Depth-Map Generation using Pixel Matching in Stereoscopic Pair of Images
Insight Centre for Data Analytics（洞察数据分析中心）；National University of Ireland（爱尔兰国立大学）；Software Research Institute（软件研究院）；Athlone Institute of Technology（阿斯隆理工学院）
Asra Aslam, Mohd. Samar Ansari
备注：
5 Pages, pre-print, to be submitted to a conference later
Modern day multimedia content generation and dissemination is moving towards the presentation of more and more `realistic' scenarios. The switch from 2-dimensional (2D) to 3-dimensional (3D) has been a major driving force in that direction. Over the recent past, a large number of approaches have been proposed for creating 3D images/videos most of which are based on the generation of depth-maps. This paper presents a new algorithm for obtaining depth information pertaining to a depicted scene from a set of available pair of stereoscopic images. The proposed algorithm performs a pixel-to-pixel matching of the two images in the stereo pair for estimation of depth. It is shown that the obtained depth-maps show improvements over the reported counterparts.
Although event-based cameras are already commercially available. Vision algorithms based on them are still not common. As a consequence, there are few Hardware Accelerators for them. In this work we present some experiments to create FPGA accelerators for a well-known vision algorithm using event-based cameras. We present a stereo matching algorithm to create a stream of disparity events disparity map and implement several accelerators using the Intel FPGA OpenCL tool-chain. The results show that multiple designs can be easily tested and that a performance speedup of more than 8x can be achiev…
Field Tracking of Insects Using a Stereoscopic Event-Based Camera Setup
University of New South Wales（新南威尔士大学）；Wageningen University & Research（瓦赫宁根大学及研究中心）；DEVCOM Army Research Lab（DEVCOM陆军研究实验室）；University of Queensland（昆士兰大学）
Singapore-ETH Center（新加坡-ETH中心）；ETH Zuerich（苏黎世联邦理工学院）；University of Melbourne（墨尔本大学）
Recently developed automatic dense image matching algorithms are now being implemented for DSM/DTM production, with their pixel-level surface generation capability offering the prospect of partially alleviating the need for manual and semi-automatic stereoscopic measurements. In this paper, five commercial/public software packages for 3D surface generation are evaluated, using 5cm GSD imagery recorded from a UAV. Generated surface models are assessed against point clouds generated from mobile LiDAR and manual stereoscopic measurements. The software packages considered are APS, MICMAC, SURE, Pi…
Univ Rennes（雷恩大学）；Inserm（法国国家健康与医学研究院）；Neotec Vision（Neotec视觉公司）；ECAM Rennes - Louis de Broglie（雷恩ECAM-路易·德布罗意学院）
In the context of a localization and tracking application, we developed a stereo vision system based on cheap low-resolution 80x60 pixels thermal cameras. We proposed a threefold sub-pixel stereo matching framework (called ST for Subpixel Thermal): 1) robust features extraction method based on phase congruency, 2) rough matching of these features in pixel precision, and 3) refined matching in sub-pixel accuracy based on local phase coherence. We performed experiments on our very low-resolution thermal images (acquired using a stereo system we manufactured) as for high-resolution images from a …
Graduate School of Engineering Science, Osaka University（大阪大学工程科学研究生院）；PRESTO, Japan Science and Technology Agency（日本科学技术振兴机构PRESTO项目）
Stereoscopic projection mapping (PM) allows a user to see a three-dimensional (3D) computer-generated (CG) object floating over physical surfaces of arbitrary shapes around us using projected imagery. However, the current stereoscopic PM technology only satisfies binocular cues and is not capable of providing correct focus cues, which causes a vergence--accommodation conflict (VAC). Therefore, we propose a multifocal approach to mitigate VAC in stereoscopic PM. Our primary technical contribution is to attach electrically focus-tunable lenses (ETLs) to active shutter glasses to control both ver…
Nokia Technologies（诺基亚技术公司）；Aalto University（阿尔托大学）
This paper addresses the problem of determining dense pixel correspondences between two images and its application to geometric correspondence verification in image retrieval. The main contribution is a geometric correspondence verification approach for re-ranking a shortlist of retrieved database images based on their dense pair-wise matching with the query image at a pixel level. We determine a set of cyclically consistent dense pixel matches between the pair of images and evaluate local similarity of matched pixels using neural network based image descriptors. Final re-ranking is based on a…
