单光子敏感的深度传感器正在越来越多地用于人类姿势和手势识别的下一代电子。但是，具有成本效益的传感器通常具有低空间分辨率，从而将其用于基本运动识别和简单的对象检测。在这里，我们执行一个时间到空间映射，从而大大增加了简单飞行时间传感器的分辨率，即〜初始分辨率为4 $ \ times $ 4像素到分辨率32 $ \ times $ 32像素的深度图像。然后，可以将输出深度图用于准确的三维人姿势估计多人。我们开发了一个新的可解释框架，该框架为我们的网络如何利用其输入数据提供了直觉，并提供了有关相关参数的关键信息。我们的工作大大扩展了简单的飞机飞行时间传感器的用例，并为将来应用于具有相似数据类型的其他类型的传感器（即雷达和声纳）开辟了有希望的可能性。

Time-resolved image sensors that capture light at pico-to-nanosecond timescales were once limited to niche applications but are now rapidly becoming mainstream in consumer devices. We propose low-cost and low-power imaging modalities that capture scene information from minimal time-resolved image sensors with as few as one pixel. The key idea is to flood illuminate large scene patches (or the entire scene) with a pulsed light source and measure the time-resolved reflected light by integrating over the entire illuminated area. The one-dimensional measured temporal waveform, called \emph{transient}, encodes both distances and albedoes at all visible scene points and as such is an aggregate proxy for the scene's 3D geometry. We explore the viability and limitations of the transient waveforms by themselves for recovering scene information, and also when combined with traditional RGB cameras. We show that plane estimation can be performed from a single transient and that using only a few more it is possible to recover a depth map of the whole scene. We also show two proof-of-concept hardware prototypes that demonstrate the feasibility of our approach for compact, mobile, and budget-limited applications.

毫米波（mmwave）雷达在不利的环境中起作用，例如在烟，雨，雪，照明等不良环境中起作用。先前的工作探索了从嘈杂且稀疏的MMWAVE雷达信号中重建3D骨骼或网格的可能性。但是，目前尚不清楚我们如何准确地从跨场景的MMWave信号重建3D主体，以及与摄像机相比的性能，当单独使用MMWave雷达或将它们与摄像机结合时，这是需要考虑的重要方面。为了回答这些问题，首先设计并构建了多个传感器，以收集大规模数据集。该数据集由在不同场景中的同步和校准的MMWave雷达点云和RGB（D）图像组成，以及在场景中人类的骨架/网格注释。使用此数据集，我们使用来自不同传感器的输入来训练最先进的方法，并在各种情况下对其进行测试。结果表明，1）尽管生成点云的噪音和稀疏性，MMWave雷达可以比RGB摄像机获得更好的重建精度，但比深度摄像头还差； 2）MMWave雷达的重建受不利天气条件的影响，而RGB（D）摄像机受到严重影响。此外，对数据集的分析和结果对改善MMWave雷达重建的重建以及来自不同传感器的信号的组合的洞察力。

了解协作环境中工人和机器人的确切3D位置可以实现多种真实应用，例如检测不安全情况或用于统计和社会目的的相互作用的研究。在本文中，我们提出了一个基于深度设备和深度神经网络的非侵入性和光变色的框架，以估算外部摄像头的3D机器人姿势。该方法可以应用于任何机器人，而无需硬件访问内部状态。我们介绍了预测姿势的新颖代表，即半光谱脱钩的热图（SPDH），以准确计算世界坐标中的3D关节位置，以适应为2D人类姿势估计设计的有效的深层网络。所提出的方法可以作为基于XYZ坐标的输入深度表示，可以在合成深度数据上进行训练，并应用于现实世界设置，而无需域适应技术。为此，我们根据合成和真实深度图像介绍SIMBA数据集，并将其用于实验评估。结果表明，由特定的深度图表示和SPDH制成的建议方法克服了当前的最新状态。

尽管最近的进步，但是，尽管最近的进展，但是从单个图像中的人类姿势的全3D估计仍然是一个具有挑战性的任务。在本文中，我们探讨了关于场景几何体的强先前信息的假设可用于提高姿态估计精度。为了主弱地解决这个问题，我们已经组装了一种新的$ \ textbf {几何姿势提供} $ DataSet，包括与各种丰富的3D环境交互的人员的多视图图像。我们利用商业运动捕获系统来收集场景本身的姿势和构造精确的几何3D CAD模型的金标估计。要将对现有框架的现有框架注入图像的现有框架，我们介绍了一种新颖的，基于视图的场景几何形状，一个$ \ textbf {多层深度图} $，它采用了多次射线跟踪到简明地编码沿着每种相机视图光线方向的多个表面入口和退出点。我们提出了两种不同的机制，用于集成多层深度信息姿势估计：输入作为升降2D姿势的编码光线特征，其次是促进学习模型以支持几何一致姿态估计的可差异损失。我们通过实验展示这些技术可以提高3D姿势估计的准确性，特别是在遮挡和复杂场景几何形状的存在中。

