Active stereo vision is important in reconstructing objects without obvious textures. However, it is still very challenging to extract and match the projected patterns from two camera views automatically and robustly. In this paper, we propose a new pattern extraction method and a new stereo vision matching method based on our novel structured light pattern. Instead of using the widely used 2D disparity to calculate the depths of the objects, we use the ray intersection to compute the 3D shapes directly. Experimental results showed that the proposed approach could reconstruct the 3D shape of the object significantly more robustly than state of the art methods that include the widely used disparity based active stereo vision method, the time of flight method and the structured light method. In addition, experimental results also showed that the proposed approach could reconstruct the 3D motions of the dynamic shapes robustly.
Although nowadays advanced dense image matching (DIM) algorithms are able to produce LiDAR (Light Detection And Ranging) comparable dense point clouds from satellite stereo images, the accuracy and completeness of such point clouds heavily depend on the geometric parameters of the satellite stereo images. The intersection angle between two images are normally seen as the most important one in stereo data acquisition, as the state-of-the-art DIM algorithms work best on narrow baseline (smaller intersection angle) stereos (E.g. Semi-Global Matching regards 15-25 degrees as good intersection angl…
Institute of Computer Science, FORTH（FORTH计算机科学研究所）；Computer Science Department, University of Crete（克里特大学计算机科学系）
We present a novel solution to the problem of 3D tracking of the articulated motion of human hand(s), possibly in interaction with other objects. The vast majority of contemporary relevant work capitalizes on depth information provided by RGBD cameras. In this work, we show that accurate and efficient 3D hand tracking is possible, even for the case of RGB stereo. A straightforward approach for solving the problem based on such input would be to first recover depth and then apply a state of the art depth-based 3D hand tracking method. Unfortunately, this does not work well in practice because t…
Cross-Modal RGB-D Fusion Transformer for 6D Pose Estimation of Non-Cooperative Spacecraft with Stereo-Derived Depth
School of Physics, Northeast Normal University（东北师范大学物理学院）；Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Science（长春光学精密机械与物理研究所）
University of Michigan（密歇根大学）；Toyota Research Institute（丰田研究院）；Cornell University（康奈尔大学）
This paper reports on a novel nonparametric rigid point cloud registration framework that jointly integrates geometric and semantic measurements such as color or semantic labels into the alignment process and does not require explicit data association. The point clouds are represented as nonparametric functions in a reproducible kernel Hilbert space. The alignment problem is formulated as maximizing the inner product between two functions, essentially a sum of weighted kernels, each of which exploits the local geometric and semantic features. As a result of the continuous models, analytical gr…
ASGrasp: Generalizable Transparent Object Reconstruction and 6-DoF Grasp Detection from RGB-D Active Stereo Camera
Samsung R&D Institute China-Beijing（三星中国北京研发中心）；CFCS, School of Computer Science, Peking University（计算机科学系，北京大学）；Beijing Academy of Artificial Intelligence (BAAI)（北京人工智能研究院）
Yonsei University（延世大学）；Ewha Womans University（梨花女子大学）；Agency for Defense Development（国防发展局）
Current self-supervised methods for monocular depth estimation are largely based on deeply nested convolutional networks that leverage stereo image pairs or monocular sequences during a training phase. However, they often exhibit inaccurate results around occluded regions and depth boundaries. In this paper, we present a simple yet effective approach for monocular depth estimation using stereo image pairs. The study aims to propose a student-teacher strategy in which a shallow student network is trained with the auxiliary information obtained from a deeper and more accurate teacher network. Sp…
Hefei University of Technology（合肥工业大学）；Orbbec（奥比中光）；Sun Yat-sen University（中山大学）
It is well known that the passive stereo system cannot adapt well to weak texture objects, e.g., white walls. However, these weak texture targets are very common in indoor environments. In this paper, we present a novel stereo system, which consists of two cameras (an RGB camera and an IR camera) and an IR speckle projector. The RGB camera is used both for depth estimation and texture acquisition. The IR camera and the speckle projector can form a monocular structured-light (MSL) subsystem, while the two cameras can form a binocular stereo subsystem. The depth map generated by the MSL subsyste…