In this paper, we introduce a self-supervised approach for video object segmentation without human labeled data.Specifically, we present Robust Pixel-level Matching Net-works (RPM-Net), a novel deep architecture that matches pixels between adjacent frames, using only color information from unlabeled videos for training. Technically, RPM-Net can be separated in two main modules. The embed-ding module first projects input images into high dimensional embedding space. Then the matching module with deformable convolution layers matches pixels between reference and target frames based on the embedd…
Semi-supervised video object segmentation (VOS) aims to track the designated objects present in the initial frame of a video at the pixel level. To fully exploit the appearance information of an object, pixel-level feature matching is widely used in VOS. Conventional feature matching runs in a surjective manner, i.e., only the best matches from the query frame to the reference frame are considered. Each location in the query frame refers to the optimal location in the reference frame regardless of how often each reference frame location is referenced. This works well in most cases and is robus…
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…
DFKI - German Research Center for Artificial Intelligence（德国人工智能研究中心）；Birla Institute of Technology and Science (BITS) Pilani（皮拉尼伯拉理工学院（BITS皮拉尼））；University of Applied Sciences Mannheim（曼海姆应用科学大学）；University of Kaiserslautern - TUK（凯泽斯劳滕大学（TUK））
Dense pixel matching is required for many computer vision algorithms such as disparity, optical flow or scene flow estimation. Feature Pyramid Networks (FPN) have proven to be a suitable feature extractor for CNN-based dense matching tasks. FPN generates well localized and semantically strong features at multiple scales. However, the generic FPN is not utilizing its full potential, due to its reasonable but limited localization accuracy. Thus, we present ResFPN -- a multi-resolution feature pyramid network with multiple residual skip connections, where at any scale, we leverage the information…
DFKI - German Research Center for Artificial Intelligence（德国人工智能研究中心）；BMW Group（宝马集团）
Training a deep neural network is a non-trivial task. Not only the tuning of hyperparameters, but also the gathering and selection of training data, the design of the loss function, and the construction of training schedules is important to get the most out of a model. In this study, we perform a set of experiments all related to these issues. The model for which different training strategies are investigated is the recently presented SDC descriptor network (stacked dilated convolution). It is used to describe images on pixel-level for dense matching tasks. Our work analyzes SDC in more detail…
Yonsei University（延世大学）；Korea Institute of Science and Technology (KIST)（韩国科学技术研究院）
Feature similarity matching, which transfers the information of the reference frame to the query frame, is a key component in semi-supervised video object segmentation. If surjective matching is adopted, background distractors can easily occur and degrade the performance. Bijective matching mechanisms try to prevent this by restricting the amount of information being transferred to the query frame, but have two limitations: 1) surjective matching cannot be fully leveraged as it is transformed to bijective matching at test time; and 2) test-time manual tuning is required for searching the optim…
Semi-supervised video object segmentation (VOS) aims to segment arbitrary target objects in video when the ground truth segmentation mask of the initial frame is provided. Due to this limitation of using prior knowledge about the target object, feature matching, which compares template features representing the target object with input features, is an essential step. Recently, pixel-level matching (PM), which matches every pixel in template features and input features, has been widely used for feature matching because of its high performance. However, despite its effectiveness, the information…