3D Flash LiDAR是传统扫描激光雷达系统的替代方法，有望在紧凑的外形尺寸中进行精确的深度成像，并且没有运动部件，例如自动驾驶汽车，机器人技术和增强现实（AR）等应用。通常在图像传感器格式中使用单光子，直接飞行时间（DTOF）接收器实施，设备的操作可能会受到需要在室外场景中处理和压缩的大量光子事件的阻碍以及对较大数组的可扩展性。我们在这里提出了一个64x32像素（256x128 spad）DTOF成像器，该成像器通过将像素与嵌入式直方图使用像素一起克服这些局限性，该直方直方图锁定并跟踪返回信号。这大大降低了输出数据帧的大小，可在10 kfps范围内或100 kfps的最大帧速率进行直接深度读数。该传感器可选择性地读数检测表面或传感运动的像素，从而减少功耗和片外处理要求。我们演示了传感器在中端激光雷达中的应用。

由于价格合理的可穿戴摄像头和大型注释数据集的可用性，在过去几年中，Egintric Vision（又名第一人称视觉-FPV）的应用程序在过去几年中蓬勃发展。可穿戴摄像机的位置（通常安装在头部上）允许准确记录摄像头佩戴者在其前面的摄像头，尤其是手和操纵物体。这种内在的优势可以从多个角度研究手：将手及其部分定位在图像中；了解双手涉及哪些行动和活动；并开发依靠手势的人类计算机界面。在这项调查中，我们回顾了使用以自我为中心的愿景专注于手的文献，将现有方法分类为：本地化（其中的手或部分在哪里？）；解释（手在做什么？）；和应用程序（例如，使用以上为中心的手提示解决特定问题的系统）。此外，还提供了带有手基注释的最突出的数据集的列表。

We introduce a new dataset, Human3.6M, of 3.6 Million accurate 3D Human poses, acquired by recording the performance of 5 female and 6 male subjects, under 4 different viewpoints, for training realistic human sensing systems and for evaluating the next generation of human pose estimation models and algorithms. Besides increasing the size of the datasets in the current state of the art by several orders of magnitude, we also aim to complement such datasets with a diverse set of motions and poses encountered as part of typical human activities (taking photos, talking on the phone, posing, greeting, eating, etc.), with additional synchronized image, human motion capture and time of flight (depth) data, and with accurate 3D body scans of all the subject actors involved. We also provide controlled mixed reality evaluation scenarios where 3D human models are animated using motion capture and inserted using correct 3D geometry, in complex real environments, viewed with moving cameras, and under occlusion. Finally, we provide a set of large scale statistical models and detailed evaluation baselines for the dataset illustrating its diversity and the scope for improvement by future work in the research community. Our experiments show that our best large scale model can leverage our full training set to obtain a 20% improvement in performance compared to a training set of the scale of the largest existing public dataset for this problem. Yet the potential for improvement by leveraging higher capacity, more complex models with our large dataset, is substantially vaster and should stimulate future research. The dataset together with code for the associated large-scale learning models, features, visualization tools, as well as the evaluation server, is available online at http://vision.imar.ro/human3.6m.

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。

Estimating human pose, shape, and motion from images and videos are fundamental challenges with many applications. Recent advances in 2D human pose estimation use large amounts of manually-labeled training data for learning convolutional neural networks (CNNs). Such data is time consuming to acquire and difficult to extend. Moreover, manual labeling of 3D pose, depth and motion is impractical. In this work we present SURREAL (Synthetic hUmans foR REAL tasks): a new large-scale dataset with synthetically-generated but realistic images of people rendered from 3D sequences of human motion capture data. We generate more than 6 million frames together with ground truth pose, depth maps, and segmentation masks. We show that CNNs trained on our synthetic dataset allow for accurate human depth estimation and human part segmentation in real RGB images. Our results and the new dataset open up new possibilities for advancing person analysis using cheap and large-scale synthetic data.

