Download or read book Scene Understanding for 3D Multi object Scenes written by Zoe Landgraf and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Representations and Techniques for 3D Object Recognition and Scene Interpretation written by Derek Hoiem and published by Morgan & Claypool Publishers. This book was released on 2011 with total page 172 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Download or read book Computer Vision ECCV 2010 written by Kostas Daniilidis and published by Springer Science & Business Media. This book was released on 2010-08-30 with total page 836 pages. Available in PDF, EPUB and Kindle. Book excerpt: The six-volume set comprising LNCS volumes 6311 until 6313 constitutes the refereed proceedings of the 11th European Conference on Computer Vision, ECCV 2010, held in Heraklion, Crete, Greece, in September 2010. The 325 revised papers presented were carefully reviewed and selected from 1174 submissions. The papers are organized in topical sections on object and scene recognition; segmentation and grouping; face, gesture, biometrics; motion and tracking; statistical models and visual learning; matching, registration, alignment; computational imaging; multi-view geometry; image features; video and event characterization; shape representation and recognition; stereo; reflectance, illumination, color; medical image analysis.

Download or read book Probabilistic Models for 3D Urban Scene Understanding from Movable Platforms written by Andreas Geiger and published by KIT Scientific Publishing. This book was released on 2014-07-29 with total page 196 pages. Available in PDF, EPUB and Kindle. Book excerpt: This work is a contribution to understanding multi-object traffic scenes from video sequences. All data is provided by a camera system which is mounted on top of the autonomous driving platform AnnieWAY. The proposed probabilistic generative model reasons jointly about the 3D scene layout as well as the 3D location and orientation of objects in the scene. In particular, the scene topology, geometry as well as traffic activities are inferred from short video sequences.

Download or read book Reconstruction and Analysis of 3D Scenes written by Martin Weinmann and published by Springer. This book was released on 2016-03-17 with total page 250 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Probabilistic Models for 3D Urban Scene Understanding From Movable Platforms written by Andreas Geiger and published by . This book was released on 2020-10-09 with total page 192 pages. Available in PDF, EPUB and Kindle. Book excerpt: This work is a contribution to understanding multi-object traffic scenes from video sequences. All data is provided by a camera system which is mounted on top of the autonomous driving platform AnnieWAY. The proposed probabilistic generative model reasons jointly about the 3D scene layout as well as the 3D location and orientation of objects in the scene. In particular, the scene topology, geometry as well as traffic activities are inferred from short video sequences. This work was published by Saint Philip Street Press pursuant to a Creative Commons license permitting commercial use. All rights not granted by the work's license are retained by the author or authors.

Download or read book 3D Scene Modeling and Understanding from Image Sequences written by Hao Tang and published by . This book was released on 2013 with total page 188 pages. Available in PDF, EPUB and Kindle. Book excerpt: A new method for 3D modeling is proposed, which generates a content-based 3D mosaic (CB3M) representation for long video sequences of 3D, dynamic urban scenes captured by a camera on a mobile platform. In the first phase, a set of parallel-perspective (pushbroom) mosaics with varying viewing directions is generated to capture both the 3D and dynamic aspects of the scene under the camera coverage. In the second phase, a unified patch-based stereo matching algorithm is applied to extract parametric representations of the color, structure and motion of the dynamic and/or 3D objects in urban scenes, where a lot of planar surfaces exist. Multiple pairs of stereo mosaics are used for facilitating reliable stereo matching, occlusion handling, accurate 3D reconstruction and robust moving target detection. The outcome of this phase is a CB3M representation, which is a highly compressed visual representation for a dynamic 3D scene, and has object contents of both 3D and motion information. In the third phase, a multi-layer based scene understanding algorithm is proposed, resulting in a planar surface model for higher-level object representations. Experimental results are given for both simulated and several different real video sequences of large-scale 3D scenes to show the accuracy and effectiveness of the representation. We also show the patch-based stereo matching algorithm and the CB3M representation can be generalized to 3D modeling with perspective views using either a single camera or a stereovision head on a ground mobile platform or a pedestrian. Applications of the proposed method include airborne or ground video surveillance, 3D urban scene modeling, traffic survey, transportation planning and the visual aid for perception and navigation of blind people.

