Hand-held light field (LF) cameras have unique advantages in computer vision such as 3D scene reconstruction and depth estimation. However, the related applications are limited by the ultra-small baseline, e.g., leading to the extremely low depth resolution in reconstruction. To solve this problem, we propose to rectify LF to obtain a large baseline. Specifically, the proposed method aligns two LFs captured by two hand-held LF cameras with a random relative pose, and extracts the corresponding row-aligned sub-aperture images (SAIs) to obtain an LF with a large baseline. For an accurate rectification, a method for pose estimation is also proposed, where the relative rotation and translation between the two LF cameras are estimated. The proposed pose estimation minimizes the degree of freedom (DoF) in the LF-point-LF-point correspondence model and explicitly solves this model in a linear way. The proposed pose estimation outperforms the state-of-the-art algorithms by providing more accurate results to support rectification. The significantly improved depth resolution in 3D reconstruction demonstrates the effectiveness of the proposed LF rectification.
In this paper, we develop a modified differential Structure from Motion (SfM) algorithm that can estimate relative pose from two consecutive frames despite of Rolling Shutter (RS) artifacts. In particular, we show that under constant velocity assumption, the errors induced by the rolling shutter effect can be easily rectified by a linear scaling operation on each optical flow. We further propose a 9-point algorithm to recover the relative pose of a rolling shutter camera that undergoes constant acceleration motion. We demonstrate that the dense depth maps recovered from the relative pose of th…
Optical Image Stabilization (OIS) system in mobile devices reduces image blurring by steering lens to compensate for hand jitters. However, OIS changes intrinsic camera parameters (i.e. $\mathrm{K}$ matrix) dynamically which hinders accurate camera pose estimation or 3D reconstruction. Here we propose a novel neural network-based approach that estimates $\mathrm{K}$ matrix in real-time so that pose estimation or scene reconstruction can be run at camera native resolution for the highest accuracy on mobile devices. Our network design takes gratified projection model discrepancy feature and 3D p…
Spectral Rectification for Parameter-Efficient Adaptation of Foundation Models in Colonoscopy Depth Estimation
Department of Biomedical Engineering, National University of Singapore, Singapore（新加坡国立大学生物医学工程系）；Department of Radiotherapy, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China（西安交通大学第一附属医院放疗科）
This paper addresses the task of relative camera pose estimation from raw image pixels, by means of deep neural networks. The proposed RPNet network takes pairs of images as input and directly infers the relative poses, without the need of camera intrinsic/extrinsic. While state-of-the-art systems based on SIFT + RANSAC, are able to recover the translation vector only up to scale, RPNet is trained to produce the full translation vector, in an end-to-end way. Experimental results on the Cambridge Landmark dataset show very promising results regarding the recovery of the full translation vector.…
RMAvatar: Photorealistic Human Avatar Reconstruction from Monocular Video Based on Rectified Mesh-embedded Gaussians
College of Computer Engineering, Jimei University（集美大学计算机工程学院）；National Institute for Data Science in Health and Medicine, Xiamen University（厦门大学健康与医学数据科学国家研究院）；The University of Texas at Dallas（德克萨斯大学达拉斯分校）；School of Informatics, Xiamen University（厦门大学信息学院）；BNU-HKBU United International College（北京师范大学-香港浸会大学联合国际学院）
SZTAKI（匈牙利科学院计算机与自动化研究所）；University of Debrecen（德布勒森大学）；Czech Technical University in Prague（布拉格捷克理工大学）
We propose a new approach for combining deep-learned non-metric monocular depth with affine correspondences (ACs) to estimate the relative pose of two calibrated cameras from a single correspondence. Considering the depth information and affine features, two new constraints on the camera pose are derived. The proposed solver is usable within 1-point RANSAC approaches. Thus, the processing time of the robust estimation is linear in the number of correspondences and, therefore, orders of magnitude faster than by using traditional approaches. The proposed 1AC+D solver is tested both on synthetic …
Although human pose estimation for various computer vision (CV) applications has been studied extensively in the last few decades, yet in-bed pose estimation using camera-based vision methods has been ignored by the CV community because it is assumed to be identical to the general purpose pose estimation methods. However, in-bed pose estimation has its own specialized aspects and comes with specific challenges including the notable differences in lighting conditions throughout a day and also having different pose distribution from the common human surveillance viewpoint. In this paper, we demo…
NEC Labs America（NEC美国实验室）；University of California San Diego（加州大学圣迭戈分校）