We propose a novel video object segmentation algorithm based on pixel-level matching using Convolutional Neural Networks (CNN). Our network aims to distinguish the target area from the background on the basis of the pixel-level similarity between two object units. The proposed network represents a target object using features from different depth layers in order to take advantage of both the spatial details and the category-level semantic information. Furthermore, we propose a feature compression technique that drastically reduces the memory requirements while maintaining the capability of fea…
The matching function for the problem of stereo reconstruction or optical flow has been traditionally designed as a function of the distance between the features describing matched pixels. This approach works under assumption, that the appearance of pixels in two stereo cameras or in two consecutive video frames does not change dramatically. However, this might not be the case, if we try to match pixels over a large interval of time. In this paper we propose a method, which learns the matching function, that automatically finds the space of allowed changes in visual appearance, such as due to …
University of Oxford（牛津大学）；National University of Defense Technology（国防科技大学）；Zhejiang University（浙江大学）；Inception Institute of Artificial Intelligence（起源人工智能研究院）；University of Edinburgh（爱丁堡大学）；Durham University（杜伦大学）；Wuhan University（武汉大学）
Accurately describing and detecting 2D and 3D keypoints is crucial to establishing correspondences across images and point clouds. Despite a plethora of learning-based 2D or 3D local feature descriptors and detectors having been proposed, the derivation of a shared descriptor and joint keypoint detector that directly matches pixels and points remains under-explored by the community. This work takes the initiative to establish fine-grained correspondences between 2D images and 3D point clouds. In order to directly match pixels and points, a dual fully convolutional framework is presented that m…
École Polytechnique（巴黎综合理工学院）；CNRS（法国国家科学研究中心）；IP Paris（巴黎理工学院）；AMIAD；École Nationale des Ponts et Chaussées（巴黎路桥学院）；UGE（巴黎第八大学）；UC Berkeley（加州大学伯克利分校）
Differentiable Registration of Images and LiDAR Point Clouds with VoxelPoint-to-Pixel Matching
Tsinghua University（清华大学）；Beijing Academy of Artificial Intelligence（北京人工智能研究院）；New York University Abu Dhabi（纽约大学阿布扎比分校）；Wayne State University（韦恩州立大学）
Hyperspectral target detection is a pixel-level recognition problem. Given a few target samples, it aims to identify the specific target pixels such as airplane, vehicle, ship, from the entire hyperspectral image. In general, the background pixels take the majority of the image and complexly distributed. As a result, the datasets are weak annotated and extremely imbalanced. To address these problems, a spectral mixing based self-supervised paradigm is designed for hyperspectral data to obtain an effective feature representation. The model adopts a spectral similarity based matching network fra…
We study the effect of adversarial perturbations of images on deep stereo matching networks for the disparity estimation task. We present a method to craft a single set of perturbations that, when added to any stereo image pair in a dataset, can fool a stereo network to significantly alter the perceived scene geometry. Our perturbation images are "universal" in that they not only corrupt estimates of the network on the dataset they are optimized for, but also generalize to different architectures trained on different datasets. We evaluate our approach on multiple benchmark datasets where our p…
Southwest Jiaotong University（西南交通大学）；University of Trento（特伦托大学）；Purdue University（普渡大学）；Fondazione Bruno Kessler（布鲁诺·凯斯勒基金会）
While image registration has been studied in remote sensing community for decades, registering multimodal data [e.g., optical, LiDAR, SAR, and map] remains a challenging problem because of significant nonlinear intensity differences between such data. To address this problem, this paper presents a fast and robust matching framework integrating local descriptors for multimodal registration. In the proposed framework, a local descriptor, such as Histogram of Oriented Gradient (HOG), Local Self Similarity (LSS), or Speeded-Up Robust Feature (SURF), is first extracted at each pixel to form a pixel…
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…
