DS-MVSNet: Unsupervised Multi-view Stereo via Depth Synthesis

• URL: http://arxiv.org/abs/2208.06674v1
• Date: Sat, 13 Aug 2022 15:25:51 GMT
• Title: DS-MVSNet: Unsupervised Multi-view Stereo via Depth Synthesis
• Authors: Jingliang Li, Zhengda Lu, Yiqun Wang, Ying Wang, Jun Xiao
• Abstract summary: In this paper, we propose the DS-MVSNet, an end-to-end unsupervised MVS structure with the source depths synthesis. To mine the information in probability volume, we creatively synthesize the source depths by splattering the probability volume and depth hypotheses to source views. On the other hand, we utilize the source depths to render the reference images and propose depth consistency loss and depth smoothness loss.
• Score: 11.346448410152844
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, supervised or unsupervised learning-based MVS methods achieved excellent performance compared with traditional methods. However, these methods only use the probability volume computed by cost volume regularization to predict reference depths and this manner cannot mine enough information from the probability volume. Furthermore, the unsupervised methods usually try to use two-step or additional inputs for training which make the procedure more complicated. In this paper, we propose the DS-MVSNet, an end-to-end unsupervised MVS structure with the source depths synthesis. To mine the information in probability volume, we creatively synthesize the source depths by splattering the probability volume and depth hypotheses to source views. Meanwhile, we propose the adaptive Gaussian sampling and improved adaptive bins sampling approach that improve the depths hypotheses accuracy. On the other hand, we utilize the source depths to render the reference images and propose depth consistency loss and depth smoothness loss. These can provide additional guidance according to photometric and geometric consistency in different views without additional inputs. Finally, we conduct a series of experiments on the DTU dataset and Tanks & Temples dataset that demonstrate the efficiency and robustness of our DS-MVSNet compared with the state-of-the-art methods.

Related papers

• Uncertainty-guided Optimal Transport in Depth Supervised Sparse-View 3D Gaussian [49.21866794516328]
  3D Gaussian splatting has demonstrated impressive performance in real-time novel view synthesis. Previous approaches have incorporated depth supervision into the training of 3D Gaussians to mitigate overfitting. We introduce a novel method to supervise the depth distribution of 3D Gaussians, utilizing depth priors with integrated uncertainty estimates.
  arXiv  Detail & Related papers  (2024-05-30T03:18:30Z)
• Learning Deformable Hypothesis Sampling for Accurate PatchMatch Multi-View Stereo [4.6332064055042865]
  This paper introduces a learnable Deformable Hypothesis Sampler (DeformSampler) to address the challenging issue of noisy depth estimation. We develop DeformSampler to learn distribution-sensitive sample spaces to propagate depths consistent with the scene's geometry. We integrate DeformSampler into a learnable PatchMatch MVS system to enhance depth estimation in challenging areas.
  arXiv  Detail & Related papers  (2023-12-26T09:36:21Z)
• Monocular Visual-Inertial Depth Estimation [66.71452943981558]
  We present a visual-inertial depth estimation pipeline that integrates monocular depth estimation and visual-inertial odometry. Our approach performs global scale and shift alignment against sparse metric depth, followed by learning-based dense alignment. We evaluate on the TartanAir and VOID datasets, observing up to 30% reduction in RMSE with dense scale alignment.
  arXiv  Detail & Related papers  (2023-03-21T18:47:34Z)
• RA-Depth: Resolution Adaptive Self-Supervised Monocular Depth Estimation [27.679479140943503]
  We propose a resolution adaptive self-supervised monocular depth estimation method (RA-Depth) by learning the scale invariance of the scene depth. RA-Depth achieves state-of-the-art performance, and also exhibits a good ability of resolution adaptation.
  arXiv  Detail & Related papers  (2022-07-25T08:49:59Z)
• Non-parametric Depth Distribution Modelling based Depth Inference for Multi-view Stereo [43.415242967722804]
  Recent cost volume pyramid based deep neural networks have unlocked the potential of efficiently leveraging high-resolution images for depth inference from multi-view stereo. In general, those approaches assume that the depth of each pixel follows a unimodal distribution. We propose constructing the cost volume by non-parametric depth distribution modeling to handle pixels with unimodal and multi-modal distributions.
  arXiv  Detail & Related papers  (2022-05-08T05:13:04Z)
• Improving Monocular Visual Odometry Using Learned Depth [84.05081552443693]
  We propose a framework to exploit monocular depth estimation for improving visual odometry (VO) The core of our framework is a monocular depth estimation module with a strong generalization capability for diverse scenes. Compared with current learning-based VO methods, our method demonstrates a stronger generalization ability to diverse scenes.
  arXiv  Detail & Related papers  (2022-04-04T06:26:46Z)
• DDL-MVS: Depth Discontinuity Learning for MVS Networks [0.5735035463793007]
  We propose depth discontinuity learning for MVS methods, which further improves accuracy while retaining the completeness of the reconstruction. We validate our idea and demonstrate that our strategies can be easily integrated into the existing learning-based MVS pipeline.
  arXiv  Detail & Related papers  (2022-03-02T20:25:31Z)
• Uncertainty-Aware Deep Multi-View Photometric Stereo [100.97116470055273]
  Photometric stereo (PS) is excellent at recovering high-frequency surface details, whereas multi-view stereo (MVS) can help remove the low-frequency distortion due to PS and retain the global shape. This paper proposes an approach that can effectively utilize such complementary strengths of PS and MVS. We estimate per-pixel surface normals and depth using an uncertainty-aware deep-PS network and deep-MVS network, respectively.
  arXiv  Detail & Related papers  (2022-02-26T05:45:52Z)
• A Confidence-based Iterative Solver of Depths and Surface Normals for Deep Multi-view Stereo [41.527018997251744]
  We introduce a deep multi-view stereo (MVS) system that jointly predicts depths, surface normals and per-view confidence maps. The key to our approach is a novel solver that iteratively solves for per-view depth map and normal map. Our proposed solver consistently improves the depth quality over both conventional and deep learning based MVS pipelines.
  arXiv  Detail & Related papers  (2022-01-19T14:08:45Z)
• 3DVNet: Multi-View Depth Prediction and Volumetric Refinement [68.68537312256144]
  3DVNet is a novel multi-view stereo (MVS) depth-prediction method. Our key idea is the use of a 3D scene-modeling network that iteratively updates a set of coarse depth predictions. We show that our method exceeds state-of-the-art accuracy in both depth prediction and 3D reconstruction metrics.
  arXiv  Detail & Related papers  (2021-12-01T00:52:42Z)
• An Adaptive Framework for Learning Unsupervised Depth Completion [59.17364202590475]
  We present a method to infer a dense depth map from a color image and associated sparse depth measurements. We show that regularization and co-visibility are related via the fitness of the model to data and can be unified into a single framework.
  arXiv  Detail & Related papers  (2021-06-06T02:27:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.