# Dense Hybrid Recurrent Multi-view Stereo Net with Dynamic Consistency Checking

arXiv: https://arxiv.org/abs/2007.10872 · HTML: https://mlsanity.com/paper/2007.10872/

## Key facts

- Cited by 81 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jul 2020.

## Built on by (most cited first, 30 of 81)

- [FLARE: Feed-forward Geometry, Appearance and Camera Estimation from Uncalibrated Sparse Views](https://mlsanity.com/paper/2502.12138/) (2025)
- [NeRO: Neural Geometry and BRDF Reconstruction of Reflective Objects from Multiview Images](https://mlsanity.com/paper/2305.17398/) (2023)
- [TransMVSNet: Global Context-aware Multi-view Stereo Network with Transformers](https://mlsanity.com/paper/2111.14600/) (2021)
- [Rethinking Depth Estimation for Multi-View Stereo: A Unified Representation](https://mlsanity.com/paper/2201.01501/) (2022)
- [AA-RMVSNet: Adaptive Aggregation Recurrent Multi-view Stereo Network](https://mlsanity.com/paper/2108.03824/) (2021)
- [GS2Mesh: Surface Reconstruction from Gaussian Splatting via Novel Stereo Views](https://mlsanity.com/paper/2404.01810/) (2024)
- [MIME: Human-Aware 3D Scene Generation](https://mlsanity.com/paper/2212.04360/) (2022)
- [IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo](https://mlsanity.com/paper/2112.05126/) (2021)
- [MVSFormer: Multi-View Stereo by Learning Robust Image Features and Temperature-based Depth](https://mlsanity.com/paper/2208.02541/) (2022)
- [Multiview Stereo with Cascaded Epipolar RAFT](https://mlsanity.com/paper/2205.04502/) (2022)
- [MVSTER: Epipolar Transformer for Efficient Multi-View Stereo](https://mlsanity.com/paper/2204.07346/) (2022)
- [MVSFormer++: Revealing the Devil in Transformer's Details for Multi-View Stereo](https://mlsanity.com/paper/2401.11673/) (2024)
- [Generalized Binary Search Network for Highly-Efficient Multi-View Stereo](https://mlsanity.com/paper/2112.02338/) (2021)
- [RC-MVSNet: Unsupervised Multi-View Stereo with Neural Rendering](https://mlsanity.com/paper/2203.03949/) (2022)
- [Differentiable Stereopsis: Meshes from multiple views using differentiable rendering](https://mlsanity.com/paper/2110.05472/) (2021)
- [S-VolSDF: Sparse Multi-View Stereo Regularization of Neural Implicit Surfaces](https://mlsanity.com/paper/2303.17712/) (2023)
- [View Synthesis with Sculpted Neural Points](https://mlsanity.com/paper/2205.05869/) (2022)
- [WT-MVSNet: Window-based Transformers for Multi-view Stereo](https://mlsanity.com/paper/2205.14319/) (2022)
- [SD-MVS: Segmentation-Driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization](https://mlsanity.com/paper/2401.06385/) (2024)
- [MonSter++: Unified Stereo Matching, Multi-view Stereo, and Real-time Stereo with Monodepth Priors](https://mlsanity.com/paper/2501.08643/) (2025)
- [KD-MVS: Knowledge Distillation Based Self-supervised Learning for Multi-view Stereo](https://mlsanity.com/paper/2207.10425/) (2022)
- [Learning-based Multi-View Stereo: A Survey](https://mlsanity.com/paper/2408.15235/) (2024)
- [VRP-UDF: Towards Unbiased Learning of Unsigned Distance Functions from Multi-view Images with Volume Rendering Priors](https://mlsanity.com/paper/2407.16396/) (2024)
- [MSP-MVS: Multi-Granularity Segmentation Prior Guided Multi-View Stereo](https://mlsanity.com/paper/2407.19323/) (2024)
- [Deep Learning for Multi-View Stereo via Plane Sweep: A Survey](https://mlsanity.com/paper/2106.15328/) (2021)
- [Non-parametric Depth Distribution Modelling based Depth Inference for Multi-view Stereo](https://mlsanity.com/paper/2205.03783/) (2022)
- [RayMVSNet++: Learning Ray-based 1D Implicit Fields for Accurate Multi-View Stereo](https://mlsanity.com/paper/2307.10233/) (2023)
- [Self-Supervised Visibility Learning for Novel View Synthesis](https://mlsanity.com/paper/2103.15407/) (2021)
- [Prior-Guided Multi-View 3D Head Reconstruction](https://mlsanity.com/paper/2107.04277/) (2021)
- [Detailed Facial Geometry Recovery from Multi-View Images by Learning an Implicit Function](https://mlsanity.com/paper/2201.01016/) (2022)

## Abstract (arXiv)

In this paper, we propose an efficient and effective dense hybrid recurrent multi-view stereo net with dynamic consistency checking, namely D2HC-RMVSNet, for accurate dense point cloud reconstruction. Our novel hybrid recurrent multi-view stereo net consists of two core modules: 1) a light DRENet (Dense Reception Expanded) module to extract dense feature maps of original size with multi-scale context information, 2) a HU-LSTM (Hybrid U-LSTM) to regularize 3D matching volume into predicted depth map, which efficiently aggregates different scale information by coupling LSTM and U-Net architecture. To further improve the accuracy and completeness of reconstructed point clouds, we leverage a dynamic consistency checking strategy instead of prefixed parameters and strategies widely adopted in existing methods for dense point cloud reconstruction. In doing so, we dynamically aggregate geometric consistency matching error among all the views. Our method ranks textbf1st on the complex outdoor textslTanks and Temples benchmark over all the methods. Extensive experiments on the in-door DTU dataset show our method exhibits competitive performance to the state-of-the-art method while dramatically reduces memory consumption, which costs only 19.4% of R-MVSNet memory consumption. The codebase is available at hyperlinkhttps://github.com/yhw-yhw/D2HC-RMVSNethttps://github.com/yhw-yhw/D2HC-RMVSNet.