Multi-view structure-from-motion for hybrid camera scenarios

We describe a pipeline for structure-from-motion (SfM) with mixed camera types, namely omnidirectional and perspective cameras. For the steps of this pipeline, we propose new approaches or adapt the existing perspective camera methods to make the pipeline effective and automatic. We model our cameras of different types with the sphere camera model. To match feature points, we describe a preprocessing algorithm which significantly increases scale invariant feature transform (SIFT) matching performance for hybrid image pairs. With this approach, automatic point matching between omnidirectional and perspective images is achieved. We robustly estimate the hybrid fundamental matrix with the obtained point correspondences. We introduce the normalization matrices for lifted coordinates so that normalization and denormalization can be performed linearly for omnidirectional images. We evaluate the alternatives of estimating camera poses in hybrid pairs. A weighting strategy is proposed for iterative linear triangulation which improves the structure estimation accuracy. Following the addition of multiple perspective and omnidirectional images to the structure, we perform sparse bundle adjustment on the estimated structure by adapting it to use the sphere camera model. Demonstrations of the end-to-end multi-view SfM pipeline with the real images of mixed camera types are presented.     

Accidental Pinhole and Pinspeck Cameras

We identify and study two types of “accidental” images that can be formed in scenes. The first is an accidental pinhole camera image. The second class of accidental images are “inverse” pinhole camera images, formed by subtracting an image with a small occluder present from a reference image without the occluder. Both types of accidental cameras happen in a variety of different situations. For example, an indoor scene illuminated by natural light, a street with a person walking under the shadow of a building, etc. The images produced by accidental cameras are often mistaken for shadows or interreflections. However, accidental images can reveal information about the scene outside the image, the lighting conditions, or the aperture by which light enters the scene.     

Measuring and modelling sewer pipes from video

This article presents a system for the automatic measurement and modelling of sewer pipes. The system recovers the interior shape of a sewer pipe from a video sequence which is acquired by a fish-eye lens camera moving inside the pipe. The approach is based on tracking interest points across successive video frames and posing the general structure-from-motion problem. It is shown that the tracked points can be reliably reconstructed despite the forward motion of the camera. This is achieved by utilizing a fish-eye lens with a wide field of view. The standard techniques for robust estimation of the two- and three-view geometry are modified so that they can be used for calibrated fish-eye lens cameras with a field of view less than 180°. The tubular arrangement of the reconstructed points allows pipe shape estimation by surface fitting. Hence, a method for modelling such surfaces with a locally cylindrical model is proposed. The system is demonstrated with a real sewer video and an error analysis for the recovered structure is presented.     

Minimal Warping: Planning Incremental Novel‐view Synthesis

Observing that many visual effects (depth‐of‐field, motion blur, soft shadows, spectral effects) and several sampling modalities (time, stereo or light fields) can be expressed as a sum of many pinhole camera images, we suggest a novel efficient image synthesis framework that exploits coherency among those images. We introduce the notion of “distribution flow” that represents the 2D image deformation in response to changes in the high‐dimensional time‐, lens‐, area light‐, spectral‐, etc. coordinates. Our approach plans the optimal traversal of the distribution space of all required pinhole images, such that starting from one representative root image, which is incrementally changed (warped) in a minimal fashion, pixels move at most by one pixel, if at all. The incremental warping allows extremely simple warping code, typically requiring half a millisecond on an Nvidia Geforce GTX 980Ti GPU per pinhole image. We show, how the bounded sampling does introduce very little errors in comparison to re‐rendering or a common warping‐based solution. Our approach allows efficient previews for arbitrary combinations of distribution effects and imaging modalities with little noise and high visual fidelity.     

A Generic Error Model and Its Application to Automatic 3D Modeling of Scenes Using a Catadioptric Camera

Recently, it was suggested that structure-from-motion be solved using generic tools which are exploitable for any kind of camera. The same challenge applies for the automatic reconstruction of 3D models from image sequences, which includes structure-from-motion. This article is a new step in this direction. First, a generic error model is introduced for central cameras. Second, this error model is systematically used in the 3D modeling process. The experiments are carried out in a context which has rarely been addressed until now: the automatic 3D modeling of scenes using a catadioptric camera.     

