Almost all previous deep learning-based multi-view stereo (MVS) approaches focus on improving reconstruction quality. Besides quality, efficiency is also a desirable feature for MVS in real scenarios. Towards this end, this paper presents a Fast-MVSNet, a novel sparse-to-dense coarse-to-fine framework, for fast and accurate depth estimation in MVS. Specifically, in our Fast-MVSNet, we first construct a sparse cost volume for learning a sparse and high-resolution depth map. Then we leverage a small-scale convolutional neural network to encode the depth dependencies for pixels within a local region to densify the sparse high-resolution depth map. At last, a simple but efficient Gauss-Newton layer is proposed to further optimize the depth map. On one hand, the high-resolution depth map, the data-adaptive propagation method and the Gauss-Newton layer jointly guarantee the effectiveness of our method. On the other hand, all modules in our Fast-MVSNet are lightweight and thus guarantee the efficiency of our approach. Besides, our approach is also memory-friendly because of the sparse depth representation. Extensive experimental results show that our method is 5$\times$ and 14$\times$ faster than Point-MVSNet and R-MVSNet, respectively, while achieving comparable or even better results on the challenging Tanks and Temples dataset as well as the DTU dataset. Code is available at https://github.com/svip-lab/FastMVSNet.
n this paper, we propose an effective and efficient pyramid multi-view stereo (MVS) net with self-adaptive view aggregation for accurate and complete dense point cloud reconstruction. Different from using mean square variance to generate cost volume in previous deep-learning based MVS methods, our \textbf{VA-MVSNet} incorporates the cost variances in different views with small extra memory consumption by introducing two novel self-adaptive view aggregations: pixel-wise view aggregation and voxel-wise view aggregation. To further boost the robustness and completeness of 3D point cloud reconstru…
ARAI-MVSNet: A multi-view stereo depth estimation network with adaptive depth range and depth interval
Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）；Institute of Electronics, Chinese Academy of Sciences（中国科学院电子学研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Beijing University of Posts and Telecommunications（北京邮电大学）
CL-MVSNet: Unsupervised Multi-view Stereo with Dual-level Contrastive Learning
School of Electronic and Computer Engineering, Peking University（北京大学电子与计算机工程学院）；Peng Cheng Laboratory（鹏城实验室）；Migu Culture Technology Co., Ltd（咪咕文化科技有限公司）；School of Computer Science, University of Birmingham（伯明翰大学计算机学院）；School of Arts, Peking University（北京大学艺术学院）
Technical University of Munich（慕尼黑工业大学）；Artisense；Karlsruhe University of Applied Sciences（卡尔斯鲁厄应用科学大学）
In this paper, we present TANDEM a real-time monocular tracking and dense mapping framework. For pose estimation, TANDEM performs photometric bundle adjustment based on a sliding window of keyframes. To increase the robustness, we propose a novel tracking front-end that performs dense direct image alignment using depth maps rendered from a global model that is built incrementally from dense depth predictions. To predict the dense depth maps, we propose Cascade View-Aggregation MVSNet (CVA-MVSNet) that utilizes the entire active keyframe window by hierarchically constructing 3D cost volumes wit…
The Hong Kong University of Science and Technology（香港科技大学）；Shenzhen Zhuke Innovation Technology（深圳珠科创新科技）
Deep learning has recently demonstrated its excellent performance for multi-view stereo (MVS). However, one major limitation of current learned MVS approaches is the scalability: the memory-consuming cost volume regularization makes the learned MVS hard to be applied to high-resolution scenes. In this paper, we introduce a scalable multi-view stereo framework based on the recurrent neural network. Instead of regularizing the entire 3D cost volume in one go, the proposed Recurrent Multi-view Stereo Network (R-MVSNet) sequentially regularizes the 2D cost maps along the depth direction via the ga…
Skolkovo Institute of Science and Technology（斯科尔科沃科学技术学院）
We study the effects of the additional input to deep multi-view stereo methods in the form of low-quality sensor depth. We modify two state-of-the-art deep multi-view stereo methods for using with the input depth. We show that the additional input depth may improve the quality of deep multi-view stereo.
