Image dehazing is one of the important and popular topics in computer vision and machine learning. A reliable real-time dehazing method with reliable performance is highly desired for many applications such as autonomous driving, security surveillance, etc. While recent learning-based methods require datasets containing pairs of hazy images and clean ground truth, it is impossible to capture them in real scenes. Many existing works compromise this difficulty to generate hazy images by rendering the haze from depth on common RGBD datasets using the haze imaging model. However, there is still a gap between the synthetic datasets and real hazy images as large datasets with high-quality depth are mostly indoor and depth maps for outdoor are imprecise. In this paper, we complement the existing datasets with a new, large, and diverse dehazing dataset containing real outdoor scenes from High-Definition (HD) 3D movies. We select a large number of high-quality frames of real outdoor scenes and render haze on them using depth from stereo. Our dataset is clearly more realistic and more diversified with better visual quality than existing ones. More importantly, we demonstrate that using this dataset greatly improves the dehazing performance on real scenes. In addition to the dataset, we also evaluate a series state of the art methods on the proposed benchmarking datasets.

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.

The success of monocular depth estimation relies on large and diverse training sets. Due to the challenges associated with acquiring dense ground-truth depth across different environments at scale, a number of datasets with distinct characteristics and biases have emerged. We develop tools that enable mixing multiple datasets during training, even if their annotations are incompatible.In particular, we propose a robust training objective that is invariant to changes in depth range and scale, advocate the use of principled multi-objective learning to combine data from different sources, and highlight the importance of pretraining encoders on auxiliary tasks. Armed with these tools, we experiment with five diverse training datasets, including a new, massive data source: 3D films. To demonstrate the generalization power of our approach we use zero-shot cross-dataset transfer, i.e. we evaluate on datasets that were not seen during training. The experiments confirm that mixing data from complementary sources greatly improves monocular depth estimation. Our approach clearly outperforms competing methods across diverse datasets, setting a new state of the art for monocular depth estimation.

这些年来，展示技术已经发展。开发实用的HDR捕获，处理和显示解决方案以将3D技术提升到一个新的水平至关重要。多曝光立体声图像序列的深度估计是开发成本效益3D HDR视频内容的重要任务。在本文中，我们开发了一种新颖的深度体系结构，以进行多曝光立体声深度估计。拟议的建筑有两个新颖的组成部分。首先，对传统立体声深度估计中使用的立体声匹配技术进行了修改。对于我们体系结构的立体深度估计部分，部署了单一到stereo转移学习方法。拟议的配方规避了成本量构造的要求，该要求由基于重新编码的单码编码器CNN取代，具有不同的重量以进行功能融合。基于有效网络的块用于学习差异。其次，我们使用强大的视差特征融合方法组合了从不同暴露水平上从立体声图像获得的差异图。使用针对不同质量度量计算的重量图合并在不同暴露下获得的差异图。获得的最终预测差异图更强大，并保留保留深度不连续性的最佳功能。提出的CNN具有使用标准动态范围立体声数据或具有多曝光低动态范围立体序列的训练的灵活性。在性能方面，所提出的模型超过了最新的单眼和立体声深度估计方法，无论是定量还是质量地，在具有挑战性的场景流以及暴露的Middlebury立体声数据集上。该体系结构在复杂的自然场景中表现出色，证明了其对不同3D HDR应用的有用性。

不同的环境对长期自主驾驶的户外强大的视觉感知构成了巨大挑战，以及对不同环境影响的学习算法的概括仍然是一个公开问题。虽然最近单眼深度预测得到了很好的研究，但很少有很多工作，专注于不同环境的强大的基于学习的深度预测，例如，由于缺乏如此多环境的现实世界数据集和基准测试，不断变化照明和季节。为此，基于CMU Visual Location DataSet建立了第一个跨赛季单眼深度预测数据集和基准赛季。为了基准不同环境下的深度估计性能，我们使用几个新配制的指标调查来自Kitti基准的代表性和最近的最先进的开源监督，自我监督和域适应深度预测方法。通过对所提出的数据集进行广泛的实验评估，定性和定量分析了多种环境对性能和鲁棒性的影响，表明即使微调，长期单眼深度预测也仍然具有挑战性。我们进一步提供了承诺的途径，即自我监督的培训和立体声几何约束有助于提高改变环境的鲁棒性。数据集可在https://seasondepth.github.io上找到，并且在https://github.com/seasondepth/seasondepth上提供基准工具包。