Depth map fusion is an essential part in both stereo and RGB-D based 3-D reconstruction pipelines. Whether produced with a passive stereo reconstruction or using an active depth sensor, such as Microsoft Kinect, the depth maps have noise and may have poor initial registration. In this paper, we introduce a method which is capable of handling outliers, and especially, even significant registration errors. The proposed method first fuses a sequence of depth maps into a single non-redundant point cloud so that the redundant points are merged together by giving more weight to more certain measurem…
NVIDIA（英伟达）；University of Erlangen-Nuremberg（埃尔朗根-纽伦堡大学）；SenseTime（商汤科技）；Technical University of Munich（慕尼黑工业大学）
Scene reconstruction from unorganized RGB images is an important task in many computer vision applications. Multi-view Stereo (MVS) is a common solution in photogrammetry applications for the dense reconstruction of a static scene. The static scene assumption, however, limits the general applicability of MVS algorithms, as many day-to-day scenes undergo non-rigid motion, e.g., clothes, faces, or human bodies. In this paper, we open up a new challenging direction: dense 3D reconstruction of scenes with non-rigid changes observed from arbitrary, sparse, and wide-baseline views. We formulate the …
The University of Adelaide（阿德莱德大学）；Tsinghua University（清华大学）；Huazhong University of Science and Technology（华中科技大学）；Shanghai Institute for Advanced Communications and Data Science, Shanghai University（上海大学上海先进通信与数据科学研究院）
Most existing algorithms for depth estimation from single monocular images need large quantities of metric groundtruth depths for supervised learning. We show that relative depth can be an informative cue for metric depth estimation and can be easily obtained from vast stereo videos. Acquiring metric depths from stereo videos is sometimes impracticable due to the absence of camera parameters. In this paper, we propose to improve the performance of metric depth estimation with relative depths collected from stereo movie videos using existing stereo matching algorithm. We introduce a new "Relati…
Niantic；University of Edinburgh（爱丁堡大学）；UCL（伦敦大学学院）
Supervised deep networks are among the best methods for finding correspondences in stereo image pairs. Like all supervised approaches, these networks require ground truth data during training. However, collecting large quantities of accurate dense correspondence data is very challenging. We propose that it is unnecessary to have such a high reliance on ground truth depths or even corresponding stereo pairs. Inspired by recent progress in monocular depth estimation, we generate plausible disparity maps from single images. In turn, we use those flawed disparity maps in a carefully designed pipel…
The University of Texas at Austin（德克萨斯大学奥斯汀分校）；Apple（苹果公司）
Deep learning has made significant impacts on multi-view stereo systems. State-of-the-art approaches typically involve building a cost volume, followed by multiple 3D convolution operations to recover the input image's pixel-wise depth. While such end-to-end learning of plane-sweeping stereo advances public benchmarks' accuracy, they are typically very slow to compute. We present \ouralg, a highly efficient multi-view stereo algorithm that seamlessly integrates multi-view constraints into single-view networks via an attention mechanism. Since \ouralg only builds on 2D convolutions, it is at le…
We propose an accurate and lightweight convolutional neural network for stereo estimation with depth completion. We name this method fully-convolutional deformable similarity network with depth completion (FCDSN-DC). This method extends FC-DCNN by improving the feature extractor, adding a network structure for training highly accurate similarity functions and a network structure for filling inconsistent disparity estimates. The whole method consists of three parts. The first part consists of fully-convolutional densely connected layers that computes expressive features of rectified image pairs…
Plane feature is a kind of stable landmark to reduce drift error in SLAM system. It is easy and fast to extract planes from dense point cloud, which is commonly acquired from RGB-D camera or lidar. But for stereo camera, it is hard to compute dense point cloud accurately and efficiently. In this paper, we propose a novel method to compute plane parameters using intersecting lines which are extracted from the stereo image. The plane features commonly exist on the surface of man-made objects and structure, which have regular shape and straight edge lines. In 3D space, two intersecting lines can …
University of California San Diego（加州大学圣迭戈分校）；Tsinghua University（清华大学）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）