Technical University of Munich（慕尼黑工业大学）；École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）；Hong Kong University of Science and Technology（香港科技大学）
Finding accurate correspondences among different views is the Achilles' heel of unsupervised Multi-View Stereo (MVS). Existing methods are built upon the assumption that corresponding pixels share similar photometric features. However, multi-view images in real scenarios observe non-Lambertian surfaces and experience occlusions. In this work, we propose a novel approach with neural rendering (RC-MVSNet) to solve such ambiguity issues of correspondences among views. Specifically, we impose a depth rendering consistency loss to constrain the geometry features close to the object surface to allev…
Australian National University（澳大利亚国立大学）；NVIDIA（英伟达）
Recent supervised multi-view depth estimation networks have achieved promising results. Similar to all supervised approaches, these networks require ground-truth data during training. However, collecting a large amount of multi-view depth data is very challenging. Here, we propose a self-supervised learning framework for multi-view stereo that exploit pseudo labels from the input data. We start by learning to estimate depth maps as initial pseudo labels under an unsupervised learning framework relying on image reconstruction loss as supervision. We then refine the initial pseudo labels using a…
The Hong Kong University of Science and Technology（香港科技大学）；Shenzhen Zhuke Innovation Technology (Altizure)（深圳珠科创新科技（Altizure））
We present an end-to-end deep learning architecture for depth map inference from multi-view images. In the network, we first extract deep visual image features, and then build the 3D cost volume upon the reference camera frustum via the differentiable homography warping. Next, we apply 3D convolutions to regularize and regress the initial depth map, which is then refined with the reference image to generate the final output. Our framework flexibly adapts arbitrary N-view inputs using a variance-based cost metric that maps multiple features into one cost feature. The proposed MVSNet is demonstr…
Tsinghua University（清华大学）；Huawei Technologies（华为技术有限公司）；Bar-Ilan University（巴伊兰大学）；Research Institute of Tsinghua, Pearl River Delta（清华大学珠江三角洲研究院）
Recently, Transformers were shown to enhance the performance of multi-view stereo by enabling long-range feature interaction. In this work, we propose Window-based Transformers (WT) for local feature matching and global feature aggregation in multi-view stereo. We introduce a Window-based Epipolar Transformer (WET) which reduces matching redundancy by using epipolar constraints. Since point-to-line matching is sensitive to erroneous camera pose and calibration, we match windows near the epipolar lines. A second Shifted WT is employed for aggregating global information within cost volume. We pr…
Tsinghua University（清华大学）；University of California, San Diego（加州大学圣地亚哥分校）；The Hong Kong University of Science and Technology（香港科技大学）
We introduce Point-MVSNet, a novel point-based deep framework for multi-view stereo (MVS). Distinct from existing cost volume approaches, our method directly processes the target scene as point clouds. More specifically, our method predicts the depth in a coarse-to-fine manner. We first generate a coarse depth map, convert it into a point cloud and refine the point cloud iteratively by estimating the residual between the depth of the current iteration and that of the ground truth. Our network leverages 3D geometry priors and 2D texture information jointly and effectively by fusing them into a …
Graz University of Technology（格拉茨理工大学）；Sony Europe B.V.（索尼欧洲有限公司）
In this work, we propose BP-MVSNet, a convolutional neural network (CNN)-based Multi-View-Stereo (MVS) method that uses a differentiable Conditional Random Field (CRF) layer for regularization. To this end, we propose to extend the BP layer and add what is necessary to successfully use it in the MVS setting. We therefore show how we can calculate a normalization based on the expected 3D error, which we can then use to normalize the label jumps in the CRF. This is required to make the BP layer invariant to different scales in the MVS setting. In order to also enable fractional label jumps, we p…
University of Chinese Academy of Sciences（中国科学院大学）；Chongqing University（重庆大学）；KAUST（阿卜杜拉国王科技大学）