Video object segmentation, aiming to segment the foreground objects given the annotation of the first frame, has been attracting increasing attentions. Many state-of-the-art approaches have achieved great performance by relying on online model updating or mask-propagation techniques. However, most online models require high computational cost due to model fine-tuning during inference. Most mask-propagation based models are faster but with relatively low performance due to failure to adapt to object appearance variation. In this paper, we are aiming to design a new model to make a good balance …
Deep learning techniques have enabled rapid progress in monocular depth estimation, but their quality is limited by the ill-posed nature of the problem and the scarcity of high quality datasets. We estimate depth from a single camera by leveraging the dual-pixel auto-focus hardware that is increasingly common on modern camera sensors. Classic stereo algorithms and prior learning-based depth estimation techniques under-perform when applied on this dual-pixel data, the former due to too-strong assumptions about RGB image matching, and the latter due to not leveraging the understanding of optics …
Beijing Institute of Technology（北京理工大学）；The University of Adelaide（阿德莱德大学）；Australian Centre for Robotic Vision（澳大利亚机器人视觉中心）
We propose a fast, accurate matching method for estimating dense pixel correspondences across scenes. It is a challenging problem to estimate dense pixel correspondences between images depicting different scenes or instances of the same object category. While most such matching methods rely on hand-crafted features such as SIFT, we learn features from a large amount of unlabeled image patches using unsupervised learning. Pixel-layer features are obtained by encoding over the dictionary, followed by spatial pooling to obtain patch-layer features. The learned features are then seamlessly embedde…
Technical University of Munich（慕尼黑工业大学）；CIIRC Czech Technical University in Prague（布拉格捷克技术大学CIIRC）
The classical matching pipeline used for visual localization typically involves three steps: (i) local feature detection and description, (ii) feature matching, and (iii) outlier rejection. Recently emerged correspondence networks propose to perform those steps inside a single network but suffer from low matching resolution due to the memory bottleneck. In this work, we propose a new perspective to estimate correspondences in a detect-to-refine manner, where we first predict patch-level match proposals and then refine them. We present Patch2Pix, a novel refinement network that refines match pr…
Beihang University（北京航空航天大学）；RIKEN AIP（理化学研究所先进智能项目中心）；The University of Tokyo（东京大学）；Griffith University（格里菲斯大学）；University of York（约克大学）
Despite recent stereo matching networks achieving impressive performance given sufficient training data, they suffer from domain shifts and generalize poorly to unseen domains. We argue that maintaining feature consistency between matching pixels is a vital factor for promoting the generalization capability of stereo matching networks, which has not been adequately considered. Here we address this issue by proposing a simple pixel-wise contrastive learning across the viewpoints. The stereo contrastive feature loss function explicitly constrains the consistency between learned features of match…
Explicit representations of the global match distributions of pixel-wise correspondences between pairs of images are desirable for uncertainty estimation and downstream applications. However, the computation of the match density for each pixel may be prohibitively expensive due to the large number of candidates. In this paper, we propose Hierarchical Discrete Distribution Decomposition (HD^3), a framework suitable for learning probabilistic pixel correspondences in both optical flow and stereo matching. We decompose the full match density into multiple scales hierarchically, and estimate the l…
Diachronic Stereo Matching for Multi-Date Satellite Imagery
IIE, Facultad de Ingeniería, Universidad de la República（乌拉圭国家工程院，工程学院，乌拉圭共和国大学）；Politecnico di Torino（托斯卡纳理工学院）；Eurecat, Centre Tecnologic de Catalunya（欧洲催化中心，加泰罗尼亚技术中心）；AMIAD, Pôle Recherche（AMIAD研究极）；Universite Paris-Saclay, CNRS, ENS Paris-Saclay, Centre Borelli（巴黎萨克雷大学，国家科学研究中心，巴黎萨克雷高等师范学校，博雷利中心）；Institut Universitaire de France（法国国家科学研究中心）
Multi-Label Stereo Matching for Transparent Scene Depth Estimation
Beijing Key Laboratory of Intelligent Information Technology, School of Computer Science & Technology, Beijing Institute of Technology, China（北京智能信息科技重点实验室，计算机科学与技术学院，北京理工大学，中国）；Guangdong Laboratory of Machine Perception and Intelligent Computing, Shenzhen MSU-BIT University, China（广东机器感知与智能计算实验室，深圳MSU-BIT大学，中国）
