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Image-Based 3D Reconstruction: Neural Networks Vs. Multiview Geometry
Image-based 3D Reconstruction: Neural Networks vs. Multiview Geometry Julius Schoning¨ and Gunther Heidemann Institute of Cognitive Science, Osnabruck¨ University, Osnabruck,¨ Germany Email: fjuschoening,[email protected] Abstract—Methods using multiple view geometry (MVG), like algorithms, guarantee linear processing time, even in cases Structure from Motion (SfM), are still the dominant approaches where the number and resolution of the input images make for image-based 3D reconstruction. These reconstruction methods MVG-based approaches infeasible. have become quite robust and accurate. However, how robust and accurate can artificial neural networks (ANNs) reconstruct For the research if the underlying mathematical principle a priori unknown 3D objects? Exceed the restriction of object of MVG can be learned by ANNs, datasets like ShapeNet categories this paper evaluates ANNs for reconstructing arbitrary [9] and ModelNet40 [10] cannot be used hence they en- 3D objects. With the use of a synthetic scalable cube dataset for code object categories such as planes, chairs, tables. The training, testing and validating ANNs, it is shown that ANNs are needed dataset must not have shape priors of object cat- capable of learning mathematical principles of 3D reconstruction. As inspiration for the design of the different ANNs architectures, egories, and also, it must be scalable in its complexity the global, hierarchical, and incremental key-point matching for providing a large body of samples with ground truth strategies of SfM approaches were taken into account. Based data, ensuring the learning of even deep ANN. For this on these benchmarks and a review of the used dataset, it is reason, we are using the synthetic scalable cube dataset [11], shown that voxel-based 3D reconstruction cannot be scaled. -
Amodal 3D Reconstruction for Robotic Manipulation Via Stability and Connectivity
Amodal 3D Reconstruction for Robotic Manipulation via Stability and Connectivity William Agnew, Christopher Xie, Aaron Walsman, Octavian Murad, Caelen Wang, Pedro Domingos, Siddhartha Srinivasa University of Washington fwagnew3, chrisxie, awalsman, ovmurad, wangc21, pedrod, [email protected] Abstract: Learning-based 3D object reconstruction enables single- or few-shot estimation of 3D object models. For robotics, this holds the potential to allow model-based methods to rapidly adapt to novel objects and scenes. Existing 3D re- construction techniques optimize for visual reconstruction fidelity, typically mea- sured by chamfer distance or voxel IOU. We find that when applied to realis- tic, cluttered robotics environments, these systems produce reconstructions with low physical realism, resulting in poor task performance when used for model- based control. We propose ARM, an amodal 3D reconstruction system that in- troduces (1) a stability prior over object shapes, (2) a connectivity prior, and (3) a multi-channel input representation that allows for reasoning over relationships between groups of objects. By using these priors over the physical properties of objects, our system improves reconstruction quality not just by standard vi- sual metrics, but also performance of model-based control on a variety of robotics manipulation tasks in challenging, cluttered environments. Code is available at github.com/wagnew3/ARM. Keywords: 3D Reconstruction, 3D Vision, Model-Based 1 Introduction Manipulating previously unseen objects is a critical functionality for robots to ubiquitously function in unstructured environments. One solution to this problem is to use methods that do not rely on explicit 3D object models, such as model-free reinforcement learning [1,2]. However, quickly generalizing learned policies across wide ranges of tasks and objects remains an open problem. -
Seamless Texture Mapping of 3D Point Clouds
Seamless Texture Mapping of 3D Point Clouds Dan Goldberg Mentor: Carl Salvaggio Chester F. Carlson Center for Imaging Science, Rochester Institute of Technology Rochester, NY November 25, 2014 Abstract The two similar, quickly growing fields of computer vision and computer graphics give users the ability to immerse themselves in a realistic computer generated environment by combining the ability create a 3D scene from images and the texture mapping process of computer graphics. The output of a popular computer vision algorithm, structure from motion (obtain a 3D point cloud from images) is incomplete from a computer graphics standpoint. The final product should be a textured mesh. The goal of this project is to make the most aesthetically pleasing output scene. In order to achieve this, auxiliary information from the structure from motion process was used to texture map a meshed 3D structure. 1 Introduction The overall goal of this project is to create a textured 3D computer model from images of an object or scene. This problem combines two different yet similar areas of study. Computer graphics and computer vision are two quickly growing fields that take advantage of the ever-expanding abilities of our computer hardware. Computer vision focuses on a computer capturing and understanding the world. Computer graphics con- centrates on accurately representing and displaying scenes to a human user. In the computer vision field, constructing three-dimensional (3D) data sets from images is becoming more common. Microsoft's Photo- synth (Snavely et al., 2006) is one application which brought attention to the 3D scene reconstruction field. Many structure from motion algorithms are being applied to data sets of images in order to obtain a 3D point cloud (Koenderink and van Doorn, 1991; Mohr et al., 1993; Snavely et al., 2006; Crandall et al., 2011; Weng et al., 2012; Yu and Gallup, 2014; Agisoft, 2014). -