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 min…
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；PhiGent Robotics（PhiGent机器人公司）；Kwai Inc.（快手公司）
Learning-based Multi-View Stereo (MVS) methods warp source images into the reference camera frustum to form 3D volumes, which are fused as a cost volume to be regularized by subsequent networks. The fusing step plays a vital role in bridging 2D semantics and 3D spatial associations. However, previous methods utilize extra networks to learn 2D information as fusing cues, underusing 3D spatial correlations and bringing additional computation costs. Therefore, we present MVSTER, which leverages the proposed epipolar Transformer to learn both 2D semantics and 3D spatial associations efficiently. S…
In this paper, we propose an efficient and effective dense hybrid recurrent multi-view stereo net with dynamic consistency checking, namely $D^{2}$HC-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 archit…
The present Multi-view stereo (MVS) methods with supervised learning-based networks have an impressive performance comparing with traditional MVS methods. However, the ground-truth depth maps for training are hard to be obtained and are within limited kinds of scenarios. In this paper, we propose a novel unsupervised multi-metric MVS network, named M^3VSNet, for dense point cloud reconstruction without any supervision. To improve the robustness and completeness of point cloud reconstruction, we propose a novel multi-metric loss function that combines pixel-wise and feature-wise loss function t…
The present Multi-view stereo (MVS) methods with supervised learning-based networks have an impressive performance comparing with traditional MVS methods. However, the ground-truth depth maps for training are hard to be obtained and are within limited kinds of scenarios. In this paper, we propose a novel unsupervised multi-metric MVS network, named M^3VSNet, for dense point cloud reconstruction without any supervision. To improve the robustness and completeness of point cloud reconstruction, we propose a novel multi-metric loss function that combines pixel-wise and feature-wise loss function t…
Multi-view stereo (MVS) is a crucial task for precise 3D reconstruction. Most recent studies tried to improve the performance of matching cost volume in MVS by designing aggregated 3D cost volumes and their regularization. This paper focuses on learning a robust feature extraction network to enhance the performance of matching costs without heavy computation in the other steps. In particular, we present a dynamic scale feature extraction network, namely, CDSFNet. It is composed of multiple novel convolution layers, each of which can select a proper patch scale for each pixel guided by the norm…
Stereo-GS: Multi-View Stereo Vision Model for Generalizable 3D Gaussian Splatting Reconstruction
Department of Computer Science, Hong Kong Baptist University（香港 Baptist 大学计算机科学系）；NVIDIA AI Technology Center, NVIDIA（NVIDIA 人工智能技术中心）；School of Control Science and Engineering, Shandong University（控制科学与工程学院，山东大学）
Australian National University（澳大利亚国立大学）；NVIDIA（英伟达）；Australian Centre for Robotic Vision（澳大利亚机器人视觉中心）
We propose a cost volume-based neural network for depth inference from multi-view images. We demonstrate that building a cost volume pyramid in a coarse-to-fine manner instead of constructing a cost volume at a fixed resolution leads to a compact, lightweight network and allows us inferring high resolution depth maps to achieve better reconstruction results. To this end, we first build a cost volume based on uniform sampling of fronto-parallel planes across the entire depth range at the coarsest resolution of an image. Then, given current depth estimate, we construct new cost volumes iterative…
We present MVLayoutNet, an end-to-end network for holistic 3D reconstruction from multi-view panoramas. Our core contribution is to seamlessly combine learned monocular layout estimation and multi-view stereo (MVS) for accurate layout reconstruction in both 3D and image space. We jointly train a layout module to produce an initial layout and a novel MVS module to obtain accurate layout geometry. Unlike standard MVSNet [33], our MVS module takes a newly-proposed layout cost volume, which aggregates multi-view costs at the same depth layer into corresponding layout elements. We additionally prov…
