mlsanityData to 7 Oct 2026

Multi-View Guided Multi-View Stereo

arXiv 2210.11467 ↗First posted 20 Oct 2022cs.CVcited by 5

Not on the map. Why?

Abstract (arXiv)

This paper introduces a novel deep framework for dense 3D reconstruction from multiple image frames, leveraging a sparse set of depth measurements gathered jointly with image acquisition. Given a deep multi-view stereo network, our framework uses sparse depth hints to guide the neural network by modulating the plane-sweep cost volume built during the forward step, enabling us to infer constantly much more accurate depth maps. Moreover, since multiple viewpoints can provide additional depth measurements, we propose a multi-view guidance strategy that increases the density of the sparse points used to guide the network, thus leading to even more accurate results. We evaluate our Multi-View Guided framework within a variety of state-of-the-art deep multi-view stereo networks, demonstrating its effectiveness at improving the results achieved by each of them on BlendedMVG and DTU datasets.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 25 of its 46 references that are in the map. First posted on arXiv 20 Oct 2022.

Builds on

25 of 46 references.

References · 25

  1. 2017Arbitrary Style Transfer in Real-time with Adaptive Instance Normalizationcited by 1,998
  2. 2019Semantic Image Synthesis with Spatially-Adaptive Normalizationcited by 1,295
  3. 2015A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimationcited by 878
  4. 2018MVSNet: Depth Inference for Unstructured Multi-view Stereocited by 780
  5. 2019BlendedMVS: A Large-scale Dataset for Generalized Multi-view Stereo Networkscited by 487
  6. 2019Cascade Cost Volume for High-Resolution Multi-View Stereo and Stereo Matchingcited by 414
  7. 2017End-to-End Learning of Geometry and Context for Deep Stereo Regressioncited by 359
  8. 2017Sparsity Invariant CNNscited by 325
  9. 2019Recurrent MVSNet for High-resolution Multi-view Stereo Depth Inferencecited by 252
  10. 2019GA-Net: Guided Aggregation Net for End-to-end Stereo Matchingcited by 242
  11. 2020PatchmatchNet: Learned Multi-View Patchmatch Stereocited by 204
  12. 2017Modulating early visual processing by languagecited by 167
13 more
  1. 2017Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Imagecited by 159
  2. 2019Cost Volume Pyramid Based Depth Inference for Multi-View Stereocited by 154
  3. 2015Stereo Matching by Training a Convolutional Neural Network to Compare Image Patchescited by 151
  4. 2019Deep Stereo using Adaptive Thin Volume Representation with Uncertainty Awarenesscited by 151
  5. 2021PENet: Towards Precise and Efficient Image Guided Depth Completioncited by 146
  6. 2019Learning Guided Convolutional Network for Depth Completioncited by 111
  7. 2020Dense Hybrid Recurrent Multi-view Stereo Net with Dynamic Consistency Checkingcited by 81
  8. 2021AA-RMVSNet: Adaptive Aggregation Recurrent Multi-view Stereo Networkcited by 71
  9. 2019Noise-Aware Unsupervised Deep Lidar-Stereo Fusioncited by 31
  10. 2019Guided Stereo Matchingcited by 24
  11. 2021A Surface Geometry Model for LiDAR Depth Completioncited by 18
  12. 20193D LiDAR and Stereo Fusion using Stereo Matching Network with Conditional Cost Volume Normalizationcited by 17
  13. 2021Sensor-Guided Optical Flowcited by 4

Built on by

Cited by year

Citing papers per year: 2023: 1; 2024: 4.

5 papers.

  1. 2024DoubleTake: Geometry Guided Depth Estimationcited by 9
  2. 2023SimpleMapping: Real-Time Visual-Inertial Dense Mapping with Deep Multi-View Stereocited by 4
  3. 2024Range-Agnostic Multi-View Depth Estimation With Keyframe Selectioncited by 4
  4. 2024Depth on Demand: Streaming Dense Depth from a Low Frame Rate Active Sensorcited by 2
  5. 2024VHS: High-Resolution Iterative Stereo Matching with Visual Hull Priorscited by 1