Beihang University（北京航空航天大学）；Hangzhou Innovation Institute, Beihang University（北京航空航天大学杭州创新研究院）；Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Meituan（美团）
Panoptic Narrative Grounding (PNG) is an emerging task whose goal is to segment visual objects of things and stuff categories described by dense narrative captions of a still image. The previous two-stage approach first extracts segmentation region proposals by an off-the-shelf panoptic segmentation model, then conducts coarse region-phrase matching to ground the candidate regions for each noun phrase. However, the two-stage pipeline usually suffers from the performance limitation of low-quality proposals in the first stage and the loss of spatial details caused by region feature pooling, as w…
A novel image matching method is proposed that utilizes learned features extracted by an off-the-shelf deep neural network to obtain a promising performance. The proposed method uses pre-trained VGG architecture as a feature extractor and does not require any additional training specific to improve matching. Inspired by well-established concepts in the psychology area, such as the Mental Rotation paradigm, an initial warping is performed as a result of a preliminary geometric transformation estimate. These estimates are simply based on dense matching of nearest neighbors at the terminal layer …
Recently proposed DNN-based stereo matching methods that learn priors directly from data are known to suffer a drastic drop in accuracy in new environments. Although supervised approaches with ground truth disparity maps often work well, collecting them in each deployment environment is cumbersome and costly. For this reason, many unsupervised domain adaptation methods based on image-to-image translation have been proposed, but these methods do not preserve the geometric structure of a stereo image pair because the image-to-image translation is applied to each view separately. To address this …
Layer-Wise Feature Metric of Semantic-Pixel Matching for Few-Shot Learning
School of Computer Science and Technology, Guangdong University of Technology（广东工业大学计算机科学与技术学院）；Zhejiang Institute of Optoelectronics（浙江光电研究所）；School of Computer Science and Engineering, Huizhou University（惠州学院计算机科学与工程学院）；School of Information Science and Technology, Guangdong University of Foreign Studies（广东外语外贸大学信息科学与技术学院）；School of Physics, Maths and Computing, University of Western Australia（西澳大学物理、数学与计算学院）
Several leading methods on public benchmarks for depth-from-stereo rely on memory-demanding 4D cost volumes and computationally intensive 3D convolutions for feature matching. We suggest a new way to process the 4D cost volume where we merge two different concepts in one deeply integrated framework to achieve a symbiotic relationship. A feature matching part is responsible for identifying matching pixels pairs along the baseline while a concurrent image volume part is inspired by depth-from-mono CNNs. However, instead of predicting depth directly from image features, it provides additional con…
Wayne State University（韦恩州立大学）；University of Maryland, College Park（马里兰大学学院公园分校）
Learning to generate 3D point clouds without 3D supervision is an important but challenging problem. Current solutions leverage various differentiable renderers to project the generated 3D point clouds onto a 2D image plane, and train deep neural networks using the per-pixel difference with 2D ground truth images. However, these solutions are still struggling to fully recover fine structures of 3D shapes, such as thin tubes or planes. To resolve this issue, we propose an unsupervised approach for 3D point cloud generation with fine structures. Specifically, we cast 3D point cloud learning as a…
The complementary fusion of light detection and ranging (LiDAR) data and image data is a promising but challenging task for generating high-precision and high-density point clouds. This study proposes an innovative LiDAR-guided stereo matching approach called LiDAR-guided stereo matching (LGSM), which considers the spatial consistency represented by continuous disparity or depth changes in the homogeneous region of an image. The LGSM first detects the homogeneous pixels of each LiDAR projection point based on their color or intensity similarity. Next, we propose a riverbed enhancement function…