Stereo geometry from 3D ego-motion streams

This paper addresses the problem of geometry determination of a stereo rig that undergoes general rigid motions. Neither known reference objects nor stereo correspondence are required. With almost no exception, all existing online solutions attempt to recover stereo geometry by first establishing stereo correspondences. We first describe a mathematical framework that allows us to solve for stereo geometry, i.e., the rotation and translation between the two cameras, using only motion correspondence that is far easier to acquire than stereo correspondence. Second, we show how to recover the rotation and present two linear methods, as well as a nonlinear one to solve for the translation. Third, we perform a stability study for the developed methods in the presence of image noise, camera parameter noise, and ego-motion noise. We also address accuracy issues. Experiments with real image data are presented. The work allows the concept of online calibration to be broadened, as it is no longer true that only single cameras can exploit structure-from-motion strategies; even the extrinsic parameters of a stereo rig of cameras can do so without solving stereo correspondence. The developed framework is applicable for estimating the relative three-dimensional (3D) geometry associated with a wide variety of mounted devices used in vision and robotics, by exploiting their scaled ego-motion streams.     

Relativistic Effects for Time‐Resolved Light Transport

We present a real‐time framework which allows interactive visualization of relativistic effects for time‐resolved light transport. We leverage data from two different sources: real‐world data acquired with an effective exposure time of less than 2 picoseconds, using an ultra‐fast imaging technique termed femto‐photography, and a transient renderer based on ray‐tracing. We explore the effects of time dilation, light aberration, frequency shift and radiance accumulation by modifying existing models of these relativistic effects to take into account the time‐resolved nature of light propagation. Unlike previous works, we do not impose limiting constraints in the visualization, allowing the virtual camera to explore freely a reconstructed 3D scene depicting dynamic illumination. Moreover, we consider not only linear motion, but also acceleration and rotation of the camera. We further introduce, for the first time, a pinhole camera model into our relativistic rendering framework, and account for subsequent changes in focal length and field of view as the camera moves through the scene.     

Multi-View AAM Fitting and Construction

Active Appearance Models (AAMs) are generative, parametric models that have been successfully used in the past to model deformable objects such as human faces. The original AAMs formulation was 2D, but they have recently been extended to include a 3D shape model. A variety of single-view algorithms exist for fitting and constructing 3D AAMs but one area that has not been studied is multi-view algorithms. In this paper we present multi-view algorithms for both fitting and constructing 3D AAMs. Fitting an AAM to an image consists of minimizing the error between the input image and the closest model instance; i.e. solving a nonlinear optimization problem. In the first part of the paper we describe an algorithm for fitting a single AAM to multiple images, captured simultaneously by cameras with arbitrary locations, rotations, and response functions. This algorithm uses the scaled orthographic imaging model used by previous authors, and in the process of fitting computes, or calibrates, the scaled orthographic camera matrices. In the second part of the paper we describe an extension of this algorithm to calibrate weak perspective (or full perspective) camera models for each of the cameras. In essence, we use the human face as a (non-rigid) calibration grid. We demonstrate that the performance of this algorithm is roughly comparable to a standard algorithm using a calibration grid. In the third part of the paper, we show how camera calibration improves the performance of AAM fitting. A variety of non-rigid structure-from-motion algorithms, both single-view and multi-view, have been proposed that can be used to construct the corresponding 3D non-rigid shape models of a 2D AAM. In the final part of the paper, we show that constructing a 3D face model using non-rigid structure-from-motion suffers from the Bas-Relief ambiguity and may result in a “scaled” (stretched/compressed) model. We outline a robust non-rigid motion-stereo algorithm for calibrated multi-view 3D AAM construction and show how using calibrated multi-view motion-stereo can eliminate the Bas-Relief ambiguity and yield face models with higher 3D fidelity. Electronic Supplementary Material The online version of this article () contains supplementary material, which is available to authorized users.     

基于立体折反射全向成像的柱面全景深度估算

针对立体视觉原理的新型立体折反射全向成像系统结构设计和面向立体柱面全景像对的局域灰度相关对应点快速匹配算法,从捕获的全向市体影像中提取有效深度信息,用于辅助全向视频分析处理中的对象检测和跟踪.采用单相机和两个不同参数的抛物面型反射镜构造了一种共轴结构的折反射全向立体成像装置,捕获的存在一定视差的原始全向立体像对被投影展开为立体柱面全景像对,而后通过特定对应点匹配算法提取稠密的深度信息.对应点匹配算法采用局部区域灰度相关的算了,并充分利用了双向匹配和柱面全景的外极线约束来提高匹配的速度和准确度.仿真实验有效恢复了场景深度信息,证明了整套装置结构设计及深度估计方法的有效性.     

