent 4D reconstruction of complex dynamic scenes

arXiv: https://arxiv.org/abs/1603.03381 · HTML: https://mlsanity.com/paper/1603.03381/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Mar 2016.

## Built on by (most cited first, 21 of 21)

- [MonST3R: A Simple Approach for Estimating Geometry in the Presence of Motion](https://mlsanity.com/paper/2410.03825/) (2024)
- [Function4D: Real-time Human Volumetric Capture from Very Sparse Consumer RGBD Sensors](https://mlsanity.com/paper/2105.01859/) (2021)
- [Fourier PlenOctrees for Dynamic Radiance Field Rendering in Real-time](https://mlsanity.com/paper/2202.08614/) (2022)
- [Editable Free-viewpoint Video Using a Layered Neural Representation](https://mlsanity.com/paper/2104.14786/) (2021)
- [UV Volumes for Real-time Rendering of Editable Free-view Human Performance](https://mlsanity.com/paper/2203.14402/) (2022)
- [Learning Parallel Dense Correspondence from Spatio-Temporal Descriptors for Efficient and Robust 4D Reconstruction](https://mlsanity.com/paper/2103.16341/) (2021)
- [iButter: Neural Interactive Bullet Time Generator for Human Free-viewpoint Rendering](https://mlsanity.com/paper/2108.05577/) (2021)
- [SceNeRFlow: Time-Consistent Reconstruction of General Dynamic Scenes](https://mlsanity.com/paper/2308.08258/) (2023)
- [PAGE-4D: Disentangled pose and geometry estimation for vggt-4d perception](https://mlsanity.com/paper/2510.17568/) (2025)
- [CanFields: Consolidating Diffeomorphic Flows for Non-Rigid 4D Interpolation from Arbitrary-Length Sequences](https://mlsanity.com/paper/2406.18582/) (2024)
- [RFNet-4D++: Joint Object Reconstruction and Flow Estimation from 4D Point Clouds with Cross-Attention Spatio-Temporal Features](https://mlsanity.com/paper/2203.16482/) (2022)
- [UFO-4D: Unposed Feedforward 4D Reconstruction from Two Images](https://mlsanity.com/paper/2602.24290/) (2026)
- [Temporal Consistency Loss for High Resolution Textured and Clothed 3DHuman Reconstruction from Monocular Video](https://mlsanity.com/paper/2104.09259/) (2021)
- [4DSR-GCN: 4D Video Point Cloud Upsampling using Graph Convolutional Networks](https://mlsanity.com/paper/2306.01081/) (2023)
- [Advances in 4D Generation: A Survey](https://mlsanity.com/paper/2503.14501/) (2025)
- [Disentangled 4D Gaussian Splatting: Rendering High-Resolution Dynamic World at 343 FPS](https://mlsanity.com/paper/2503.22159/) (2025)
- [Learning Multi-frame and Monocular Prior for Estimating Geometry in Dynamic Scenes](https://mlsanity.com/paper/2505.01737/) (2025)
- [Dense Dynamic Scene Reconstruction and Camera Pose Estimation from Multi-View Videos](https://mlsanity.com/paper/2603.12064/) (2026)
- [Dynamic Scene Novel View Synthesis via Deferred Spatio-temporal Consistency](https://mlsanity.com/paper/2109.01018/) (2021)
- [MoAngelo: Motion-Aware Neural Surface Reconstruction for Dynamic Scenes](https://mlsanity.com/paper/2509.15892/) (2025)
- [4D Reconstruction from Sparse Dynamic Cameras](https://mlsanity.com/paper/2606.04593/) (2026)

## Abstract (arXiv)

This paper presents an approach for reconstruction of 4D temporally coherent models of complex dynamic scenes. No prior knowledge is required of scene structure or camera calibration allowing reconstruction from multiple moving cameras. Sparse-to-dense temporal correspondence is integrated with joint multi-view segmentation and reconstruction to obtain a complete 4D representation of static and dynamic objects. Temporal coherence is exploited to overcome visual ambiguities resulting in improved reconstruction of complex scenes. Robust joint segmentation and reconstruction of dynamic objects is achieved by introducing a geodesic star convexity constraint. Comparative evaluation is performed on a variety of unstructured indoor and outdoor dynamic scenes with hand-held cameras and multiple people. This demonstrates reconstruction of complete temporally coherent 4D scene models with improved nonrigid object segmentation and shape reconstruction.