Learning methods for relative camera pose estimation have been developed largely in isolation from classical geometric approaches. The question of how to integrate predictions from deep neural networks (DNNs) and solutions from geometric solvers, such as the 5-point algorithm, has as yet remained under-explored. In this paper, we present a novel framework that involves probabilistic fusion between the two families of predictions during network training, with a view to leveraging their complementary benefits in a learnable way. The fusion is achieved by learning the DNN uncertainty under explic…
Nanjing University of Science and Technology（南京理工大学）；Czech Technical University in Prague（捷克技术大学（布拉格））；SZTAKI（匈牙利计算机科学与控制研究所）
Smartphones, tablets and camera systems used, e.g., in cars and UAVs, are typically equipped with IMUs (inertial measurement units) that can measure the gravity vector accurately. Using this additional information, the $y$-axes of the cameras can be aligned, reducing their relative orientation to a single degree-of-freedom. With this assumption, we propose a novel globally optimal solver, minimizing the algebraic error in the least-squares sense, to estimate the relative pose in the over-determined case. Based on the epipolar constraint, we convert the optimization problem into solving two pol…
RePoseD: Efficient Relative Pose Estimation With Known Depth Information
Czech Technical University in Prague（布拉格捷克技术大学）；Comenius University in Bratislava（布拉迪斯拉发夸美纽斯大学）；Nankai University（南开大学）；Czech Institute of Informatics, Robotics and Cybernetics（捷克信息学、机器人学与控制论研究所）
In this paper, we derive a new differential homography that can account for the scanline-varying camera poses in Rolling Shutter (RS) cameras, and demonstrate its application to carry out RS-aware image stitching and rectification at one stroke. Despite the high complexity of RS geometry, we focus in this paper on a special yet common input -- two consecutive frames from a video stream, wherein the inter-frame motion is restricted from being arbitrarily large. This allows us to adopt simpler differential motion model, leading to a straightforward and practical minimal solver. To deal with non-…
KAIST（韩国科学技术院）；Institute for Basic Science（基础科学研究院）
Relative camera pose estimation, i.e. estimating the translation and rotation vectors using a pair of images taken in different locations, is an important part of systems in augmented reality and robotics. In this paper, we present an end-to-end relative camera pose estimation network using a siamese architecture that is independent of camera parameters. The network is trained using the Cambridge Landmarks data with four individual scene datasets and a dataset combining the four scenes. To improve generalization, we propose a novel two-stage training that alleviates the need of a hyperparamete…
Research Center for Systems and Technologies (SYSTEC)（系统与技术研究中心）；Faculty of Engineering, University of Porto（波尔图大学工程学院）；KTH Royal Institute of Technology（KTH皇家理工学院）
The 3D depth estimation and relative pose estimation problem within a decentralized architecture is a challenging problem that arises in missions that require coordination among multiple vision-controlled robots. The depth estimation problem aims at recovering the 3D information of the environment. The relative localization problem consists of estimating the relative pose between two robots, by sensing each other's pose or sharing information about the perceived environment. Most solutions for these problems use a set of discrete data without taking into account the chronological order of the …
FlowAWR: Online Adaptive Flow Reinforcement via Advantage-Weighted Rectification
Beihang University（北京航空航天大学）；Joy Future Academy（京东探索研究院）；Zhongguancun Academy（中关村学院）；Zhejiang University（浙江大学）；University of Science and Technology of China（中国科学技术大学）
University of Texas at Austin（德克萨斯大学奥斯汀分校）；UT Austin（德克萨斯大学奥斯汀分校）；Phillips Academy Andover（安多弗菲利普斯学院）；Snap Research（Snap研究）；Zhejiang University（浙江大学）；Facebook AI Research（Facebook人工智能研究院）
Estimating the relative rigid pose between two RGB-D scans of the same underlying environment is a fundamental problem in computer vision, robotics, and computer graphics. Most existing approaches allow only limited maximum relative pose changes since they require considerable overlap between the input scans. We introduce a novel deep neural network that extends the scope to extreme relative poses, with little or even no overlap between the input scans. The key idea is to infer more complete scene information about the underlying environment and match on the completed scans. In particular, ins…
College of Aerospace Science and Engineering, National University of Defense Technology（国防科技大学航天科学与工程学院）
The point correspondence (PC) and affine correspondence (AC) are widely used for relative pose estimation. An AC consists of a PC across two views and an affine transformation between the small patches around this PC. Previous work demonstrates that one AC generally provides three independent constraints for relative pose estimation. For multi-camera systems, there is still not any AC-based minimal solver for general relative pose estimation. To deal with this problem, we propose a complete solution to relative pose estimation from two ACs for multi-camera systems, consisting of a series of mi…