Accurate stereo depth estimation plays a critical role in various 3D tasks in both indoor and outdoor environments. Recently, learning-based multi-view stereo methods have demonstrated competitive performance with a limited number of views. However, in challenging scenarios, especially when building cross-view correspondences is hard, these methods still cannot produce satisfying results. In this paper, we study how to leverage a normal estimation model and the predicted normal maps to improve the depth quality. We couple the learning of a multi-view normal estimation module and a multi-view d…
Progressive Per-Branch Depth Optimization for DEFOM-Stereo and SAM3 Joint Analysis in UAV Forestry Applications
Centre for Data Science and Artificial Intelligence（数据科学与人工智能中心）；Victoria University of Wellington（威灵顿维多利亚大学）；Department of Computer Science and Software Engineering（计算机科学与软件工程系）；University of Canterbury（坎特伯雷大学）
University of Innsbruck（因斯布鲁克大学）；LEC GmbH（LEC有限公司）；INNIO Jenbacher GmbH & Co OG（INNIO颜巴赫两合公司）
Real-time estimation of actual object depth is an essential module for various autonomous system tasks such as 3D reconstruction, scene understanding and condition assessment. During the last decade of machine learning, extensive deployment of deep learning methods to computer vision tasks has yielded approaches that succeed in achieving realistic depth synthesis out of a simple RGB modality. Most of these models are based on paired RGB-depth data and/or the availability of video sequences and stereo images. The lack of sequences, stereo data and RGB-depth pairs makes depth estimation a fully …
Aerial Multi-View Stereo via Adaptive Depth Range Inference and Normal Cues
Department of Information Science and Technology, Ocean University of China（信息科学与技术学院，中国海洋大学）；School of Computing and Mathematical Sciences, University of Leicester（计算与数学科学学院，莱斯特大学）；Department of Electrical and Computer Engineering, Mississippi State University（电气与计算机工程学院，密苏里州立大学）
ZipMVS: Multi-View Stereo with Compressed Cost Volumes
School of Artificial Intelligence and Robotics, Hunan University（湖南大学人工智能与机器人学院）；I3A, Universidad de Zaragoza（萨拉戈萨大学I3A研究所）；Agricultural Information Institute, Chinese Academy of Agricultural Sciences（中国农业科学院农业信息研究所）
Learning-based stereo matching and depth estimation networks currently excel on public benchmarks with impressive results. However, state-of-the-art networks often fail to generalize from synthetic imagery to more challenging real data domains. This paper is an attempt to uncover hidden secrets of achieving domain robustness and in particular, discovering the important ingredients of generalization success of stereo matching networks by analyzing the effect of synthetic image learning on real data performance. We provide evidence that demonstrates that learning of features in the synthetic dom…
Stereo-Inertial Poser: Towards Metric-Accurate Shape-Aware Motion Capture Using Sparse IMUs and a Single Stereo Camera
School of Computer Science, Shanghai Jiao Tong University（上海交通大学计算机科学学院）；Meta Robotics Institute, SJTU（SJTU机器人研究院）；Shanghai Innovation Institute（上海创新研究院）
Hong Kong University of Science and Technology（香港科技大学）
We design a multiscopic vision system that utilizes a low-cost monocular RGB camera to acquire accurate depth estimation for robotic applications. Unlike multi-view stereo with images captured at unconstrained camera poses, the proposed system actively controls a robot arm with a mounted camera to capture a sequence of images in horizontally or vertically aligned positions with the same parallax. In this system, we combine the cost volumes for stereo matching between the reference image and the surrounding images to form a fused cost volume that is robust to outliers. Experiments on the Middle…
Murdoch University（莫道克大学）；University of South Australia（南澳大学）；University of Western Australia（西澳大学）
Estimating depth from RGB images is a long-standing ill-posed problem, which has been explored for decades by the computer vision, graphics, and machine learning communities. Among the existing techniques, stereo matching remains one of the most widely used in the literature due to its strong connection to the human binocular system. Traditionally, stereo-based depth estimation has been addressed through matching hand-crafted features across multiple images. Despite the extensive amount of research, these traditional techniques still suffer in the presence of highly textured areas, large unifo…
Deep Depth Estimation from Thermal Image: Dataset, Benchmark, and Challenges
School of Computer Science, Carnegie Mellon University（卡内基梅隆大学计算机科学学院）；School of Computer Science and Engineering, Pusan National University（釜山国立大学计算机科学与工程学院）
We introduce DROID-SLAM, a new deep learning based SLAM system. DROID-SLAM consists of recurrent iterative updates of camera pose and pixelwise depth through a Dense Bundle Adjustment layer. DROID-SLAM is accurate, achieving large improvements over prior work, and robust, suffering from substantially fewer catastrophic failures. Despite training on monocular video, it can leverage stereo or RGB-D video to achieve improved performance at test time. The URL to our open source code is https://github.com/princeton-vl/DROID-SLAM.
