Deep PatchMatch MVS with Learned Patch Coplanarity, Geometric Consistency and Adaptive Pixel Sampling

• URL: http://arxiv.org/abs/2210.07582v1
• Date: Fri, 14 Oct 2022 07:29:03 GMT
• Title: Deep PatchMatch MVS with Learned Patch Coplanarity, Geometric Consistency and Adaptive Pixel Sampling
• Authors: Jae Yong Lee, Chuhang Zou, Derek Hoiem
• Abstract summary: We build on learning-based approaches to improve photometric scores by learning patch coplanarity and encourage geometric consistency. We propose an adaptive pixel sampling strategy for candidate propagation that reduces memory to enable training on larger resolution with more views and a larger encoder.
• Score: 19.412014102866507
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent work in multi-view stereo (MVS) combines learnable photometric scores and regularization with PatchMatch-based optimization to achieve robust pixelwise estimates of depth, normals, and visibility. However, non-learning based methods still outperform for large scenes with sparse views, in part due to use of geometric consistency constraints and ability to optimize over many views at high resolution. In this paper, we build on learning-based approaches to improve photometric scores by learning patch coplanarity and encourage geometric consistency by learning a scaled photometric cost that can be combined with reprojection error. We also propose an adaptive pixel sampling strategy for candidate propagation that reduces memory to enable training on larger resolution with more views and a larger encoder. These modifications lead to 6-15% gains in accuracy and completeness on the challenging ETH3D benchmark, resulting in higher F1 performance than the widely used state-of-the-art non-learning approaches ACMM and ACMP.

Related papers

• SD-MVS: Segmentation-Driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization [6.886220026399106]
  We introduce Multi-View Stereo (SD-MVS) to tackle challenges in 3D reconstruction of textureless areas. We are the first to adopt the Segment Anything Model (SAM) to distinguish semantic instances in scenes. We propose a unique refinement strategy that combines spherical coordinates and gradient descent on normals and pixelwise search interval on depths.
  arXiv  Detail & Related papers  (2024-01-12T05:25:57Z)
• Adaptive Image Registration: A Hybrid Approach Integrating Deep Learning and Optimization Functions for Enhanced Precision [13.242184146186974]
  We propose a single framework for image registration based on deep neural networks and optimization. We show improvements of up to 1.6% in test data, while maintaining the same inference time, and a substantial 1.0% points performance gain in deformation field smoothness.
  arXiv  Detail & Related papers  (2023-11-27T02:48:06Z)
• MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo [7.130834755320434]
  We propose a resilient and effective multi-view stereo approach (MP-MVS) We design a multi-scale windows PatchMatch (mPM) to obtain reliable depth of untextured areas. In contrast with other multi-scale approaches, which is faster and can be easily extended to PatchMatch-based MVS approaches.
  arXiv  Detail & Related papers  (2023-09-23T07:30:42Z)
• Rank-Enhanced Low-Dimensional Convolution Set for Hyperspectral Image Denoising [50.039949798156826]
  This paper tackles the challenging problem of hyperspectral (HS) image denoising. We propose rank-enhanced low-dimensional convolution set (Re-ConvSet) We then incorporate Re-ConvSet into the widely-used U-Net architecture to construct an HS image denoising method.
  arXiv  Detail & Related papers  (2022-07-09T13:35:12Z)
• PatchMVSNet: Patch-wise Unsupervised Multi-View Stereo for Weakly-Textured Surface Reconstruction [2.9896482273918434]
  This paper proposes robust loss functions leveraging constraints beneath multi-view images to alleviate matching ambiguity. Our strategy can be implemented with arbitrary depth estimation frameworks and can be trained with arbitrary large-scale MVS datasets. Our method reaches the performance of the state-of-the-art methods on popular benchmarks, like DTU, Tanks and Temples and ETH3D.
  arXiv  Detail & Related papers  (2022-03-04T07:05:23Z)
• Image-specific Convolutional Kernel Modulation for Single Image Super-resolution [85.09413241502209]
  In this issue, we propose a novel image-specific convolutional modulation kernel (IKM) We exploit the global contextual information of image or feature to generate an attention weight for adaptively modulating the convolutional kernels. Experiments on single image super-resolution show that the proposed methods achieve superior performances over state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-11-16T11:05:10Z)
• PatchMatch-RL: Deep MVS with Pixelwise Depth, Normal, and Visibility [23.427619869594437]
  We propose an end-to-end trainable PatchMatch-based MVS approach that combines advantages of trainable costs and regularizations with pixelwise estimates. We evaluate our method on widely used MVS benchmarks, ETH3D and Tanks and Temples (TnT)
  arXiv  Detail & Related papers  (2021-08-19T23:14:48Z)
• 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)
• Universal and Flexible Optical Aberration Correction Using Deep-Prior Based Deconvolution [51.274657266928315]
  We propose a PSF aware plug-and-play deep network, which takes the aberrant image and PSF map as input and produces the latent high quality version via incorporating lens-specific deep priors. Specifically, we pre-train a base model from a set of diverse lenses and then adapt it to a given lens by quickly refining the parameters.
  arXiv  Detail & Related papers  (2021-04-07T12:00:38Z)
• Depth image denoising using nuclear norm and learning graph model [107.51199787840066]
  Group-based image restoration methods are more effective in gathering the similarity among patches. For each patch, we find and group the most similar patches within a searching window. The proposed method is superior to other current state-of-the-art denoising methods in both subjective and objective criterion.
  arXiv  Detail & Related papers  (2020-08-09T15:12:16Z)
• Learning Deformable Image Registration from Optimization: Perspective, Modules, Bilevel Training and Beyond [62.730497582218284]
  We develop a new deep learning based framework to optimize a diffeomorphic model via multi-scale propagation. We conduct two groups of image registration experiments on 3D volume datasets including image-to-atlas registration on brain MRI data and image-to-image registration on liver CT data.
  arXiv  Detail & Related papers  (2020-04-30T03:23:45Z)

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.