在简单的数据集中,在简单的数据集中开发和广泛地进行了深度多视图立体声(MVS)方法,在那里他们现在优于经典方法。在本文中,我们询问控制方案中达到的结论是否仍然有效,在使用互联网照片集合时仍然有效。我们提出了一种评估方法,探讨了深度MVS方法的三个方面的影响:网络架构,培训数据和监督。我们进行了几个关键观察,我们广泛地定量和定性地验证,无论是深度预测和完整的3D重建。首先,复杂的无监督方法无法在野外训练数据。我们的新方法使三个关键要素成为可能:上采样输出,基于Softmin的聚合和单一的重建损失。其次,监督基于深度堤map的MVS方法是用于重建几个互联网图像的最新技术。最后,我们的评估提供了比通常的结果非常不同。这表明在不受控制的方案中的评估对于新架构很重要。
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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 demonstrated on the large-scale indoor DTU dataset. With simple post-processing, our method not only significantly outperforms previous state-of-the-arts, but also is several times faster in runtime. We also evaluate MVSNet on the complex outdoor Tanks and Temples dataset, where our method ranks first before April 18, 2018 without any fine-tuning, showing the strong generalization ability of MVSNet.
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在不同观点之间找到准确的对应关系是无监督的多视图立体声(MVS)的跟腱。现有方法是基于以下假设:相应的像素具有相似的光度特征。但是,在实际场景中,多视图图像观察到非斜面的表面和经验遮挡。在这项工作中,我们提出了一种新颖的方法,即神经渲染(RC-MVSNET),以解决观点之间对应关系的歧义问题。具体而言,我们施加了一个深度渲染一致性损失,以限制靠近对象表面的几何特征以减轻遮挡。同时,我们引入了参考视图综合损失,以产生一致的监督,即使是针对非兰伯特表面。关于DTU和TANKS \&Temples基准测试的广泛实验表明,我们的RC-MVSNET方法在无监督的MVS框架上实现了最先进的性能,并对许多有监督的方法进行了竞争性能。该代码在https://github.com/上发布。 BOESE0601/RC-MVSNET
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神经隐式表面已成为多视图3D重建的重要技术,但它们的准确性仍然有限。在本文中,我们认为这来自难以学习和呈现具有神经网络的高频纹理。因此,我们建议在不同视图中添加标准神经渲染优化直接照片一致性术语。直观地,我们优化隐式几何体,以便以一致的方式扭曲彼此的视图。我们证明,两个元素是这种方法成功的关键:(i)使用沿着每条光线的预测占用和3D点的预测占用和法线来翘曲整个补丁,并用稳健的结构相似度测量它们的相似性; (ii)以这种方式处理可见性和遮挡,使得不正确的扭曲不会给出太多的重要性,同时鼓励重建尽可能完整。我们评估了我们的方法,在标准的DTU和EPFL基准上被称为NeuralWarp,并表明它在两个数据集上以超过20%重建的艺术态度优于未经监督的隐式表面。
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While deep learning has recently achieved great success on multi-view stereo (MVS), limited training data makes the trained model hard to be generalized to unseen scenarios. Compared with other computer vision tasks, it is rather difficult to collect a large-scale MVS dataset as it requires expensive active scanners and labor-intensive process to obtain ground truth 3D structures. In this paper, we introduce BlendedMVS, a novel large-scale dataset, to provide sufficient training ground truth for learning-based MVS. To create the dataset, we apply a 3D reconstruction pipeline to recover high-quality textured meshes from images of well-selected scenes. Then, we render these mesh models to color images and depth maps. To introduce the ambient lighting information during training, the rendered color images are further blended with the input images to generate the training input. Our dataset contains over 17k high-resolution images covering a variety of scenes, including cities, architectures, sculptures and small objects. Extensive experiments demonstrate that BlendedMVS endows the trained model with significantly better generalization ability compared with other MVS datasets. The dataset and pretrained models are available at https: //github.com/YoYo000/BlendedMVS.