Stereoscopic Vision System for Reconstruction of 3D Objects
International Journal of Applied Engineering Research ISSN 0973-4562 Volume 13, Number 18 (2018) pp. 13762-13766 © Research India Publications. http://www.ripublication.com Stereoscopic Vision System for reconstruction of 3D objects Robinson Jimenez-Moreno Professor, Department of Mechatronics Engineering, Nueva Granada Military University, Bogotá, Colombia. Javier O. Pinzón-Arenas Research Assistant, Department of Mechatronics Engineering, Nueva Granada Military University, Bogotá, Colombia. César G. Pachón-Suescún Research Assistant, Department of Mechatronics Engineering, Nueva Granada Military University, Bogotá, Colombia. Abstract applications of mobile robotics, for autonomous anthropomorphic agents [9] or not, require reducing costs and The present work details the implementation of a stereoscopic using free software for their development. Which, by means of 3D vision system, by means of two digital cameras of similar laser systems or mono-camera, does not allow to obtain a characteristics. This system is based on the calibration of the reduction in price or analysis of adequate depth. two cameras to find the intrinsic and extrinsic parameters of the same in order to use projective geometry to find the disparity The system proposed in this article presents a system of between the images taken by each camera with respect to the reconstruction with stereoscopic pair, i.e. two cameras that will same scene. From the disparity, it is obtained the depth map capture the scene from their own perspective allowing, by that will allow to find the 3D points that are part of the means of projective geometry, to establish point reconstruction of the scene and to recognize an object clearly correspondences in the scene to find the calibration matrices of in it. -
Robust Tracking and Structure from Motion with Sampling Method
ROBUST TRACKING AND STRUCTURE FROM MOTION WITH SAMPLING METHOD A DISSERTATION SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS for the degree DOCTOR OF PHILOSOPHY IN ROBOTICS Robotics Institute Carnegie Mellon University Peng Chang July, 2002 Thesis Committee: Martial Hebert, Chair Robert Collins Jianbo Shi Michael Black This work was supported in part by the DARPA Tactical Mobile Robotics program under JPL contract 1200008 and by the Army Research Labs Robotics Collaborative Technology Alliance Contract DAAD 19- 01-2-0012. °c 2002 Peng Chang Keywords: structure from motion, visual tracking, visual navigation, visual servoing °c 2002 Peng Chang iii Abstract Robust tracking and structure from motion (SFM) are fundamental problems in computer vision that have important applications for robot visual navigation and other computer vision tasks. Al- though the geometry of the SFM problem is well understood and effective optimization algorithms have been proposed, SFM is still difficult to apply in practice. The reason is twofold. First, finding correspondences, or "data association", is still a challenging problem in practice. For visual navi- gation tasks, the correspondences are usually found by tracking, which often assumes constancy in feature appearance and smoothness in camera motion so that the search space for correspondences is much reduced. Therefore tracking itself is intrinsically difficult under degenerate conditions, such as occlusions, or abrupt camera motion which violates the assumptions for tracking to start with. Second, the result of SFM is often observed to be extremely sensitive to the error in correspon- dences, which is often caused by the failure of the tracking. This thesis aims to tackle both problems simultaneously. -
Configurable 3D Scene Synthesis and 2D Image Rendering with Per-Pixel Ground Truth Using Stochastic Grammars
International Journal of Computer Vision https://doi.org/10.1007/s11263-018-1103-5 Configurable 3D Scene Synthesis and 2D Image Rendering with Per-pixel Ground Truth Using Stochastic Grammars Chenfanfu Jiang1 · Siyuan Qi2 · Yixin Zhu2 · Siyuan Huang2 · Jenny Lin2 · Lap-Fai Yu3 · Demetri Terzopoulos4 · Song-Chun Zhu2 Received: 30 July 2017 / Accepted: 20 June 2018 © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract We propose a systematic learning-based approach to the generation of massive quantities of synthetic 3D scenes and arbitrary numbers of photorealistic 2D images thereof, with associated ground truth information, for the purposes of training, bench- marking, and diagnosing learning-based computer vision and robotics algorithms. In particular, we devise a learning-based pipeline of algorithms capable of automatically generating and rendering a potentially infinite variety of indoor scenes by using a stochastic grammar, represented as an attributed Spatial And-Or Graph, in conjunction with state-of-the-art physics- based rendering. Our pipeline is capable of synthesizing scene layouts with high diversity, and it is configurable inasmuch as it enables the precise customization and control of important attributes of the generated scenes. It renders photorealistic RGB images of the generated scenes while automatically synthesizing detailed, per-pixel ground truth data, including visible surface depth and normal, object identity, and material information (detailed to object parts), as well as environments (e.g., illuminations and camera viewpoints). We demonstrate the value of our synthesized dataset, by improving performance in certain machine-learning-based scene understanding tasks—depth and surface normal prediction, semantic segmentation, reconstruction, etc.—and by providing benchmarks for and diagnostics of trained models by modifying object attributes and scene properties in a controllable manner. -