近年来，Imbersive显示器（例如VR耳机，AR眼镜，多视图显示器，自由点电视）已成为一种新的展示技术，与传统显示相比，提供了更好的视觉体验和观众的参与度。随着3D视频和展示技术的发展，高动态范围（HDR）摄像机和显示器的消费市场迅速增长。缺乏适当的实验数据是3D HDR视频技术领域的主要研究工作的关键障碍。同样，足够的现实世界多曝光实验数据集的不可用是用于HDR成像研究的主要瓶颈，从而限制了观众的体验质量（QOE）。在本文中，我们介绍了在印度理工学院马德拉斯校园内捕获的多元化立体曝光数据集，该数据集是多元化的动植物的所在地。该数据集使用ZED立体相机捕获，并提供户外位置的复杂场景，例如花园，路边景观，节日场地，建筑物和室内地区，例如学术和居住区。提出的数据集可容纳宽深度范围，复杂的深度结构，使物体运动复杂化，照明变化，丰富的色彩动态，纹理差异，除了通过移动摄像机和背景运动引入的显着随机性。拟议的数据集可公开向研究界公开使用。此外，详细描述了捕获，对齐和校准多曝光立体视频和图像的过程。最后，我们讨论了有关HDR成像，深度估计，一致的音调映射和3D HDR编码的进度，挑战，潜在用例和未来研究机会。

从视频中获得地面真相标签很具有挑战性，因为在像素流标签的手动注释非常昂贵且费力。此外，现有的方法试图将合成数据集的训练模型调整到真实的视频中，该视频不可避免地遭受了域差异并阻碍了现实世界应用程序的性能。为了解决这些问题，我们提出了RealFlow，这是一个基于期望最大化的框架，可以直接从任何未标记的现实视频中创建大规模的光流数据集。具体而言，我们首先估计一对视频帧之间的光流，然后根据预测流从该对中合成新图像。因此，新图像对及其相应的流可以被视为新的训练集。此外，我们设计了一种逼真的图像对渲染（RIPR）模块，该模块采用软磁性裂口和双向孔填充技术来减轻图像合成的伪像。在E-Step中，RIPR呈现新图像以创建大量培训数据。在M-Step中，我们利用生成的训练数据来训练光流网络，该数据可用于估计下一个E步骤中的光流。在迭代学习步骤中，流网络的能力逐渐提高，流量的准确性以及合成数据集的质量也是如此。实验结果表明，REALFLOW的表现优于先前的数据集生成方法。此外，基于生成的数据集，我们的方法与受监督和无监督的光流方法相比，在两个标准基准测试方面达到了最先进的性能。我们的代码和数据集可从https://github.com/megvii-research/realflow获得

作为许多自主驾驶和机器人活动的基本组成部分，如自我运动估计，障碍避免和场景理解，单眼深度估计（MDE）引起了计算机视觉和机器人社区的极大关注。在过去的几十年中，已经开发了大量方法。然而，据我们所知，对MDE没有全面调查。本文旨在通过审查1970年至2021年之间发布的197个相关条款来弥补这一差距。特别是，我们为涵盖各种方法的MDE提供了全面的调查，介绍了流行的绩效评估指标并汇总公开的数据集。我们还总结了一些代表方法的可用开源实现，并比较了他们的表演。此外，我们在一些重要的机器人任务中审查了MDE的应用。最后，我们通过展示一些有希望的未来研究方向来结束本文。预计本调查有助于读者浏览该研究领域。

我们提出了一个大规模的真实世界和干净的图像对数据集，以及一种从图像中降低降解的方法，从图像中降低了降解。由于没有用于降低的现实世界数据集，因此当前的最新方法依赖于合成数据，因此受SIM2REAL域间隙的限制。此外，由于没有真实的配对数据集，严格的评估仍然是一个挑战。我们通过通过对非鼻子变化的细致控制收集第一个真实的配对数据集来填补这一空白。我们的数据集对各种现实世界的雨水现象（例如雨条和雨水积累）进行了配对的培训和定量评估。为了学习对雨现象不变的代表，我们提出了一个深层神经网络，该网络通过最大程度地减少雨水和干净图像之间的雨水不变损失来重建基础场景。广泛的实验表明，所提出的数据集使现有的DERAINER受益，我们的模型可以在各种条件下对真实雨水图像的最先进方法优于最先进的方法。