PLA Strategic Support Force Information Engineering University（中国人民解放军战略支援部队信息工程大学）
We present an efficient multi-view stereo (MVS) network for 3D reconstruction from multiview images. While previous learning based reconstruction approaches performed quite well, most of them estimate depth maps at a fixed resolution using plane sweep volumes with a fixed depth hypothesis at each plane, which requires densely sampled planes for desired accuracy and therefore is difficult to achieve high resolution depth maps. In this paper we introduce a coarseto-fine depth inference strategy to achieve high resolution depth. This strategy estimates the depth map at coarsest level, while the d…
MVSGaussian: Fast Generalizable Gaussian Splatting Reconstruction from Multi-View Stereo
Great Bay University（湾谷大学）；Huazhong University of Science and Technology（华中科技大学）；Nanyang Technological University（南洋理工大学）；Shanghai AI Laboratory（上海人工智能实验室）
We propose a novel idea for depth estimation from multi-view image-pose pairs, where the model has capability to leverage information from previous latent-space encodings of the scene. This model uses pairs of images and poses, which are passed through an encoder--decoder model for disparity estimation. The novelty lies in soft-constraining the bottleneck layer by a nonparametric Gaussian process prior. We propose a pose-kernel structure that encourages similar poses to have resembling latent spaces. The flexibility of the Gaussian process (GP) prior provides adapting memory for fusing informa…
Towards Understanding 3D Vision: the Role of Gaussian Curvature
Instituto de Ciências Matemáticas e de Computação, Universidade de São Paulo (ICMC-USP)（圣保罗大学数学与计算机科学研究所）；Courant Institute of Mathematical Sciences, New York University (NYU)（纽约大学Courant数学科学研究所）；Instituto de Matemática Pura e Aplicada (IMPA)（数学与应用数学研究所）
Select-and-Combine (SAC): A Novel Multi-Stereo Depth Fusion Algorithm for Point Cloud Generation via Efficient Local Markov Netlets
The Ohio State University（俄亥俄州立大学）；Geospatial Data Analytics lab（地理空间数据分析实验室）；Department of Electrical and Computer Engineering（电气与计算机工程系）；Translational Data Analytics Institute（转化数据分析研究所）
Learning-based multi-view stereo (MVS) methods have demonstrated promising results. However, very few existing networks explicitly take the pixel-wise visibility into consideration, resulting in erroneous cost aggregation from occluded pixels. In this paper, we explicitly infer and integrate the pixel-wise occlusion information in the MVS network via the matching uncertainty estimation. The pair-wise uncertainty map is jointly inferred with the pair-wise depth map, which is further used as weighting guidance during the multi-view cost volume fusion. As such, the adverse influence of occluded p…
Accurate Point Measurement in 3DGS -- A New Alternative to Traditional Stereoscopic-View Based Measurements
Dept. of Civil, Environmental and Geodetic Engineering, The Ohio State University, Columbus OH, US（土木、环境与大地测量工程系，俄亥俄州立大学，哥伦布 OH，美国）；Dept. of Electrical and Computer Engineering, The Ohio State University, Columbus OH, US（电气与计算机工程系，俄亥俄州立大学，哥伦布 OH，美国）
Diachronic Stereo Matching for Multi-Date Satellite Imagery
IIE, Facultad de Ingeniería, Universidad de la República（乌拉圭国家工程院，工程学院，乌拉圭共和国大学）；Politecnico di Torino（托斯卡纳理工学院）；Eurecat, Centre Tecnologic de Catalunya（欧洲催化中心，加泰罗尼亚技术中心）；AMIAD, Pôle Recherche（AMIAD研究极）；Universite Paris-Saclay, CNRS, ENS Paris-Saclay, Centre Borelli（巴黎萨克雷大学，国家科学研究中心，巴黎萨克雷高等师范学校，博雷利中心）；Institut Universitaire de France（法国国家科学研究中心）
Multi-Label Stereo Matching for Transparent Scene Depth Estimation
Beijing Key Laboratory of Intelligent Information Technology, School of Computer Science & Technology, Beijing Institute of Technology, China（北京智能信息科技重点实验室，计算机科学与技术学院，北京理工大学，中国）；Guangdong Laboratory of Machine Perception and Intelligent Computing, Shenzhen MSU-BIT University, China（广东机器感知与智能计算实验室，深圳MSU-BIT大学，中国）
Department of Computer Science, ETH Zurich（苏黎世联邦理工学院计算机科学系）；Graduate School of Information Science and Technology, The University of Tokyo（东京大学信息科学与技术研究生院）；Department of Computer Science, University of Southern California（南加州大学计算机科学系）；Department of Engineering Science, University of Oxford（牛津大学工程科学系）；University of Chinese Academy of Sciences（中国科学院大学）；School of Computer and Communication Sciences, EPFL（苏黎世联邦理工学院计算机与通信科学学院）；Australian National University（澳大利亚国立大学）；Microsoft, Zurich（微软（瑞士））