Download or read book Multimodal Scene Understanding written by Michael Yang and published by Academic Press. This book was released on 2019-07-16 with total page 422 pages. Available in PDF, EPUB and Kindle. Book excerpt: Multimodal Scene Understanding: Algorithms, Applications and Deep Learning presents recent advances in multi-modal computing, with a focus on computer vision and photogrammetry. It provides the latest algorithms and applications that involve combining multiple sources of information and describes the role and approaches of multi-sensory data and multi-modal deep learning. The book is ideal for researchers from the fields of computer vision, remote sensing, robotics, and photogrammetry, thus helping foster interdisciplinary interaction and collaboration between these realms. Researchers collecting and analyzing multi-sensory data collections – for example, KITTI benchmark (stereo+laser) - from different platforms, such as autonomous vehicles, surveillance cameras, UAVs, planes and satellites will find this book to be very useful. Contains state-of-the-art developments on multi-modal computing Shines a focus on algorithms and applications Presents novel deep learning topics on multi-sensor fusion and multi-modal deep learning

Download or read book Two dimensional Plus Three dimensional Rich Data Approach to Scene Understanding written by Jianxiong Xiao and published by . This book was released on 2013 with total page 227 pages. Available in PDF, EPUB and Kindle. Book excerpt: On your one-minute walk from the coffee machine to your desk each morning, you pass by dozens of scenes - a kitchen, an elevator, your office - and you effortlessly recognize them and perceive their 3D structure. But this one-minute scene-understanding problem has been an open challenge in computer vision since the field was first established 50 years ago. In this dissertation, we aim to rethink the path researchers took over these years, challenge the standard practices and implicit assumptions in the current research, and redefine several basic principles in computational scene understanding. The key idea of this dissertation is that learning from rich data under natural setting is crucial for finding the right representation for scene understanding. First of all, to overcome the limitations of object-centric datasets, we built the Scene Understanding (SUN) Database, a large collection of real-world images that exhaustively spans all scene categories. This scene-centric dataset provides a more natural sample of human visual world, and establishes a realistic benchmark for standard 2D recognition tasks. However, while an image is a 2D array, the world is 3D and our eyes see it from a viewpoint, but this is not traditionally modeled. To obtain a 3D understanding at high-level, we reintroduce geometric figures using modern machinery. To model scene viewpoint, we propose a panoramic place representation to go beyond aperture computer vision and use data that is close to natural input for human visual system. This paradigm shift toward rich representation also opens up new challenges that require a new kind of big data - data with extra descriptions, namely rich data. Specifically, we focus on a highly valuable kind of rich data - multiple viewpoints in 3D - and we build the SUN3D database to obtain an integrated place-centric representation of scenes. We argue for the great importance of modeling the computer's role as an agent in a 3D scene, and demonstrate the power of place-centric scene representation.

Download or read book 3D Scene Understanding with Efficient Spatio temporal Reasoning written by JunYoung Gwak and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Robust and efficient 3D scene understanding could enable embodied agents to safely interact with the physical world in real-time. The key to the remarkable success of computer vision in the last decade owes to the rediscovery of convolutional neural networks. However, this technology does not always directly translate to 3D due to the curse of dimensionality. The size of the data grows cubically with the voxels, and the same level of input resolution and network depth was infeasible compared to that of 2D. Based on the observation that the 3D space is mostly empty, sparse tensors and sparse convolutions stand out as an efficient and effective 3D counterparts to the 2D convolution by exclusively operating on non-empty spaces. Such efficiency gain supports deeper neural networks for higher accuracy in real-time reference speed. To this end, this thesis explores the application of sparse convolution to various 3D scene understanding tasks. This thesis breaks down a holistic 3D scene understanding pipeline into the following subgoals; 1. data collection from 3D reconstruction, 2. semantic segmentation, 3. object detection, and 4. multi-object tracking. With robotics applications in mind, this thesis aims to achieve better performance, scalability, and efficiency in understanding the high-level semantics of the spatio-temporal domain while addressing the unique challenges the sparse data poses. In this thesis, we propose generalized sparse convolution and demonstrate how our method 1. gains efficiency by leveraging the sparseness of the 3D point cloud, 2. achieves robust performance by utilizing the gained efficiency, 3. makes predictions on empty spaces by dynamically generating points, and 4. jointly solves detection and tracking with spatio-temporal reasoning. Altogether, this thesis proposes an efficient and reliable pipeline for a holistic 3D scene understanding.