The rolling shutter camera has received great attention due to its low cost imaging capability, however, the estimation of relative pose between rolling shutter cameras still remains a difficult problem owing to its line-by-line image capturing characteristics. To alleviate this problem, we exploit gyroscope measurements, angular velocity, along with image measurement to compute the relative pose between rolling shutter cameras. The gyroscope measurements provide the information about instantaneous motion that causes the rolling shutter distortion. Having gyroscope measurements in one hand, we…
D$^2$Turb: Depth-Aware Simulation and Decoupled Learning for Single-Frame Atmospheric Turbulence Mitigation
University of Electronic Science and Technology of China（电子科技大学）；Beijing Institute of Space Mechanics and Electricity（北京空间机械与电子研究所）；School of Physics, Northwest University Xi'an（西安西北大学物理学院）
The estimation of the camera poses associated with a set of images commonly relies on feature matches between the images. In contrast, we are the first to address this challenge by using objectness regions to guide the pose estimation problem rather than explicit semantic object detections. We propose Pose Refiner Network (PoserNet) a light-weight Graph Neural Network to refine the approximate pair-wise relative camera poses. PoserNet exploits associations between the objectness regions - concisely expressed as bounding boxes - across multiple views to globally refine sparsely connected view g…
Gwangju Institute of Science Technology (GIST)（光州科学技术院）
Relative pose estimation is a fundamental problem in computer vision and it has been studied for conventional global shutter cameras for decades. However, recently, a rolling shutter camera has been widely used due to its low cost imaging capability and, since the rolling shutter camera captures the image line-by-line, the relative pose estimation of a rolling shutter camera is more difficult than that of a global shutter camera. In this paper, we propose to exploit inertial measurements (gravity and angular velocity) for the rolling shutter relative pose estimation problem. The inertial measu…
In this paper, we introduce a novel RGB-D based relative pose estimation approach that is suitable for small-overlapping or non-overlapping scans and can output multiple relative poses. Our method performs scene completion and matches the completed scans. However, instead of using a fixed representation for completion, the key idea is to utilize hybrid representations that combine 360-image, 2D image-based layout, and planar patches. This approach offers adaptively feature representations for relative pose estimation. Besides, we introduce a global-2-local matching procedure, which utilizes in…
We address the problem of 3D human pose estimation from 2D input images using only weakly supervised training data. Despite showing considerable success for 2D pose estimation, the application of supervised machine learning to 3D pose estimation in real world images is currently hampered by the lack of varied training images with corresponding 3D poses. Most existing 3D pose estimation algorithms train on data that has either been collected in carefully controlled studio settings or has been generated synthetically. Instead, we take a different approach, and propose a 3D human pose estimation …
Relative pose regressors (RPRs) localize a camera by estimating its relative translation and rotation to a pose-labelled reference. Unlike scene coordinate regression and absolute pose regression methods, which learn absolute scene parameters, RPRs can (theoretically) localize in unseen environments, since they only learn the residual pose between camera pairs. In practice, however, the performance of RPRs is significantly degraded in unseen scenes. In this work, we propose to aggregate paired feature maps into latent codes, instead of operating on global image descriptors, in order to improve…
ETH Zurich（苏黎世联邦理工学院）；Niantic；University College London（伦敦大学学院）
Visual re-localization means using a single image as input to estimate the camera's location and orientation relative to a pre-recorded environment. The highest-scoring methods are "structure based," and need the query camera's intrinsics as an input to the model, with careful geometric optimization. When intrinsics are absent, methods vie for accuracy by making various other assumptions. This yields fairly good localization scores, but the models are "narrow" in some way, eg., requiring costly test-time computations, or depth sensors, or multiple query frames. In contrast, our proposed method…
South Ural State University（南乌拉尔国立大学）；TrunkTech Co., Ltd.（创科有限公司）
