Pseudo-Stereo Inputs: A Solution to the Occlusion Challenge in Self-Supervised Stereo Matching
Shenyang Institute of Automation, Chinese Academy of Sciences（沈阳自动化研究所，中国科学院）；Liaoning Liaohe Laboratory（辽宁辽河实验室）；University of Chinese Academy of Sciences（中国科学院大学）；Key Laboratory on Intelligent Detection and Equipment Technology of Liaoning Province（辽宁省智能检测与装备技术重点实验室）
Self-supervised stereo matching holds great promise by eliminating the reliance on expensive ground-truth data. Its dominant paradigm, based on photometric consistency, is however fundamentally hindered by the occlusion challenge -- an issue that persists regardless of network architecture. The essential insight is that for any occluders, valid feedback signals can only be derived from the unoccluded areas on one side of the occluder. Existing methods attempt to address this by focusing on the erroneous feedback from the other side, either by identifying and removing it, or by introducing additional regularities for correction on that basis. Nevertheless, these approaches have failed to provide a complete solution. This work proposes a more fundamental solution. The core idea is to transform the fixed state of one-sided valid and one-sided erroneous signals into a probabilistic acquisition of valid feedback from both sides of an occluder. This is achieved through a complete framework, centered on a pseudo-stereo inputs strategy that decouples the input and feedback, without introducing any additional constraints. Qualitative results visually demonstrate that the occlusion problem is resolved, manifested by fully symmetrical and identical performance on both flanks of occluding objects. Quantitative experiments thoroughly validate the significant performance improvements resulting from solving the occlusion challenge.
Localizing stereo boundaries is difficult because matching cues are absent in the occluded regions that are adjacent to them. We introduce an energy and level-set optimizer that improves boundaries by encoding the essential geometry of occlusions: The spatial extent of an occlusion must equal the amplitude of the disparity jump that causes it. In a collection of figure-ground scenes from Middlebury and Falling Things stereo datasets, the model provides more accurate boundaries than previous occlusion-handling techniques.
Recently, there are emerging many stereo matching methods for autonomous driving based on unsupervised learning. Most of them take advantage of reconstruction losses to remove dependency on disparity groundtruth. Occlusion handling is a challenging problem in stereo matching, especially for unsupervised methods. Previous unsupervised methods failed to take full advantage of geometry properties in occlusion handling. In this paper, we introduce an effective way to detect occlusion regions and propose a novel unsupervised training strategy to deal with occlusion that only uses the predicted left…
In this paper, we contrive a stereo matching algorithm with careful handling of disparity, discontinuity and occlusion. This algorithm works a worldwide matching stereo model which is based on minimization of energy. The global energy comprises two terms, firstly the data term and secondly the smoothness term. The data term is approximated by a color-weighted correlation, then refined in obstruct and low-texture areas in many applications of hierarchical loopy belief propagation algorithm. The results during the experiment are evaluated on the Middlebury data sets, showing that out algorithm i…
Localizing stereo boundaries and predicting nearby disparities are difficult because stereo boundaries induce occluded regions where matching cues are absent. Most modern computer vision algorithms treat occlusions secondarily (e.g., via left-right consistency checks after matching) or rely on high-level cues to improve nearby disparities (e.g., via deep networks and large training sets). They ignore the geometry of stereo occlusions, which dictates that the spatial extent of occlusion must equal the amplitude of the disparity jump that causes it. This paper introduces an energy and level-set …
In this paper, we extend the standard belief propagation (BP) sequential technique proposed in the tree-reweighted sequential method to the fully connected CRF models with the geodesic distance affinity. The proposed method has been applied to the stereo matching problem. Also a new approach to the BP marginal solution is proposed that we call one-view occlusion detection (OVOD). In contrast to the standard winner takes all (WTA) estimation, the proposed OVOD solution allows to find occluded regions in the disparity map and simultaneously improve the matching result. As a result we can perform…