Self-Driving Car Autonomous System Overview
Self-Driving Car Autonomous System Overview - Industrial Electronics Engineering - Bachelors’ Thesis - Author: Daniel Casado Herráez Thesis Director: Javier Díaz Dorronsoro, PhD Thesis Supervisor: Andoni Medina, MSc San Sebastián - Donostia, June 2020 Self-Driving Car Autonomous System Overview Daniel Casado Herráez "When something is important enough, you do it even if the odds are not in your favor." - Elon Musk - 2 Self-Driving Car Autonomous System Overview Daniel Casado Herráez To my Grandfather, Family, Friends & to my supervisor Javier Díaz 3 Self-Driving Car Autonomous System Overview Daniel Casado Herráez 1. Contents 1.1. Index 1. Contents ..................................................................................................................................................... 4 1.1. Index.................................................................................................................................................. 4 1.2. Figures ............................................................................................................................................... 6 1.1. Tables ................................................................................................................................................ 7 1.2. Algorithms ........................................................................................................................................ 7 2. Abstract ..................................................................................................................................................... -
3D Scene Reconstruction from Multiple Uncalibrated Views
3D Scene Reconstruction from Multiple Uncalibrated Views Li Tao Xuerong Xiao [email protected] [email protected] Abstract aerial photo filming. The 3D scene reconstruction applications such as Google Earth allow people to In this project, we focus on the problem of 3D take flight over entire metropolitan areas in a vir- scene reconstruction from multiple uncalibrated tually real 3D world, explore 3D tours of build- views. We have studied different 3D scene recon- ings, cities and famous landmarks, as well as take struction methods, including Structure from Mo- a virtual walk around natural and cultural land- tion (SFM) and volumetric stereo (space carv- marks without having to be physically there. A ing and voxel coloring). Here we report the re- computer vision based reconstruction method also sults of applying these methods to different scenes, allows the use of rich image resources from the in- ranging from simple geometric structures to com- ternet. plicated buildings, and will compare the perfor- In this project, we have studied different 3D mances of different methods. scene reconstruction methods, including Struc- ture from Motion (SFM) method and volumetric 1. Introduction stereo (space carving and voxel coloring). Here we report the results of applying these methods to 3D reconstruction from multiple images is the different scenes, ranging from simple geometric creation of three-dimensional models from a set of structures to complicated buildings, and will com- images. It is the reverse process of obtaining 2D pare the performances of different methods. images from 3D scenes. In recent decades, there is an important demand for 3D content for com- 2. -
3D Shape Reconstruction from Vision and Touch
3D Shape Reconstruction from Vision and Touch Edward J. Smith1;2∗ Roberto Calandra1 Adriana Romero1;2 Georgia Gkioxari1 David Meger2 Jitendra Malik1;3 Michal Drozdzal1 1 Facebook AI Research 2 McGill University 3 University of California, Berkeley Abstract When a toddler is presented a new toy, their instinctual behaviour is to pick it up and inspect it with their hand and eyes in tandem, clearly searching over its surface to properly understand what they are playing with. At any instance here, touch provides high fidelity localized information while vision provides complementary global context. However, in 3D shape reconstruction, the complementary fusion of visual and haptic modalities remains largely unexplored. In this paper, we study this problem and present an effective chart-based approach to multi-modal shape understanding which encourages a similar fusion vision and touch information. To do so, we introduce a dataset of simulated touch and vision signals from the interaction between a robotic hand and a large array of 3D objects. Our results show that (1) leveraging both vision and touch signals consistently improves single- modality baselines; (2) our approach outperforms alternative modality fusion methods and strongly benefits from the proposed chart-based structure; (3) the reconstruction quality increases with the number of grasps provided; and (4) the touch information not only enhances the reconstruction at the touch site but also extrapolates to its local neighborhood. 1 Introduction From an early age children clearly and often loudly demonstrate that they need to both look and touch any new object that has peaked their interest. The instinctual behavior of inspecting with both their eyes and hands in tandem demonstrates the importance of fusing vision and touch information for 3D object understanding. -