Visual SLAM for Flying Vehicles

The ability to learn a map of the environment is important for numerous types of robotic vehicles. In this paper, we address the problem of learning a visual map of the ground using flying vehicles. We assume that the vehicles are equipped with one or two low-cost downlooking cameras in combination with an attitude sensor. Our approach is able to construct a visual map that can later on be used for navigation. Key advantages of our approach are that it is comparably easy to implement, can robustly deal with noisy camera images, and can operate either with a monocular camera or a stereo camera system. Our technique uses visual features and estimates the correspondences between features using a variant of the progressive sample consensus (PROSAC) algorithm. This allows our approach to extract spatial constraints between camera poses that can then be used to address the simultaneous localization and mapping (SLAM) problem by applying graph methods. Furthermore, we address the problem of efficiently identifying loop closures. We performed several experiments with flying vehicles that demonstrate that our method is able to construct maps of large outdoor and indoor environments.      

A Linear Approach for Depth and Colour Camera Calibration Using Hybrid Parameters

Many recent applications of computer graphics and human computer interaction have adopted both colour cameras and depth cameras as input devices. Therefore, an effective calibration of both types of hardware taking different colour and depth inputs is required. Our approach removes the numerical difficulties of using non-linear optimization in previous methods which explicitly resolve camera intrinsics as well as the transformation between depth and colour cameras. A matrix of hybrid parameters is introduced to linearize our optimization. The hybrid parameters offer a transformation from a depth parametric space (depth camera image) to a colour parametric space (colour camera image) by combining the intrinsic parameters of depth camera and a rotation transformation from depth camera to colour camera. Both the rotation transformation and intrinsic parameters can be explicitly calculated from our hybrid parameters with the help of a standard QR factorisation. We test our algorithm with both synthesized data and real-world data where ground-truth depth information is captured by Microsoft Kinect. The experiments show that our approach can provide comparable accuracy of calibration with the state-of-the-art algorithms while taking much less computation time (1/50 of Herrera’s method and 1/10 of Raposo’s method) due to the advantage of using hybrid parameters.     

Joint radiometric calibration and feature tracking system with an application to stereo

To capture the full brightness range of natural scenes, cameras automatically adjust the exposure value which causes the brightness of scene points to change from frame to frame. Given such a video sequence, we introduce a system for tracking features and estimating the radiometric response function of the camera and the exposure difference between frames simultaneously. We model the global and nonlinear process that is responsible for the changes in image brightness rather than adapting to the changes locally and linearly which makes our tracking more robust to the change in brightness. We apply our system to perform structure-from-motion and stereo to reconstruct a texture-mapped 3D surface from a video taken in a high dynamic range environment.     

Hybrid light field imaging for improved spatial resolution and depth range

Light field imaging involves capturing both angular and spatial distribution of light; it enables new capabilities, such as post-capture digital refocusing, camera aperture adjustment, perspective shift, and depth estimation. Micro-lens array (MLA)-based light field cameras provide a cost-effective approach to light field imaging. There are two main limitations of MLA-based light field cameras: low spatial resolution and narrow baseline. While low spatial resolution limits the general purpose use and applicability of light field cameras, narrow baseline limits the depth estimation range and accuracy. In this paper, we present a hybrid stereo imaging system that includes a light field camera and a regular camera. The hybrid system addresses both spatial resolution and narrow baseline issues of the MLA-based light field cameras while preserving light field imaging capabilities.     

Digital cubism

To make a synthetic picture with computer graphics, we usually imagine a camera of some sort, taking a picture of a scene. Such cameras range from the simplest pinhole camera to a sophisticated simulation of optics and shutters. We explore some interesting, alternative imaging models - such as digital cubism. We harness the possibilities for communicating ideas and story points in new and expressive ways. We look at some basic camera models and then discuss about how we can extend them for its efficient use.     

An omni-directional mobile millimeter-sized microrobot with 3-mm electromagnetic micromotors for a micro-factory

This paper presents an omni-directional mobile microrobot for micro-assembly in a micro-factory. A novel structure is designed for omni-directional movement with three normal wheels. The millimeter-sized microrobot is actuated by four electromagnetic micromotors whose size is 3.1 mm × 3.1 mm × 1.4 mm. Three of the micromotors are for translation and the other one is for steering. The micromotor rotors are designed as the wheels to reduce the microrobot volume. A piezoelectric micro-gripper is fabricated for grasping micro-parts. The corresponding kinematics matrix is analyzed to prove the omni-directional mobility. A control system composed of two CCD cameras, a host computer and circuit board is designed. The macro camera is for a global view and the micro camera is for local supervision. Unique location methods are proposed for different scenarios. A microstep control approach for the micromotors is presented to satisfy the requirement of high positioning accuracy. The experiment demonstrates the mobility of the microrobot and the validity of the control system.     