Detecting the occlusion from stereo images or video frames is important to many computer vision applications. Previous efforts focus on bundling it with the computation of disparity or optical flow, leading to a chicken-and-egg problem. In this paper, we leverage convolutional neural network to liberate the occlusion detection task from the interleaved, traditional calculation framework. We propose a Symmetric Network (SymmNet) to directly exploit information from an image pair, without estimating disparity or motion in advance. The proposed network is structurally left-right symmetric to lear…
Robotics Institute, Carnegie Mellon University（卡内基梅隆大学机器人研究所）
Stereo reconstruction models trained on small images do not generalize well to high-resolution data. Training a model on high-resolution image size faces difficulties of data availability and is often infeasible due to limited computing resources. In this work, we present the Occlusion-aware Recurrent binocular Stereo matching (ORStereo), which deals with these issues by only training on available low disparity range stereo images. ORStereo generalizes to unseen high-resolution images with large disparity ranges by formulating the task as residual updates and refinements of an initial predicti…
SED-MVS: Segmentation-Driven and Edge-Aligned Deformation Multi-View Stereo with Depth Restoration and Occlusion Constraint
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；School of Electrical Engineering and Computer Science, The University of Queensland（昆士兰大学电气工程与计算机科学学院）；Hunan Police Academy（湖南警察学院）；Cooperative MediaNet Innovation Center, Shanghai Jiao Tong University（上海交通大学协同媒体网络创新中心）；School of Information Science & Engineering, Lanzhou University（兰州大学信息科学与工程学院）；Agricultural Information Institute, Chinese Academy of Agricultural Sciences（中国农业科学院农业信息研究所）
Toyota Technological Institute at Chicago（芝加哥丰田技术研究院）
In this paper we present a novel slanted-plane MRF model which reasons jointly about occlusion boundaries as well as depth. We formulate the problem as the one of inference in a hybrid MRF composed of both continuous (i.e., slanted 3D planes) and discrete (i.e., occlusion boundaries) random variables. This allows us to define potentials encoding the ownership of the pixels that compose the boundary between segments, as well as potentials encoding which junctions are physically possible. Our approach outperforms the state-of-the-art on Middlebury high resolution imagery as well as in the more c…
Stereo matching is a key component of autonomous driving perception. Recent unsupervised stereo matching approaches have received adequate attention due to their advantage of not requiring disparity ground truth. These approaches, however, perform poorly near occlusions. To overcome this drawback, in this paper, we propose CoT-Stereo, a novel unsupervised stereo matching approach. Specifically, we adopt a co-teaching framework where two networks interactively teach each other about the occlusions in an unsupervised fashion, which greatly improves the robustness of unsupervised stereo matching.…
German Research Center for Artificial Intelligence - DFKI（德国人工智能研究中心（DFKI））；Birla Institute of Technology and Science - BITS Pilani（贝拉理工学院（BITS Pilani））；University of Kaiserslautern - TUK（凯泽斯劳滕大学（TUK））
In the last few years, convolutional neural networks (CNNs) have demonstrated increasing success at learning many computer vision tasks including dense estimation problems such as optical flow and stereo matching. However, the joint prediction of these tasks, called scene flow, has traditionally been tackled using slow classical methods based on primitive assumptions which fail to generalize. The work presented in this paper overcomes these drawbacks efficiently (in terms of speed and accuracy) by proposing PWOC-3D, a compact CNN architecture to predict scene flow from stereo image sequences i…
Zhejiang University（浙江大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
Pseudo-LiDAR 3D detectors have made remarkable progress in monocular 3D detection by enhancing the capability of perceiving depth with depth estimation networks, and using LiDAR-based 3D detection architectures. The advanced stereo 3D detectors can also accurately localize 3D objects. The gap in image-to-image generation for stereo views is much smaller than that in image-to-LiDAR generation. Motivated by this, we propose a Pseudo-Stereo 3D detection framework with three novel virtual view generation methods, including image-level generation, feature-level generation, and feature-clone, for de…
Back to the Future Cyclopean Stereo: a human perception approach combining deep and geometric constraints