现代智能手机可以在60〜Hz中持续流动多百万像素RGB图像，与高质量的3D姿势信息和低分辨率LIDAR驱动深度估计同步。在快照照片期间，摄影师的手的自然不稳定性提供了相机姿势的毫米级别变化，我们可以在圆形缓冲器中与RGB和深度一起捕获。在这项工作中，我们探索如何从取景期间获得的这些测量束，我们可以将密集的微基线线视差提示与千克激光雷达深度相结合，以蒸馏高保真深度图。我们采取测试时间优化方法并训练坐标MLP，以沿着摄影师的自然抖动跟踪的路径的连续坐标输出光度计和几何一致深度估计。该方法将高分辨率深度估计为“点拍摄”桌面摄影而言，不需要额外的硬件，人造手动运动或超出按钮的按钮的用户交互。

Visual perception plays an important role in autonomous driving. One of the primary tasks is object detection and identification. Since the vision sensor is rich in color and texture information, it can quickly and accurately identify various road information. The commonly used technique is based on extracting and calculating various features of the image. The recent development of deep learning-based method has better reliability and processing speed and has a greater advantage in recognizing complex elements. For depth estimation, vision sensor is also used for ranging due to their small size and low cost. Monocular camera uses image data from a single viewpoint as input to estimate object depth. In contrast, stereo vision is based on parallax and matching feature points of different views, and the application of deep learning also further improves the accuracy. In addition, Simultaneous Location and Mapping (SLAM) can establish a model of the road environment, thus helping the vehicle perceive the surrounding environment and complete the tasks. In this paper, we introduce and compare various methods of object detection and identification, then explain the development of depth estimation and compare various methods based on monocular, stereo, and RDBG sensors, next review and compare various methods of SLAM, and finally summarize the current problems and present the future development trends of vision technologies.

Its numerous applications make multi-human 3D pose estimation a remarkably impactful area of research. Nevertheless, assuming a multiple-view system composed of several regular RGB cameras, 3D multi-pose estimation presents several challenges. First of all, each person must be uniquely identified in the different views to separate the 2D information provided by the cameras. Secondly, the 3D pose estimation process from the multi-view 2D information of each person must be robust against noise and potential occlusions in the scenario. In this work, we address these two challenges with the help of deep learning. Specifically, we present a model based on Graph Neural Networks capable of predicting the cross-view correspondence of the people in the scenario along with a Multilayer Perceptron that takes the 2D points to yield the 3D poses of each person. These two models are trained in a self-supervised manner, thus avoiding the need for large datasets with 3D annotations.

内部的姿势估计显示出在医院患者监测，睡眠研究和智能家居等领域的价值。在本文中，我们探讨了借助现有的姿势估计器，从高度模棱两可的压力数据中检测身体姿势的不同策略。我们通过直接使用或通过在两个压力数据集上对其进行重新训练来检查预训练的姿势估计器的性能。我们还利用可学习的预处理域适应步骤探索了其他策略，该步骤将模糊的压力图转换为更接近共同目的姿势估计模块的预期输入空间的表示。因此，我们使用了具有多个尺度的完全卷积网络，以向预训练的姿势估计模块提供压力图的姿势特异性特征。我们对不同方法的完整分析表明，在压力数据上，可学习的预处理模块的组合以及重新训练基于图像的姿势估计器能够克服诸如高度模糊的压力点之类的问题，以实现很高的姿势估计准确性。

作为一种引起巨大关注的新兴技术，通过分析继电器表面上的漫反射来重建隐藏物体的非视线（NLOS）成像，具有广泛的应用前景，在自主驾驶，医学成像和医学成像领域防御。尽管信噪比低（SNR）和高不良效率的挑战，但近年来，NLOS成像已迅速发展。大多数当前的NLOS成像技术使用传统的物理模型，通过主动或被动照明构建成像模型，并使用重建算法来恢复隐藏场景。此外，NLOS成像的深度学习算法最近也得到了很多关注。本文介绍了常规和深度学习的NLOS成像技术的全面概述。此外，我们还调查了新的拟议的NLOS场景，并讨论了现有技术的挑战和前景。这样的调查可以帮助读者概述不同类型的NLOS成像，从而加速了在角落周围看到的发展。

传感器是将物理参数或环境特征（例如温度，距离，速度等）转换为可以通过数字测量和处理以执行特定任务的信号的设备。移动机器人需要传感器来测量其环境的属性，从而允许安全导航，复杂的感知和相应的动作以及与填充环境的其他代理的有效相互作用。移动机器人使用的传感器范围从简单的触觉传感器（例如保险杠）到复杂的基于视觉的传感器，例如结构化灯相机。所有这些都提供了可以由机器人计算机处理的数字输出（例如，字符串，一组值，矩阵等）。通常通过使用传感器中包含的数字转换器（ADC）的类似物来离散一个或多个模拟电信号来获得此类输出。在本章中，我们介绍了移动机器人技术中最常见的传感器，并提供了其分类法，基本特征和规格的介绍。对功能和应用程序类型的描述遵循一种自下而上的方法：在描述现实世界传感器之前，介绍了传感器所基于的基本原理和组件，这些传感器通常基于多种技术和基本设备。