一种扩展小孔成像模型的鱼眼相机矫正与标定方法

鱼眼相机由于其超宽的视场范围（Field of view,FOV）（可以达到180°以上）,得到越来越广泛的应用. 常规的基于小孔成像模型的相机矫正与标定算法在超宽视场的鱼眼成像系统中已经不太适用,为了兼顾小孔模型的特点,本文提出了一种扩展小孔成像模型的鱼眼相机矫正与标定方法. 此方法是对小孔成像模型的进一步拓展,不仅具备小孔模型实现简单、适合人眼视觉效果以及相机标定方便等优点,同时将小孔成像模型适用的视场范围扩展到超宽视场领域. 其基本思路是:在利用小孔成像模型对鱼眼相机90°左右视场范围进行矫正与标定的基础上,使用非等间距的点阵模板,并结合直线拟合以及自然邻点插值算法,扩展小孔模型适用的视场范围. 本文使用鱼眼相机从不同的角度拍摄多幅模板图,完成鱼眼相机的矫正与标定. 通过求取的小孔成像模型参数实现相机的标定;对鱼眼相机拍摄的实际场景图进行畸变矫正测试,结果表明此方法能够很好地矫正鱼眼相机存在的畸变,得到符合人眼视觉效果的矫正图;单幅矫正图视场范围达到130°,结合不同角度拍摄的多幅模板图,可把矫正的视场范围扩展到180°.     

A hierarchy of cameras for 3D photography

The view-independent visualization of 3D scenes is most often based on rendering accurate 3D models or utilizes image-based rendering techniques. To compute the 3D structure of a scene from a moving vision sensor or to use image-based rendering approaches, we need to be able to estimate the motion of the sensor from the recorded image information with high accuracy, a problem that has been well-studied. In this work, we investigate the relationship between camera design and our ability to perform accurate 3D photography, by examining the influence of camera design on the estimation of the motion and structure of a scene from video data. By relating the differential structure of the time varying plenoptic function to different known and new camera designs, we can establish a hierarchy of cameras based upon the stability and complexity of the computations necessary to estimate structure and motion. At the low end of this hierarchy is the standard planar pinhole camera for which the structure from motion problem is non-linear and ill-posed. At the high end is a camera, which we call the full field of view polydioptric camera, for which the motion estimation problem can be solved independently of the depth of the scene which leads to fast and robust algorithms for 3D Photography. In between are multiple view cameras with a large field of view which we have built, as well as omni-directional sensors.     

A Comprehensive and Versatile Camera Model for Cameras with Tilt Lenses

We propose camera models for cameras that are equipped with lenses that can be tilted in an arbitrary direction (often called Scheimpflug optics). The proposed models are comprehensive: they can handle all tilt lens types that are in common use for machine vision and consumer cameras and correctly describe the imaging geometry of lenses for which the ray angles in object and image space differ, which is true for many lenses. Furthermore, they are versatile since they can also be used to describe the rectification geometry of a stereo image pair in which one camera is perspective and the other camera is telecentric. We also examine the degeneracies of the models and propose methods to handle the degeneracies. Furthermore, we examine the relation of the proposed camera models to different classes of projective camera matrices and show that all classes of projective cameras can be interpreted as cameras with tilt lenses in a natural manner. In addition, we propose an algorithm that can calibrate an arbitrary combination of perspective and telecentric cameras (no matter whether they are tilted or untilted). The calibration algorithm uses a planar calibration object with circular control points. It is well known that circular control points may lead to biased calibration results. We propose two efficient algorithms to remove the bias and thus obtain accurate calibration results. Finally, we perform an extensive evaluation of the proposed camera models and calibration algorithms that establishes the validity and accuracy of the proposed models.     

折反射全向图像与遥感图像配准的建筑物高度提取算法

提出一种折反射全向图像与遥感图像配准的建筑物高度提取算法,可应用于大范围三维城市重建。首先,利用全向Hough变换方法提取折反射全向图像中建筑物的顶部边界线;然后基于提取的边界线,根据空间水平直线全向成像的角度不变性对折反射全向图像与遥感图像进行配准;最后利用配准结果,依据折反射全向图成像模型计算建筑物高度。实验结果证明该方法简捷易行且计算结果准确,误差较小。     

Geometric Information Criterion for Model Selection

In building a 3-D model of the environment from image and sensor data, one must fit to the data an appropriate class of models, which can be regarded as a parametrized manifold, or geometric model, defined in the data space. In this paper, we present a statistical framework for detecting degeneracies of a geometric model by evaluating its predictive capability in terms of the expected residual and derive the geometric AIC. We show that it allows us to detect singularities in a structure-from-motion analysis without introducing any empirically adjustable thresholds. We illustrate our approach by simulation examples. We also discuss the application potential of this theory for a wide range of computer vision and robotics problems.