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（ICMC-USP））；Courant Institute of Mathematical Sciences, New York University (NYU)（纽约大学柯朗数学科学研究所（NYU））；Instituto de Matemática Pura e Aplicada (IMPA)（纯数学与应用数学研究所（IMPA））
Perception and localization are essential for autonomous delivery vehicles, mostly estimated from 3D LiDAR sensors due to their precise distance measurement capability. This paper presents a strategy to obtain the real-time pseudo point cloud instead of the laser sensor from the image sensor. We propose an approach to use different depth estimators to obtain pseudo point clouds like LiDAR to obtain better performance. Moreover, the training and validating strategy of the depth estimator has adopted stereo imagery data to estimate more accurate depth estimation as well as point cloud results. O…
Technical University of Munich（慕尼黑工业大学）；Sun Yat-sen University（中山大学）
The proposal of Pseudo-Lidar representation has significantly narrowed the gap between visual-based and active Lidar-based 3D object detection. However, current researches exclusively focus on pushing the accuracy improvement of Pseudo-Lidar by taking the advantage of complex and time-consuming neural networks. Seldom explore the profound characteristics of Pseudo-Lidar representation to obtain the promoting opportunities. In this paper, we dive deep into the pseudo Lidar representation and argue that the performance of 3D object detection is not fully dependent on the high precision stereo de…
In this paper we present ActiveStereoNet, the first deep learning solution for active stereo systems. Due to the lack of ground truth, our method is fully self-supervised, yet it produces precise depth with a subpixel precision of $1/30th$ of a pixel; it does not suffer from the common over-smoothing issues; it preserves the edges; and it explicitly handles occlusions. We introduce a novel reconstruction loss that is more robust to noise and texture-less patches, and is invariant to illumination changes. The proposed loss is optimized using a window-based cost aggregation with an adaptive supp…
In this paper, we present a decomposition model for stereo matching to solve the problem of excessive growth in computational cost (time and memory cost) as the resolution increases. In order to reduce the huge cost of stereo matching at the original resolution, our model only runs dense matching at a very low resolution and uses sparse matching at different higher resolutions to recover the disparity of lost details scale-by-scale. After the decomposition of stereo matching, our model iteratively fuses the sparse and dense disparity maps from adjacent scales with an occlusion-aware mask. A re…
Stereo Anywhere: Robust Zero-Shot Deep Stereo Matching Even Where Either Stereo or Mono Fail
Advanced Research Center on Electronic System (ARCES)（电子系统高级研究中心）；Department of Computer Science and Engineering (DISI)（计算机科学与工程系）；University of Bologna（博洛尼亚大学）
Omnidirectional multi-view stereo (MVS) vision is attractive for its ultra-wide field-of-view (FoV), enabling machines to perceive 360° 3D surroundings. However, the existing solutions require expensive dense depth labels for supervision, making them impractical in real-world applications. In this paper, we propose the first unsupervised omnidirectional MVS framework based on multiple fisheye images. To this end, we project all images to a virtual view center and composite two panoramic images with spherical geometry from two pairs of back-to-back fisheye images. The two 360° images formulate …
In this article, we present a very lightweight neural network architecture, trained on stereo data pairs, which performs view synthesis from one single image. With the growing success of multi-view formats, this problem is indeed increasingly relevant. The network returns a prediction built from disparity estimation, which fills in wrongly predicted regions using a occlusion handling technique. To do so, during training, the network learns to estimate the left-right consistency structural constraint on the pair of stereo input images, to be able to replicate it at test time from one single ima…
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…
In this paper, we present confidence inference approachin an unsupervised way in stereo matching. Deep Neu-ral Networks (DNNs) have recently been achieving state-of-the-art performance. However, it is often hard to tellwhether the trained model was making sensible predictionsor just guessing at random. To address this problem, westart from a probabilistic interpretation of theL1loss usedin stereo matching, which inherently assumes an indepen-dent and identical (aka i.i.d.) Laplacian distribution. Weshow that with the newly introduced dense confidence map,the identical assumption is relaxed. In…