Euclidean Reconstruction of Natural Underwater
EUCLIDEAN RECONSTRUCTION OF NATURAL UNDERWATER SCENES USING OPTIC IMAGERY SEQUENCE By Han Hu B.S. in Remote Sensing Science and Technology, Wuhan University, 2005 M.S. in Photogrammetry and Remote Sensing, Wuhan University, 2009 THESIS Submitted to the University of New Hampshire In Partial Fulfillment of The Requirements for the Degree of Master of Science In Ocean Engineering Ocean Mapping September, 2015 This thesis has been examined and approved in partial fulfillment of the requirements for the degree of M.S. in Ocean Engineering Ocean Mapping by: Thesis Director, Yuri Rzhanov, Research Professor, Ocean Engineering Philip J. Hatcher, Professor, Computer Science R. Daniel Bergeron, Professor, Computer Science On May 26, 2015 Original approval signatures are on file with the University of New Hampshire Graduate School. ii ACKNOWLEDGEMENTS I would like to thank all those people who made this thesis possible. My first debt of sincere gratitude and thanks must go to my advisor, Dr. Yuri Rzhanov. Throughout the entire three-year period, he offered his unreserved help, guidance and support to me in both work and life. Without him, it is definite that my thesis could not be completed successfully. I could not have imagined having a better advisor than him. Besides my advisor in Ocean Engineering, I would like to give many thanks to the rest of my thesis committee and my advisors in the Department of Computer Science: Prof. Philip J. Hatcher and Prof. R. Daniel Bergeron as well for their encouragement, insightful comments and all their efforts in making this thesis successful. I also would like to express my thanks to the Center for Coastal and Ocean Mapping and the Department of Computer Science at the University of New Hampshire for offering me the opportunities and environment to work on exciting projects and study in useful courses from which I learned many advanced skills and knowledge. -
3D Reconstruction Is Not Just a Low-Level Task: Retrospect and Survey
3D reconstruction is not just a low-level task: retrospect and survey Jianxiong Xiao Massachusetts Institute of Technology [email protected] Abstract 3D reconstruction is in obtaining more accurate depth maps [44, 45, 55] or 3D point clouds [47, 48, 58, 50]. We now Although an image is a 2D array, we live in a 3D world. have reliable techniques [47, 48] for accurately computing The desire to recover the 3D structure of the world from 2D a partial 3D model of an environment from thousands of images is the key that distinguished computer vision from partially overlapping photographs (using keypoint match- the already existing field of image processing 50 years ago. ing and structure from motion). Given a large enough set of For the past two decades, the dominant research focus for views of a particular object, we can create accurate dense 3D reconstruction is in obtaining more accurate depth maps 3D surface models (using stereo matching and surface fit- or 3D point clouds. However, even when a robot has a depth ting [44, 45, 55, 58, 50, 59]). In particular, using Microsoft map, it still cannot manipulate an object, because there is Kinect (also Primesense and Asus Xtion), a reliable depth no high-level representation of the 3D world. Essentially, map can be obtained straightly out of box. 3D reconstruction is not just a low-level task. Obtaining However, despite all of these advances, the dream of hav- a depth map to capture a distance at each pixel is analo- ing a computer interpret an image at the same level as a two- gous to inventing a digital camera to capture the color value year old (for example, counting all of the objects in a pic- at each pixel. -
Motion Estimation for Self-Driving Cars with a Generalized Camera
2013 IEEE Conference on Computer Vision and Pattern Recognition Motion Estimation for Self-Driving Cars With a Generalized Camera Gim Hee Lee1, Friedrich Fraundorfer2, Marc Pollefeys1 1 Department of Computer Science Faculty of Civil Engineering and Surveying2 ETH Zurich,¨ Switzerland Technische Universitat¨ Munchen,¨ Germany {glee@student, pomarc@inf}.ethz.ch [email protected] Abstract In this paper, we present a visual ego-motion estima- tion algorithm for a self-driving car equipped with a close- to-market multi-camera system. By modeling the multi- camera system as a generalized camera and applying the non-holonomic motion constraint of a car, we show that this leads to a novel 2-point minimal solution for the general- ized essential matrix where the full relative motion includ- ing metric scale can be obtained. We provide the analytical solutions for the general case with at least one inter-camera correspondence and a special case with only intra-camera Figure 1. Car with a multi-camera system consisting of 4 cameras correspondences. We show that up to a maximum of 6 so- (front, rear and side cameras in the mirrors). lutions exist for both cases. We identify the existence of degeneracy when the car undergoes straight motion in the ready available in some COTS cars, we focus this paper special case with only intra-camera correspondences where on using a multi-camera setup for ego-motion estimation the scale becomes unobservable and provide a practical al- which is one of the key features for self-driving cars. A ternative solution. Our formulation can be efficiently imple- multi-camera setup can be modeled as a generalized cam- mented within RANSAC for robust estimation.