We propose two minimal solutions to the problem of relative pose estimation of (i) a calibrated camera from four points in two views and (ii) a calibrated generalized camera from five points in two views. In both cases, the relative rotation angle between the views is assumed to be known. In practice, such angle can be derived from the readings of a 3d gyroscope. We represent the rotation part of the motion in terms of unit quaternions in order to construct polynomial equations encoding the epipolar constraints. The Gröbner basis technique is then used to efficiently derive the solutions. Our …
In this paper, we present a novel linear algorithm to estimate the 6 DoF relative pose from consecutive frames of stereo rolling shutter (RS) cameras. Our method is derived based on the assumption that stereo cameras undergo motion with constant velocity around the center of the baseline, which needs 9 pairs of correspondences on both left and right consecutive frames. The stereo RS images enable the recovery of depth maps from the semi-global matching (SGM) algorithm. With the estimated camera motion and depth map, we can correct the RS images to get the undistorted images without any scene s…
VGGT-HPE: Reframing Head Pose Estimation as Relative Pose Prediction
Archimedes, Athena Research Center（雅典娜研究中心阿基米德实验室）；HERON – Hellenic Robotics Center of Excellence（HERON – 希腊机器人卓越中心）；Robotics Institute, Athena Research Center（雅典娜研究中心机器人研究所）；School of ECE, National Technical University of Athens（雅典国家技术大学电子与计算机工程学院）；University of Pennsylvania（宾夕法尼亚大学）
We propose a new method for estimating the relative pose between two images, where we jointly learn keypoint detection, description extraction, matching and robust pose estimation. While our architecture follows the traditional pipeline for pose estimation from geometric computer vision, all steps are learnt in an end-to-end fashion, including feature matching. We demonstrate our method for the task of visual localization of a query image within a database of images with known pose. Pairwise pose estimation has many practical applications for robotic mapping, navigation, and AR. For example, t…
Benchmarking Visual-Inertial Deep Multimodal Fusion for Relative Pose Regression and Odometry-aided Absolute Pose Regression
Fraunhofer Institute for Integrated Circuits IIS（弗劳恩霍夫集成电路研究所）；Munich Center for Machine Learning（慕尼黑机器学习中心）；Technical University of Dortmund（多特蒙德工业大学）；Ulm University（乌尔姆大学）
In this paper we present the first fast optimality certifier for the non-minimal version of the Relative Pose problem for calibrated cameras from epipolar constraints. The proposed certifier is based on Lagrangian duality and relies on a novel closed-form expression for dual points. We also leverage an efficient solver that performs local optimization on the manifold of the original problem's non-convex domain. The optimality of the solution is then checked via our novel fast certifier. The extensive conducted experiments demonstrate that, despite its simplicity, this certifiable solver perfor…
Str-L Pose: Integrating Point and Structured Line for Relative Pose Estimation in Dual-Graph
Institute of Information Science, Beijing Jiaotong University（北京交通大学信息科学研究所）；Beijing Key Laboratory of Advanced Information Science and Network Technology（先进信息科学与网络技术北京市重点实验室）
We present a simple baseline for directly estimating the relative pose (rotation and translation, including scale) between two images. Deep methods have recently shown strong progress but often require complex or multi-stage architectures. We show that a handful of modifications can be applied to a Vision Transformer (ViT) to bring its computations close to the Eight-Point Algorithm. This inductive bias enables a simple method to be competitive in multiple settings, often substantially improving over the state of the art with strong performance gains in limited data regimes.
This work contributes an efficient algorithm to compute the Relative Pose problem (RPp) between calibrated cameras and certify the optimality of the solution, given a set of pair-wise feature correspondences affected by noise and probably corrupted by wrong matches. We propose a family of certifiers that is shown to increase the ratio of detected optimal solutions. This set of certifiers is incorporated into a fast essential matrix estimation pipeline that, given any initial guess for the RPp, refines it iteratively on the product space of 3D rotations and 2-sphere. In addition, this fast cert…
Globally Optimal Solution to the Generalized Relative Pose Estimation Problem using Affine Correspondences
College of Aerospace Science and Engineering, National University of Defense Technology（航空科学与工程学院，国防科技大学）；Global Navigation Satellite System Research Center, Wuhan University（导航卫星系统研究所，武汉大学）
Shanghai Research Institute for Intelligent Autonomous Systems, Tongji University（同济大学智能自主系统研究所）；College of Electronics & Information Engineering, Tongji University（电子信息工程学院）；Key Laboratory of Embedded System and Service Computing (Ministry of Education), Tongji University（教育部嵌入式系统与服务计算重点实验室）；Frontiers Science Center for Intelligent Autonomous Systems, Tongji University（智能自主系统前沿科学中心）