University of Michigan（密歇根大学）；TuSimple, Inc.（图森未来公司）
Dense stereo matching with deep neural networks is of great interest to the research community. Existing stereo matching networks typically use slow and computationally expensive 3D convolutions to improve the performance, which is not friendly to real-world applications such as autonomous driving. In this paper, we propose the Efficient Stereo Network (ESNet), which achieves high performance and efficient inference at the same time. ESNet relies only on 2D convolution and computes multi-scale cost volume efficiently using a warping-based method to improve the performance in regions with fine-…
Intelligent Media Institute, Communication University of Zhejiang（浙江传媒学院智能媒体研究院）；Key Lab of Film and TV Media Technology of Zhejiang Province（浙江省影视媒体技术重点实验室）；Software Engineering School, Zhejiang University（浙江大学软件学院）
Mixed reality applications often require virtual objects that are partly occluded by real objects. However, previous research and commercial products have limitations in terms of performance and efficiency. To address these challenges, we propose a novel depth contour occlusion (DCO) algorithm. The proposed method is based on the sensitivity of contour occlusion and a binocular stereoscopic vision device. In this method, a depth contour map is combined with a sparse depth map obtained from a two-stage adaptive filter area stereo matching algorithm and the depth contour information of the objec…
SenseTime Research（商汤科技研究院）；The Chinese University of Hong Kong（香港中文大学）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Shanghai AI Laboratory（上海人工智能实验室）；Qing Yuan Research Institute, Shanghai Jiao Tong University（上海交通大学清源研究院）
Recently, records on stereo matching benchmarks are constantly broken by end-to-end disparity networks. However, the domain adaptation ability of these deep models is quite poor. Addressing such problem, we present a novel domain-adaptive pipeline called AdaStereo that aims to align multi-level representations for deep stereo matching networks. Compared to previous methods for adaptive stereo matching, our AdaStereo realizes a more standard, complete and effective domain adaptation pipeline. Firstly, we propose a non-adversarial progressive color transfer algorithm for input image-level alignm…
Most autonomous vehicles are equipped with LiDAR sensors and stereo cameras. The former is very accurate but generates sparse data, whereas the latter is dense, has rich texture and color information but difficult to extract robust 3D representations from. In this paper, we propose a novel data fusion algorithm to combine accurate point clouds with dense but less accurate point clouds obtained from stereo pairs. We develop a framework to integrate this algorithm into various 3D object detection methods. Our framework starts with 2D detections from both of the RGB images, calculates frustums an…
Cornell University（康奈尔大学）；The Ohio State University（俄亥俄州立大学）
Detecting objects such as cars and pedestrians in 3D plays an indispensable role in autonomous driving. Existing approaches largely rely on expensive LiDAR sensors for accurate depth information. While recently pseudo-LiDAR has been introduced as a promising alternative, at a much lower cost based solely on stereo images, there is still a notable performance gap. In this paper we provide substantial advances to the pseudo-LiDAR framework through improvements in stereo depth estimation. Concretely, we adapt the stereo network architecture and loss function to be more aligned with accurate depth…
Learning Intra-view and Cross-view Geometric Knowledge for Stereo Matching
School of Electrical and Electronic Engineering, Nanyang Technological University（南洋理工大学电气与电子工程学院）；Institute for Infocomm Research, A*STAR（新加坡科技研究局信息通信研究院）
The performance of PatchMatch-based multi-view stereo algorithms depends heavily on the source views selected for computing matching costs. Instead of modeling the visibility of different views, most existing approaches handle occlusions in an ad-hoc manner. To address this issue, we propose a novel visibility-guided pixelwise view selection scheme in this paper. It progressively refines the set of source views to be used for each pixel in the reference view based on visibility information provided by already validated solutions. In addition, the Artificial Multi-Bee Colony (AMBC) algorithm is…
