Dense 3d Point Cloud Representation Of A Scene Using Uncalibrated Monocular Vision

We present a 3D reconstruction algorithm designed to support various automation and navigation applications. The algorithm presented focuses on the 3D reconstruction of a scene using only a single moving camera. Utilizing video frames captured at different points in time allows us to determine the depths of a scene. In this way, the system can be used to construct a point cloud model of its unknown surroundings. In this thesis, we present the step by step methodology of the development of a reconstruction technique. The original reconstruction process, resulting with a point cloud was computed based on feature matching and depth triangulation analysis. In an improved version of the algorithm, we utilized optical flow features to create an extremely dense representation model. Although dense, this model is hindered due to its low disparity resolution. As feature points were matched from frame to frame, the resolution of the input images and the discrete nature of disparities limited the depth computations within a scene. With the third algorithmic modification, we introduce the addition of the preprocessing step of nonlinear super resolution. With this addition, the accuracy of the point cloud which relies on precise disparity measurement has significantly increased. Using a pixel by pixel approach, the super resolution technique computes the phase congruency of each pixel's neighborhood and produces nonlinearly interpolated high resolution input frames. Thus, a feature point travels a more precise discrete disparity. Also, the quantity of points within the 3D point cloud model is significantly increased since the number of features is directly proportional to the resolution and high frequencies of the input image. Our final contribution of additional preprocessing steps is designed to filter noise points and mismatched features, giving birth to the complete Dense Point-cloud Representation (DPR) technique. We measure the success of DPR by evaluating the visual appeal, density, accuracy and computational expense of the reconstruction technique and compare with two state-of-the-arts techniques. After the presentation of rigorous analysis and comparison, we conclude by presenting the future direction of development and its plans for deployment in real-world applications.

This unique work presents a detailed review of the processing and analysis of 3D point clouds. A fully automated framework is introduced, incorporating each aspect of a typical end-to-end processing workflow, from raw 3D point cloud data to semantic objects in the scene. For each of these components, the book describes the theoretical background, and compares the performance of the proposed approaches to that of current state-of-the-art techniques. Topics and features: reviews techniques for the acquisition of 3D point cloud data and for point quality assessment; explains the fundamental concepts for extracting features from 2D imagery and 3D point cloud data; proposes an original approach to keypoint-based point cloud registration; discusses the enrichment of 3D point clouds by additional information acquired with a thermal camera, and describes a new method for thermal 3D mapping; presents a novel framework for 3D scene analysis.

Point cloud is a commonly used geometric data type with many applications in computer vision, computer graphics and robotics. The availability of inexpensive 3D sensors has made point cloud data widely available and the current interest in self-driving vehicles has highlighted the importance of reliable and efficient point cloud processing. Due to its irregular format, however, current convolutional deep learning methods cannot be directly used with point clouds. Most researchers transform such data to regular 3D voxel grids or collections of images, which renders data unnecessarily voluminous and causes quantization and other issues. In this thesis, we present novel types of neural networks (PointNet and PointNet++) that directly consume point clouds, in ways that respect the permutation invariance of points in the input. Our network provides a unified architecture for applications ranging from object classification and part segmentation to semantic scene parsing, while being efficient and robust against various input perturbations and data corruption. We provide a theoretical analysis of our approach, showing that our network can approximate any set function that is continuous, and explain its robustness. In PointNet++, we further exploit local contexts in point clouds, investigate the challenge of non-uniform sampling density in common 3D scans, and design new layers that learn to adapt to varying sampling densities. The proposed architectures have opened doors to new 3D-centric approaches to scene understanding. We show how we can adapt and apply PointNets to two important perception problems in robotics: 3D object detection and 3D scene flow estimation. In 3D object detection, we propose a new frustum-based detection framework that achieves 3D instance segmentation and 3D amodal box estimation in point clouds. Our model, called Frustum PointNets, benefits from accurate geometry provided by 3D points and is able to canonicalize the learning problem by applying both non-parametric and data-driven geometric transformations on the inputs. Evaluated on large-scale indoor and outdoor datasets, our real-time detector significantly advances state of the art. In scene flow estimation, we propose a new deep network called FlowNet3D that learns to recover 3D motion flow from two frames of point clouds. Compared with previous work that focuses on 2D representations and optimizes for optical flow, our model directly optimizes 3D scene flow and shows great advantages in evaluations on real LiDAR scans. As point clouds are prevalent, our architectures are not restricted to the above two applications or even 3D scene understanding. This thesis concludes with a discussion on other potential application domains and directions for future research.