We present a novel single-shot interferometric ToF camera targeted for precise 3D measurements of dynamic objects. The camera concept is based on Synthetic Wavelength Interferometry, a technique that allows retrieval of depth maps of objects with optically rough surfaces at submillimeter depth precision. In contrast to conventional ToF cameras, our device uses only off-the-shelf CCD/CMOS detectors and works at their native chip resolution (as of today, theoretically up to 20 Mp and beyond). Moreover, we can obtain a full 3D model of the object in single-shot, meaning that no temporal sequence of exposures or temporal illumination modulation (such as amplitude or frequency modulation) is necessary, which makes our camera robust against object motion. In this paper, we introduce the novel camera concept and show first measurements that demonstrate the capabilities of our system. We present 3D measurements of small (cm-sized) objects with > 2 Mp point cloud resolution (the resolution of our used detector) and up to sub-mm depth precision. We also report a "single-shot 3D video" acquisition and a first single-shot "Non-Line-of-Sight" measurement. Our technique has great potential for high-precision applications with dynamic object movement, e.g., in AR/VR, industrial inspection, medical imaging, and imaging through scattering media like fog or human tissue.

自动化驾驶系统（广告）开辟了汽车行业的新领域，为未来的运输提供了更高的效率和舒适体验的新可能性。然而，在恶劣天气条件下的自主驾驶已经存在，使自动车辆（AVS）长时间保持自主车辆（AVS）或更高的自主权。本文评估了天气在分析和统计方式中为广告传感器带来的影响和挑战，并对恶劣天气条件进行了解决方案。彻底报道了关于对每种天气的感知增强的最先进技术。外部辅助解决方案如V2X技术，当前可用的数据集，模拟器和天气腔室的实验设施中的天气条件覆盖范围明显。通过指出各种主要天气问题，自主驾驶场目前正在面临，近年来审查硬件和计算机科学解决方案，这项调查概述了在不利的天气驾驶条件方面的障碍和方向的障碍和方向。

Autonomous driving is an exciting new industry, posing important research questions. Within the perception module, 3D human pose estimation is an emerging technology, which can enable the autonomous vehicle to perceive and understand the subtle and complex behaviors of pedestrians. While hardware systems and sensors have dramatically improved over the decades -- with cars potentially boasting complex LiDAR and vision systems and with a growing expansion of the available body of dedicated datasets for this newly available information -- not much work has been done to harness these novel signals for the core problem of 3D human pose estimation. Our method, which we coin HUM3DIL (HUMan 3D from Images and LiDAR), efficiently makes use of these complementary signals, in a semi-supervised fashion and outperforms existing methods with a large margin. It is a fast and compact model for onboard deployment. Specifically, we embed LiDAR points into pixel-aligned multi-modal features, which we pass through a sequence of Transformer refinement stages. Quantitative experiments on the Waymo Open Dataset support these claims, where we achieve state-of-the-art results on the task of 3D pose estimation.

自治机器人目前是最受欢迎的人工智能问题之一，在过去十年中，从自动驾驶汽车和人形系统到交付机器人和无人机，这是一项最受欢迎的智能问题。部分问题是获得一个机器人，以模仿人类的感知，我们的视觉感，用诸如神经网络等数学模型用相机和大脑的眼睛替换眼睛。开发一个能够在没有人为干预的情况下驾驶汽车的AI和一个小型机器人在城市中递送包裹可能看起来像不同的问题，因此来自感知和视觉的观点来看，这两个问题都有几种相似之处。我们目前的主要解决方案通过使用计算机视觉技术，机器学习和各种算法来实现对环境感知的关注，使机器人理解环境或场景，移动，调整其轨迹并执行其任务（维护，探索，等。）无需人为干预。在这项工作中，我们从头开始开发一个小型自动车辆，能够仅使用视觉信息理解场景，通过工业环境导航，检测人员和障碍，或执行简单的维护任务。我们审查了基本问题的最先进问题，并证明了小规模采用的许多方法类似于来自特斯拉或Lyft等公司的真正自动驾驶汽车中使用的方法。最后，我们讨论了当前的机器人和自主驾驶状态以及我们在这一领域找到的技术和道德限制。