We present a method for detecting and mapping trees in noisy stereo camera point clouds, using a learned 3-D object detector. Inspired by recent advancements in 3-D object detection using a pseudo-lidar representation for stereo data, we train a PointRCNN detector to recognize trees in forest-like environments. We generate detector training data with a novel automatic labeling process that clusters a fused global point cloud. This process annotates large stereo point cloud training data sets with minimal user supervision, and unlike previous pseudo-lidar detection pipelines, requires no 3-D gr…
Tissue deformation during the surgery may significantly decrease the accuracy of surgical navigation systems. In this paper, we propose an approach to estimate the deformation of tissue surface from stereo videos in real-time, which is capable of handling occlusion, smooth surface and fast deformation. We first use a stereo matching method to extract depth information from stereo video frames and generate the tissue template, and then estimate the deformation of the obtained template by minimizing ICP, ORB feature matching and as-rigid-as-possible (ARAP) costs. The main novelties are twofold: …
Technische Universität Kaiserslautern（凯泽斯劳滕工业大学）；DFKI -- German Research Center for Artificial Intelligence（德国人工智能研究中心）
In recent years, deep neural networks showed their exceeding capabilities in addressing many computer vision tasks including scene flow prediction. However, most of the advances are dependent on the availability of a vast amount of dense per pixel ground truth annotations, which are very difficult to obtain for real life scenarios. Therefore, synthetic data is often relied upon for supervision, resulting in a representation gap between the training and test data. Even though a great quantity of unlabeled real world data is available, there is a huge lack in self-supervised methods for scene fl…
Monocular Depth Guided Occlusion-Aware Disparity Refinement via Semi-supervised Learning in Laparoscopic Images
School of Life Science and Technology, Harbin Institute of Technology（生命科学与技术学院，哈尔滨工业大学）；State Key Laboratory of Robotics and System, Harbin Institute of Technology（机器人与系统国家重点实验室，哈尔滨工业大学）
CogStereo: Neural Stereo Matching with Implicit Spatial Cognition Embedding
Robotics and Computer Vision (RCV) Laboratory, Southern University of Science and Technology (SUSTech)（机器人与计算机视觉实验室，南方科技大学）；International Digital Economy Academy (IDEA)（国际数字经济学院）；School of Automation Science and Engineering, Xi’an Jiaotong University (XJTU)（自动化科学与工程学院，西安交通大学）
DMS:Diffusion-Based Multi-Baseline Stereo Generation for Improving Self-Supervised Depth Estimation
Institute of Science Tokyo, Japan（东京科学研究所）；Sony Semiconductor Solutions Group, Japan（日本索尼半导体解决方案集团）；Nanyang Technological University, Singapore（南洋理工大学）
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）
Self-supervised monocular depth estimation, aiming to learn scene depths from single images in a self-supervised manner, has received much attention recently. In spite of recent efforts in this field, how to learn accurate scene depths and alleviate the negative influence of occlusions for self-supervised depth estimation, still remains an open problem. Addressing this problem, we firstly empirically analyze the effects of both the continuous and discrete depth constraints which are widely used in the training process of many existing works. Then inspired by the above empirical analysis, we pr…
Deep networks for stereo matching typically leverage 2D or 3D convolutional encoder-decoder architectures to aggregate cost and regularize the cost volume for accurate disparity estimation. Due to content-insensitive convolutions and down-sampling and up-sampling operations, these cost aggregation mechanisms do not take full advantage of the information available in the images. Disparity maps suffer from over-smoothing near occlusion boundaries, and erroneous predictions in thin structures. In this paper, we show how deep adaptive filtering and differentiable semi-global aggregation can be int…
SenseTime Group Limited（商汤科技集团）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Chinese University of Hong Kong（香港中文大学）；ShanghaiTech University（上海科技大学）
Recently, records on stereo matching benchmarks are constantly broken by end-to-end disparity networks. However, the domain adaptation ability of these deep models is quite limited. Addressing such problem, we present a novel domain-adaptive approach called AdaStereo that aims to align multi-level representations for deep stereo matching networks. Compared to previous methods, our AdaStereo realizes a more standard, complete and effective domain adaptation pipeline. Firstly, we propose a non-adversarial progressive color transfer algorithm for input image-level alignment. Secondly, we design a…