One of the grand challenges of artificial intelligence is to enable computers to interpret 3D scenes and objects from imagery. This book organizes and introduces major concepts in 3D scene and object representation and inference from still images, with a focus on recent efforts to fuse models of geometry and perspective with statistical machine learning. The book is organized into three sections: (1) Interpretation of Physical Space; (2) Recognition of 3D Objects; and (3) Integrated 3D Scene Interpretation. The first discusses representations of spatial layout and techniques to interpret physical scenes from images. The second section introduces representations for 3D object categories that account for the intrinsically 3D nature of objects and provide robustness to change in viewpoints. The third section discusses strategies to unite inference of scene geometry and object pose and identity into a coherent scene interpretation. Each section broadly surveys important ideas from cognitive science and artificial intelligence research, organizes and discusses key concepts and techniques from recent work in computer vision, and describes a few sample approaches in detail. Newcomers to computer vision will benefit from introductions to basic concepts, such as single-view geometry and image classification, while experts and novices alike may find inspiration from the book's organization and discussion of the most recent ideas in 3D scene understanding and 3D object recognition. Specific topics include: mathematics of perspective geometry; visual elements of the physical scene, structural 3D scene representations; techniques and features for image and region categorization; historical perspective, computational models, and datasets and machine learning techniques for 3D object recognition; inferences of geometrical attributes of objects, such as size and pose; and probabilistic and feature-passing approaches for contextual reasoning about 3D objects and scenes. Table of Contents: Background on 3D Scene Models / Single-view Geometry / Modeling the Physical Scene / Categorizing Images and Regions / Examples of 3D Scene Interpretation / Background on 3D Recognition / Modeling 3D Objects / Recognizing and Understanding 3D Objects / Examples of 2D 1/2 Layout Models / Reasoning about Objects and Scenes / Cascades of Classifiers / Conclusion and Future Directions

Monocular Model-Based 3D Tracking of Rigid Objects reviews the different techniques and approaches that have been developed by industry and research.

A basic problem in computer vision is to understand the structure of a real world scene given several images of it. Techniques for solving this problem are taken from projective geometry and photogrammetry. Here, the authors cover the geometric principles and their algebraic representation in terms of camera projection matrices, the fundamental matrix and the trifocal tensor. The theory and methods of computation of these entities are discussed with real examples, as is their use in the reconstruction of scenes from multiple images. The new edition features an extended introduction covering the key ideas in the book (which itself has been updated with additional examples and appendices) and significant new results which have appeared since the first edition. Comprehensive background material is provided, so readers familiar with linear algebra and basic numerical methods can understand the projective geometry and estimation algorithms presented, and implement the algorithms directly from the book.

In the past few decades, imaging hardware has improved tremendously in terms of resolution, making widespread usage of images in many diverse applications on Earth and planetary missions. However, practical issues associated with image acquisition are still affecting image quality. Some of these issues such as blurring, measurement noise, mosaicing artifacts, low spatial or spectral resolution, etc. can seriously affect the accuracy of the aforementioned applications. This book intends to provide the reader with a glimpse of the latest developments and recent advances in image restoration, which includes image super-resolution, image fusion to enhance spatial, spectral resolution, and temporal resolutions, and the generation of synthetic images using deep learning techniques. Some practical applications are also included.

CVPR is the premier annual computer vision event comprising the main conference and several co located workshops and short courses With its high quality and low cost, it provides an exceptional value for students, academics and industry researchers

This timely and authoritative volume explores the bidirectional relationship between images and locations. The text presents a comprehensive review of the state of the art in large-scale visual geo-localization, and discusses the emerging trends in this area. Valuable insights are supplied by a pre-eminent selection of experts in the field, into a varied range of real-world applications of geo-localization. Topics and features: discusses the latest methods to exploit internet-scale image databases for devising geographically rich features and geo-localizing query images at different scales; investigates geo-localization techniques that are built upon high-level and semantic cues; describes methods that perform precise localization by geometrically aligning the query image against a 3D model; reviews techniques that accomplish image understanding assisted by the geo-location, as well as several approaches for geo-localization under practical, real-world settings.

The first book, by the leading experts, on this rapidly developing field with applications to security, smart homes, multimedia, and environmental monitoring Comprehensive coverage of fundamentals, algorithms, design methodologies, system implementation issues, architectures, and applications Presents in detail the latest developments in multi-camera calibration, active and heterogeneous camera networks, multi-camera object and event detection, tracking, coding, smart camera architecture and middleware This book is the definitive reference in multi-camera networks. It gives clear guidance on the conceptual and implementation issues involved in the design and operation of multi-camera networks, as well as presenting the state-of-the-art in hardware, algorithms and system development. The book is broad in scope, covering smart camera architectures, embedded processing, sensor fusion and middleware, calibration and topology, network-based detection and tracking, and applications in distributed and collaborative methods in camera networks. This book will be an ideal reference for university researchers, R&D engineers, computer engineers, and graduate students working in signal and video processing, computer vision, and sensor networks. Hamid Aghajan is a Professor of Electrical Engineering (consulting) at Stanford University. His research is on multi-camera networks for smart environments with application to smart homes, assisted living and well being, meeting rooms, and avatar-based communication and social interactions. He is Editor-in-Chief of Journal of Ambient Intelligence and Smart Environments, and was general chair of ACM/IEEE ICDSC 2008. Andrea Cavallaro is Reader (Associate Professor) at Queen Mary, University of London (QMUL). His research is on target tracking and audiovisual content analysis for advanced surveillance and multi-sensor systems. He serves as Associate Editor of the IEEE Signal Processing Magazine and the IEEE Trans. on Multimedia, and has been general chair of IEEE AVSS 2007, ACM/IEEE ICDSC 2009 and BMVC 2009. The first book, by the leading experts, on this rapidly developing field with applications to security, smart homes, multimedia, and environmental monitoring Comprehensive coverage of fundamentals, algorithms, design methodologies, system implementation issues, architectures, and applications Presents in detail the latest developments in multi-camera calibration, active and heterogeneous camera networks, multi-camera object and event detection, tracking, coding, smart camera architecture and middleware