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近年来,与传统方法相比,受监督或无监督的基于学习的MVS方法的性能出色。但是,这些方法仅使用成本量正规化计算的概率量来预测参考深度,这种方式无法从概率量中挖掘出足够的信息。此外,无监督的方法通常尝试使用两步或其他输入进行训练,从而使过程更加复杂。在本文中,我们提出了DS-MVSNET,这是一种具有源深度合成的端到端无监督的MVS结构。为了挖掘概率量的信息,我们通过将概率量和深度假设推向源视图来创造性地综合源深度。同时,我们提出了自适应高斯采样和改进的自适应垃圾箱采样方法,以改善深度假设精度。另一方面,我们利用源深度渲染参考图像,并提出深度一致性损失和深度平滑度损失。这些可以根据不同视图的光度和几何一致性提供其他指导,而无需其他输入。最后,我们在DTU数据集和储罐数据集上进行了一系列实验,这些实验证明了与最先进的方法相比,DS-MVSNET的效率和鲁棒性。
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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 highresolution 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 gated recurrent unit (GRU). This reduces dramatically the memory consumption and makes high-resolution reconstruction feasible. We first show the state-of-the-art performance achieved by the proposed R-MVSNet on the recent MVS benchmarks. Then, we further demonstrate the scalability of the proposed method on several large-scale scenarios, where previous learned approaches often fail due to the memory constraint. Code is available at https://github.com/ YoYo000/MVSNet.
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我们呈现3DVNET,一种新型多视图立体声(MVS)深度预测方法,该方法结合了基于深度和体积的MVS方法的优点。我们的关键思想是使用3D场景建模网络,可迭代地更新一组粗略深度预测,从而产生高度准确的预测,它达成底层场景几何形状。与现有的深度预测技术不同,我们的方法使用体积3D卷积神经网络(CNN),该网络(CNN)在所有深度图中共同地在世界空间上运行。因此,网络可以学习有意义的场景级别。此外,与现有的体积MVS技术不同,我们的3D CNN在特征增强点云上运行,允许有效地聚合多视图信息和灵活的深度映射的迭代细化。实验结果表明,我们的方法超过了Scannet DataSet的深度预测和3D重建度量的最先进的准确性,以及来自Tum-RGBD和ICL-Nuim数据集的一系列场景。这表明我们的方法既有效又推广到新设置。
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Single-view depth prediction is a fundamental problem in computer vision. Recently, deep learning methods have led to significant progress, but such methods are limited by the available training data. Current datasets based on 3D sensors have key limitations, including indoor-only images (NYU), small numbers of training examples (Make3D), and sparse sampling (KITTI). We propose to use multi-view Internet photo collections, a virtually unlimited data source, to generate training data via modern structure-from-motion and multi-view stereo (MVS) methods, and present a large depth dataset called MegaDepth based on this idea. Data derived from MVS comes with its own challenges, including noise and unreconstructable objects. We address these challenges with new data cleaning methods, as well as automatically augmenting our data with ordinal depth relations generated using semantic segmentation. We validate the use of large amounts of Internet data by showing that models trained on MegaDepth exhibit strong generalization-not only to novel scenes, but also to other diverse datasets including Make3D, KITTI, and DIW, even when no images from those datasets are seen during training. 1
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我们呈现ITEMVS,一种用于高分辨率多视图立体声的新数据驱动方法。我们提出了一种基于GRU的基于GRU的估计器,其在其隐藏状态下编码深度的像素明显概率分布。摄入多尺度匹配信息,我们的模型将这些分布物流在多个迭代和Infers深度和信心上。要提取深度图,我们以新颖的方式结合传统的分类和回归。我们验证了我们对DTU,坦克和寺庙和ETH3D的方法的效率和有效性。虽然成为内存和运行时最有效的方法,但我们的模型在DTU和坦克和寺庙的更好的泛化能力方面取得了竞争性能,以及Eth3D而不是最先进的方法。代码可在https://github.com/fangjinhuawang/Itermvs获得。
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传统上,来自摆姿势的图像的3D室内场景重建分为两个阶段:人均深度估计,然后进行深度合并和表面重建。最近,出现了一个直接在最终3D体积特征空间中进行重建的方法家族。尽管这些方法显示出令人印象深刻的重建结果,但它们依赖于昂贵的3D卷积层,从而限制了其在资源受限环境中的应用。在这项工作中,我们回到了传统的路线,并展示着专注于高质量的多视图深度预测如何使用简单的现成深度融合来高度准确的3D重建。我们提出了一个简单的最先进的多视图深度估计器,其中有两个主要贡献:1)精心设计的2D CNN,该2D CNN利用强大的图像先验以及平面扫描特征量和几何损失,并结合2)将密钥帧和几何元数据集成到成本量中,这允许知情的深度平面评分。我们的方法在当前的最新估计中获得了重要的领先优势,以进行深度估计,并在扫描仪和7个镜头上进行3D重建,但仍允许在线实时实时低音重建。代码,模型和结果可在https://nianticlabs.github.io/simplerecon上找到
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With the success of neural volume rendering in novel view synthesis, neural implicit reconstruction with volume rendering has become popular. However, most methods optimize per-scene functions and are unable to generalize to novel scenes. We introduce VolRecon, a generalizable implicit reconstruction method with Signed Ray Distance Function (SRDF). To reconstruct with fine details and little noise, we combine projection features, aggregated from multi-view features with a view transformer, and volume features interpolated from a coarse global feature volume. A ray transformer computes SRDF values of all the samples along a ray to estimate the surface location, which are used for volume rendering of color and depth. Extensive experiments on DTU and ETH3D demonstrate the effectiveness and generalization ability of our method. On DTU, our method outperforms SparseNeuS by about 30% in sparse view reconstruction and achieves comparable quality as MVSNet in full view reconstruction. Besides, our method shows good generalization ability on the large-scale ETH3D benchmark. Project page: https://fangjinhuawang.github.io/VolRecon.