Learning based methods have shown very promising results for the task of depth estimation in single images. However, most existing approaches treat depth prediction as a supervised regression problem and as a result, require vast quantities of corresponding ground truth depth data for training. Just recording quality depth data in a range of environments is a challenging problem. In this paper, we innovate beyond existing approaches, replacing the use of explicit depth data during training with easier-to-obtain binocular stereo footage.We propose a novel training objective that enables our convolutional neural network to learn to perform single image depth estimation, despite the absence of ground truth depth data. Exploiting epipolar geometry constraints, we generate disparity images by training our network with an image reconstruction loss. We show that solving for image reconstruction alone results in poor quality depth images. To overcome this problem, we propose a novel training loss that enforces consistency between the disparities produced relative to both the left and right images, leading to improved performance and robustness compared to existing approaches. Our method produces state of the art results for monocular depth estimation on the KITTI driving dataset, even outperforming supervised methods that have been trained with ground truth depth.

部分闭塞作用是一种现象，即相机附近的模糊物体是半透明的，导致部分外观被遮挡的背景。但是，由于现有的散景渲染方法，由于在全焦点图像中的遮挡区域缺少信息而模拟现实的部分遮挡效果是一项挑战。受到可学习的3D场景表示的启发，我们试图通过引入一种基于MPI的新型高分辨率Bokeh渲染框架来解决部分遮挡，称为MPIB。为此，我们首先介绍了如何将MPI表示形式应用于散布渲染的分析。基于此分析，我们提出了一个MPI表示模块与背景介入模块相结合，以实现高分辨率场景表示。然后，可以将此表示形式重复使用以根据控制参数呈现各种散景效应。为了训练和测试我们的模型，我们还为数据生成设计了基于射线追踪的散景生成器。对合成和现实世界图像的广泛实验验证了该框架的有效性和灵活性。

Recent work has shown that optical flow estimation can be formulated as a supervised learning task and can be successfully solved with convolutional networks. Training of the so-called FlowNet was enabled by a large synthetically generated dataset. The present paper extends the concept of optical flow estimation via convolutional networks to disparity and scene flow estimation. To this end, we propose three synthetic stereo video datasets with sufficient realism, variation, and size to successfully train large networks. Our datasets are the first large-scale datasets to enable training and evaluating scene flow methods. Besides the datasets, we present a convolutional network for real-time disparity estimation that provides state-of-the-art results. By combining a flow and disparity estimation network and training it jointly, we demonstrate the first scene flow estimation with a convolutional network.

With the development of convolutional neural networks, hundreds of deep learning based dehazing methods have been proposed. In this paper, we provide a comprehensive survey on supervised, semi-supervised, and unsupervised single image dehazing. We first discuss the physical model, datasets, network modules, loss functions, and evaluation metrics that are commonly used. Then, the main contributions of various dehazing algorithms are categorized and summarized. Further, quantitative and qualitative experiments of various baseline methods are carried out. Finally, the unsolved issues and challenges that can inspire the future research are pointed out. A collection of useful dehazing materials is available at \url{https://github.com/Xiaofeng-life/AwesomeDehazing}.

Ground truth optical flow is difficult to measure in real scenes with natural motion. As a result, optical flow data sets are restricted in terms of size, complexity, and diversity, making optical flow algorithms difficult to train and test on realistic data. We introduce a new optical flow data set derived from the open source 3D animated short film Sintel. This data set has important features not present in the popular Middlebury flow evaluation: long sequences, large motions, specular reflections, motion blur, defocus blur, and atmospheric effects. Because the graphics data that generated the movie is open source, we are able to render scenes under conditions of varying complexity to evaluate where existing flow algorithms fail. We evaluate several recent optical flow algorithms and find that current highly-ranked methods on the Middlebury evaluation have difficulty with this more complex data set suggesting further research on optical flow estimation is needed. To validate the use of synthetic data, we compare the image-and flow-statistics of Sintel to those of real films and videos and show that they are similar. The data set, metrics, and evaluation website are publicly available.