Rethinking Monocular Depth Embedding for Generalized Stereo Matching
Shaanxi Key Laboratory for Network Computing and Security Technology, Xi’an University of Technology（陕西理工大学网络计算与安全技术重点实验室）；College of Computer Science, Sichuan University（四川大学计算机学院）
In this paper, we introduce a deep multi-view stereo (MVS) system that jointly predicts depths, surface normals and per-view confidence maps. The key to our approach is a novel solver that iteratively solves for per-view depth map and normal map by optimizing an energy potential based on the locally planar assumption. Specifically, the algorithm updates depth map by propagating from neighboring pixels with slanted planes, and updates normal map with local probabilistic plane fitting. Both two steps are monitored by a customized confidence map. This solver is not only effective as a post-proces…
In this paper, we present a new perspective towards image-based shape generation. Most existing deep learning based shape reconstruction methods employ a single-view deterministic model which is sometimes insufficient to determine a single groundtruth shape because the back part is occluded. In this work, we first introduce a conditional generative network to model the uncertainty for single-view reconstruction. Then, we formulate the task of multi-view reconstruction as taking the intersection of the predicted shape spaces on each single image. We design new differentiable guidance including …
This work proposes a new method to accurately complete sparse LiDAR maps guided by RGB images. For autonomous vehicles and robotics the use of LiDAR is indispensable in order to achieve precise depth predictions. A multitude of applications depend on the awareness of their surroundings, and use depth cues to reason and react accordingly. On the one hand, monocular depth prediction methods fail to generate absolute and precise depth maps. On the other hand, stereoscopic approaches are still significantly outperformed by LiDAR based approaches. The goal of the depth completion task is to generat…
IIIT-Delhi（德里信息技术学院）；NEC Laboratories America, Inc.（NEC美国实验室公司）；University of California, San Diego（加利福尼亚大学圣迭戈分校）
Classical monocular Simultaneous Localization And Mapping (SLAM) and the recently emerging convolutional neural networks (CNNs) for monocular depth prediction represent two largely disjoint approaches towards building a 3D map of the surrounding environment. In this paper, we demonstrate that the coupling of these two by leveraging the strengths of each mitigates the other's shortcomings. Specifically, we propose a joint narrow and wide baseline based self-improving framework, where on the one hand the CNN-predicted depth is leveraged to perform pseudo RGB-D feature-based SLAM, leading to bett…
ETH Zurich（苏黎世联邦理工学院）；Technical University of Munich（慕尼黑工业大学）；Stanford University（斯坦福大学）
We introduce a new method that efficiently computes a set of viewpoints and trajectories for high-quality 3D reconstructions in outdoor environments. Our goal is to automatically explore an unknown area, and obtain a complete 3D scan of a region of interest (e.g., a large building). Images from a commodity RGB camera, mounted on an autonomously navigated quadcopter, are fed into a multi-view stereo reconstruction pipeline that produces high-quality results but is computationally expensive. In this setting, the scanning result is constrained by the restricted flight time of quadcopters. To this…
This manual is intended to provide a detailed description of the DIML/CVL RGB-D dataset. This dataset is comprised of 2M color images and their corresponding depth maps from a great variety of natural indoor and outdoor scenes. The indoor dataset was constructed using the Microsoft Kinect v2, while the outdoor dataset was built using the stereo cameras (ZED stereo camera and built-in stereo camera). Table I summarizes the details of our dataset, including acquisition, processing, format, and toolbox. Refer to Section II and III for more details.