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We propose a novel approach for deep learning-based Multi-View Stereo (MVS). For each pixel in the reference image, our method leverages a deep architecture to search for the corresponding point in the source image directly along the corresponding epipolar line. We denote our method DELS-MVS: Deep Epipolar Line Search Multi-View Stereo. Previous works in deep MVS select a range of interest within the depth space, discretize it, and sample the epipolar line according to the resulting depth values: this can result in an uneven scanning of the epipolar line, hence of the image space. Instead, our method works directly on the epipolar line: this guarantees an even scanning of the image space and avoids both the need to select a depth range of interest, which is often not known a priori and can vary dramatically from scene to scene, and the need for a suitable discretization of the depth space. In fact, our search is iterative, which avoids the building of a cost volume, costly both to store and to process. Finally, our method performs a robust geometry-aware fusion of the estimated depth maps, leveraging a confidence predicted alongside each depth. We test DELS-MVS on the ETH3D, Tanks and Temples and DTU benchmarks and achieve competitive results with respect to state-of-the-art approaches.
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具有已知相机参数的多视图立体声(MVS)基本上是有效深度范围内的1D搜索问题。最近的基于深度学习的MVS方法通常在深度范围内密集地样本深度假设,然后构造对深度预测的预测存储器消耗的3D成本卷。虽然粗细的抽样策略在一定程度上缓解了这个开销问题,但MVS的效率仍然是一个开放的挑战。在这项工作中,我们提出了一种用于高效MV的新方法,其显着降低了内存足迹,同时明显推进最先进的深度预测性能。考虑到效率和有效性,我们调查搜索策略可以合理地最佳地最佳。我们首先将MVS制定为二进制搜索问题,因此提出了用于MV的广义二进制搜索网络。具体地,在每个步骤中,深度范围被分成2个箱,两侧具有额外的1个误差容差箱。执行分类以确定哪个箱包含真实深度。我们还将三种机制分别设计为分别处理分类错误,处理超出范围的样本并降低培训记忆。新配方使我们的方法仅在每个步骤中示出非常少量的深度假设,这是高度记忆效率,并且还极大地促进了快速训练收敛。竞争力基准的实验表明,我们的方法达到了最先进的准确性,内存要少得多。特别是,我们的方法在DTU数据集中获得0.289的总分,并在所有基于学习的方法中排列在具有挑战性的坦克和寺庙高级数据集上的第一名。训练有素的型号和代码将在https://github.com/mizhenxing/gbi-net发布。
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在本文中,我们串联串联一个实时单手抄语和密集的测绘框架。对于姿势估计,串联基于关键帧的滑动窗口执行光度束调整。为了增加稳健性,我们提出了一种新颖的跟踪前端,使用从全局模型中呈现的深度图来执行密集的直接图像对齐,该模型从密集的深度预测逐渐构建。为了预测密集的深度映射,我们提出了通过分层构造具有自适应视图聚合的3D成本卷来平衡关键帧之间的不同立体声基线的3D成本卷来使用整个活动密钥帧窗口的级联视图 - 聚合MVSNet(CVA-MVSNET)。最后,将预测的深度映射融合到表示为截短的符号距离函数(TSDF)体素网格的一致的全局映射中。我们的实验结果表明,在相机跟踪方面,串联优于其他最先进的传统和学习的单眼视觉径管(VO)方法。此外,串联示出了最先进的实时3D重建性能。
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在本文中,我们基于我们对多视图立体声(MVS)中的特征匹配的探索来呈现TransVSNet。我们将MVS模拟返回其特征匹配任务的性质,因此提出了一个强大的功能匹配变换器(FMT),以利用(自我)和(交叉)关注(交叉)在图像内和跨越图像中聚合的长程上下文信息。为了便于更好地调整FMT,我们利用自适应接收领域(ARF)模块,以确保在特征范围内平滑过境,并使用特征途径桥接不同阶段,以通过不同尺度的转换特征和梯度。此外,我们应用配对特征相关性以测量特征之间的相似性,并采用歧义降低焦损,以加强监管。据我们所知,TransmVSNet首次尝试将变压器利用到MV的任务。因此,我们的方法在DTU数据集,坦克和寺庙基准测试和BlendedMVS数据集中实现了最先进的性能。我们的方法代码将在https://github.com/megviirobot/transmvsnet中提供。
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我们提出了一个有效的多视角立体声(MVS)网络,用于从多个RGB图像中推断深度值。最近的研究表明,将实际空间中的几何关系映射到神经网络是MVS问题的重要主题。具体而言,这些方法着重于如何通过构造出色的成本量来表达不同视图之间的对应关系。在本文中,我们提出了一种基于吸收先前经验的更完整的成本量构建方法。首先,我们介绍了自我发挥的机制,以完全汇总输入图像的主导信息,并准确地对远程依赖性进行建模,以选择性地汇总参考特征。其次,我们将小组相关性引入特征聚合,从而大大减轻了记忆和计算负担。同时,此方法增强了不同特征通道之间的信息相互作用。通过这种方法,构建了更轻巧,更有效的成本量。最后,我们遵循粗略的策略,并借助不确定性估计,根据规模完善深度采样范围。我们进一步结合了以前的步骤,以获取注意力较薄。提出了定量和定性实验,以证明我们的模型的性能。