传统上，本征成像或内在图像分解被描述为将图像分解为两层：反射率，材料的反射率;和一个阴影，由光和几何之间的相互作用产生。近年来，深入学习技术已广泛应用，以提高这些分离的准确性。在本调查中，我们概述了那些在知名内在图像数据集和文献中使用的相关度量的结果，讨论了预测所需的内在图像分解的适用性。虽然Lambertian的假设仍然是许多方法的基础，但我们表明，对图像形成过程更复杂的物理原理组件的潜力越来越意识到，这是光学准确的材料模型和几何形状，更完整的逆轻型运输估计。考虑使用的前瞻和模型以及驾驶分解过程的学习架构和方法，我们将这些方法分类为分解的类型。考虑到最近神经，逆和可微分的渲染技术的进步，我们还提供了关于未来研究方向的见解。

基于深度学习的当前计算机视觉任务需要大量数据，并具有用于模型培训或测试的注释，尤其是在某些密集的估计任务中，例如光流分段和深度估计。实际上，密集估计任务的手动标记非常困难甚至不可能，并且数据集的场景通常仅限于较小的范围，这极大地限制了社区的发展。为了克服这种缺陷，我们提出了一种合成数据集生成方法，以获取无繁重的手动劳动力的可扩展数据集。通过这种方法，我们构建了一个名为Minenavi的数据集，该数据集包含来自飞机的第一镜头视频视频素材，并与准确的地面真相相匹配，以实现飞机导航应用中的深度估算。我们还提供定量实验，以证明通过Minenavi数据集进行预训练可以提高深度估计模型的性能，并加快模型在真实场景数据上的收敛性。由于合成数据集在深层模型的训练过程中与现实世界数据集具有相似的效果，因此我们还提供了具有单眼深度估计方法的其他实验，以证明各种因素在我们的数据集中的影响，例如照明条件和运动模式。

Image view synthesis has seen great success in reconstructing photorealistic visuals, thanks to deep learning and various novel representations. The next key step in immersive virtual experiences is view synthesis of dynamic scenes. However, several challenges exist due to the lack of high-quality training datasets, and the additional time dimension for videos of dynamic scenes. To address this issue, we introduce a multi-view video dataset, captured with a custom 10-camera rig in 120FPS. The dataset contains 96 high-quality scenes showing various visual effects and human interactions in outdoor scenes. We develop a new algorithm, Deep 3D Mask Volume, which enables temporally-stable view extrapolation from binocular videos of dynamic scenes, captured by static cameras. Our algorithm addresses the temporal inconsistency of disocclusions by identifying the error-prone areas with a 3D mask volume, and replaces them with static background observed throughout the video. Our method enables manipulation in 3D space as opposed to simple 2D masks, We demonstrate better temporal stability than frame-by-frame static view synthesis methods, or those that use 2D masks. The resulting view synthesis videos show minimal flickering artifacts and allow for larger translational movements.

Fast and easy handheld capture with guideline: closest object moves at most D pixels between views Promote sampled views to local light field via layered scene representation Blend neighboring local light fields to render novel views

大多数自我监督的单眼深度估计方法侧重于驾驶场景。我们表明，这些方法概括了看不见的复杂室内场景，其中物体杂乱，在近场中被任意排列。为了获得更多的稳健性，我们提出了一种结构蒸馏方法来从预磨削的深度估计器中学习诀窍，该估计由于其在野外的混合数据集训练而产生的结构化但度量无话束深度。通过将蒸馏与自我监督的分支组合，从左右一致性学习指标，我们为通用室内场景获得结构化和公制深度，并实时推断。为了促进学习和评估，我们收集Simsin，一个数据集，与数千个环境和Unisin，一个数据集，该数据集包含了关于通用室内环境的大约500个真实扫描序列。我们在SIM-to-Real和实际设置中进行实验，并显示定性和定量的改进，以及使用我们的深度映射的下游应用程序。这项工作提供了完整的研究，涵盖方法，数据和应用程序。我们认为这项工作通过自我监督为实际室内深度估计奠定了坚实的基础。