Technical University of Munich（慕尼黑工业大学）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）
We introduce TransformerFusion, a transformer-based 3D scene reconstruction approach. From an input monocular RGB video, the video frames are processed by a transformer network that fuses the observations into a volumetric feature grid representing the scene; this feature grid is then decoded into an implicit 3D scene representation. Key to our approach is the transformer architecture that enables the network to learn to attend to the most relevant image frames for each 3D location in the scene, supervised only by the scene reconstruction task. Features are fused in a coarse-to-fine fashion, s…
The introduction of cheap RGB-D cameras, stereo cameras, and LIDAR devices has given the computer vision community 3D information that conventional RGB cameras cannot provide. This data is often stored as a point cloud. In this paper, we present a novel method to apply the concept of convolutional neural networks to this type of data. By creating a mapping of nearest neighbors in a dataset, and individually applying weights to spatial relationships between points, we achieve an architecture that works directly with point clouds, but closely resembles a convolutional neural net in both design a…
University of Electronic Science and Technology of China（电子科技大学）
3D object detection has become an emerging task in autonomous driving scenarios. Previous works process 3D point clouds using either projection-based or voxel-based models. However, both approaches contain some drawbacks. The voxel-based methods lack semantic information, while the projection-based methods suffer from numerous spatial information loss when projected to different views. In this paper, we propose the Stereo RGB and Deeper LIDAR (SRDL) framework which can utilize semantic and spatial information simultaneously such that the performance of network for 3D object detection can be im…
We present a single-shot system to recover surface geometry of objects with spatially-varying albedos, from images captured under a calibrated RGB photometric stereo setup---with three light directions multiplexed across different color channels in the observed RGB image. Since the problem is ill-posed point-wise, we assume that the albedo map can be modeled as piece-wise constant with a restricted number of distinct albedo values. We show that under ideal conditions, the shape of a non-degenerate local constant albedo surface patch can theoretically be recovered exactly. Moreover, we present …
University of California, San Diego（加州大学圣地亚哥分校）；Nuro Inc.（Nuro公司）；Columbia University（哥伦比亚大学）；Scale AI
We present Uncertainty-aware Cascaded Stereo Network (UCS-Net) for 3D reconstruction from multiple RGB images. Multi-view stereo (MVS) aims to reconstruct fine-grained scene geometry from multi-view images. Previous learning-based MVS methods estimate per-view depth using plane sweep volumes with a fixed depth hypothesis at each plane; this generally requires densely sampled planes for desired accuracy, and it is very hard to achieve high-resolution depth. In contrast, we propose adaptive thin volumes (ATVs); in an ATV, the depth hypothesis of each plane is spatially varying, which adapts to t…
In this paper, we propose a hybrid depth imaging system in which a polarisation camera is augmented by a second image from a standard digital camera. For this modest increase in equipment complexity over conventional shape-from-polarisation, we obtain a number of benefits that enable us to overcome longstanding problems with the polarisation shape cue. The stereo cue provides a depth map which, although coarse, is metrically accurate. This is used as a guide surface for disambiguation of the polarisation surface normal estimates using a higher order graphical model. In turn, these are used to …
Computer Vision Group, Technical University of Munich（慕尼黑工业大学计算机视觉组）；Erasmus Mundus Masters in Vision and Robotics (VIBOT)（伊拉斯谟世界计划视觉与机器人硕士项目（VIBOT））
A novel depth super-resolution approach for RGB-D sensors is presented. It disambiguates depth super-resolution through high-resolution photometric clues and, symmetrically, it disambiguates uncalibrated photometric stereo through low-resolution depth cues. To this end, an RGB-D sequence is acquired from the same viewing angle, while illuminating the scene from various uncalibrated directions. This sequence is handled by a variational framework which fits high-resolution shape and reflectance, as well as lighting, to both the low-resolution depth measurements and the high-resolution RGB ones. …
NeVStereo: A NeRF-Driven NVS-Stereo Architecture for High-Fidelity 3D Tasks
University of Washington（华盛顿大学）；University of Southern California（南加州大学）；CUHK-Shenzhen（香港中文大学（深圳））；USC Institute for Creative Technologies（南加州大学创意技术研究所）
University College London（伦敦大学学院）；Imperial College London（帝国理工学院）
Multispectral imaging (MSI) can provide information about tissue oxygenation, perfusion and potentially function during surgery. In this paper we present a novel, near real-time technique for intrinsic measurements of total haemoglobin (THb) and blood oxygenation (SO2) in tissue using only RGB images from a stereo laparoscope. The high degree of spectral overlap between channels makes inference of haemoglobin concentration challenging, non-linear and under constrained. We decompose the problem into two constrained linear sub-problems and show that with Tikhonov regularisation the estimation si…