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In this paper, we present a learning-based approach for multi-view stereo (MVS), i.e., estimate the depth map of a reference frame using posed multi-view images. Our core idea lies in leveraging a "learning-to-optimize" paradigm to iteratively index a plane-sweeping cost volume and regress the depth map via a convolutional Gated Recurrent Unit (GRU). Since the cost volume plays a paramount role in encoding the multi-view geometry, we aim to improve its construction both in pixel- and frame- levels. In the pixel level, we propose to break the symmetry of the Siamese network (which is typically used in MVS to extract image features) by introducing a transformer block to the reference image (but not to the source images). Such an asymmetric volume allows the network to extract global features from the reference image to predict its depth map. In view of the inaccuracy of poses between reference and source images, we propose to incorporate a residual pose network to make corrections to the relative poses, which essentially rectifies the cost volume in the frame-level. We conduct extensive experiments on real-world MVS datasets and show that our method achieves state-of-the-art performance in terms of both within-dataset evaluation and cross-dataset generalization.
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A significant weakness of most current deep Convolutional Neural Networks is the need to train them using vast amounts of manually labelled data. In this work we propose a unsupervised framework to learn a deep convolutional neural network for single view depth prediction, without requiring a pre-training stage or annotated ground-truth depths. We achieve this by training the network in a manner analogous to an autoencoder. At training time we consider a pair of images, source and target, with small, known camera motion between the two such as a stereo pair. We train the convolutional encoder for the task of predicting the depth map for the source image. To do so, we explicitly generate an inverse warp of the target image using the predicted depth and known inter-view displacement, to reconstruct the source image; the photometric error in the reconstruction is the reconstruction loss for the encoder. The acquisition of this training data is considerably simpler than for equivalent systems, requiring no manual annotation, nor calibration of depth sensor to camera. We show that our network trained on less than half of the KITTI dataset gives comparable performance to that of the state-of-the-art supervised methods for single view depth estimation. 1 1 Find the model and other imformation on the project github page: https://github. com/Ravi-Garg/Unsupervised_Depth_Estimation
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最新的多视图深度估计方法是在深度视频或多视图立体设置中采用的。尽管设置不同,但这些方法在技术上是相似的:它们将多个源视图与关键视图相关联,以估算关键视图的深度图。在这项工作中,我们介绍了强大的多视图深度基准,该基准构建在一组公共数据集上,并允许在两个设置中对来自不同域的数据进行评估。我们评估了最近的方法,并发现跨领域的性能不平衡。此外,我们考虑了第三个设置,可以使用相机姿势,目的是用正确的尺度估算相应的深度图。我们表明,最近的方法不会在这种情况下跨数据集概括。这是因为它们的成本量输出不足。为了解决这一问题,我们介绍了多视图深度估计的强大MVD基线模型,该模型构建在现有组件上,但采用了新颖的规模增强程序。它可以应用于与目标数据无关的强大多视图深度估计。我们在https://github.com/lmb-freiburg/robustmvd上为建议的基准模型提供了代码。
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