Download or read book Computer Vision ECCV 2014 written by David Fleet and published by Springer. This book was released on 2014-08-14 with total page 855 pages. Available in PDF, EPUB and Kindle. Book excerpt: The seven-volume set comprising LNCS volumes 8689-8695 constitutes the refereed proceedings of the 13th European Conference on Computer Vision, ECCV 2014, held in Zurich, Switzerland, in September 2014. The 363 revised papers presented were carefully reviewed and selected from 1444 submissions. The papers are organized in topical sections on tracking and activity recognition; recognition; learning and inference; structure from motion and feature matching; computational photography and low-level vision; vision; segmentation and saliency; context and 3D scenes; motion and 3D scene analysis; and poster sessions.

Download or read book Single View 3D Reconstruction and Parsing Using Geometric Commonsense for Scene Understanding written by Chengcheng Yu and published by . This book was released on 2017 with total page 105 pages. Available in PDF, EPUB and Kindle. Book excerpt: My thesis studies this topic in three perspective: (1) 3D scene reconstruction to understand the 3D structure of a scene. (2) Geometry and physics reasoning to understand the relationships of objects in a scene. (3) The interaction between human action and objects in a scene. Specifically, the 3D reconstruction builds a unified grammatical framework capable of reconstructing a variety of scene types (e.g., urban, campus, county etc.) from a single input image. The key idea of our approach is to study a novel commonsense reasoning framework that mainly exploits two types of prior knowledges: (i) prior distributions over a single dimension of objects, e.g., that the length of a sedan is about 4.5 meters; (ii) pair-wise relationships between the dimensions of scene entities, e.g., that the length of a sedan is shorter than a bus. These unary or relative geometric knowledge, once extracted, are fairly stable across different types of natural scenes, and are informative for enhancing the understanding of various scenes in both 2D images and 3D world. Methodologically, we propose to construct a hierarchical graph representation as a unified representation of the input image and related geometric knowledge. We formulate these objectives with a unified probabilistic formula and develop a data-driven Monte Carlo method to infer the optimal solution with both bottom-to-up and top-down computations. Results with comparisons on public datasets showed that our method clearly outperforms the alternative methods. For geometry and physics reasoning, we present an approach for scene understanding by reasoning physical stability of objects from point cloud. We utilize a simple observation that, by human design, objects in static scenes should be stable with respect to gravity. This assumption is applicable to all scene categories and poses useful constraints for the plausible interpretations (parses) in scene understanding. Our method consists of two major steps: 1) geometric reasoning: recovering solid 3D volumetric primitives from defective point cloud; and 2) physical reasoning: grouping the unstable primitives to physically stable objects by optimizing the stability and the scene prior. We propose to use a novel disconnectivity graph (DG) to represent the energy landscape and use a Swendsen-Wang Cut (MCMC) method for optimization. In experiments, we demonstrate that the algorithm achieves substantially better performance for i) object segmentation, ii) 3D volumetric recovery of the scene, and iii) better parsing result for scene understanding in comparison to state-of-the-art methods in both public dataset and our own new dataset. Detecting potential dangers in the environment is a fundamental ability of living beings. In order to endure such ability to a robot, my thesis presents an algorithm for detecting potential falling objects, i.e. physically unsafe objects, given an input of 3D point clouds captured by the range sensors. We formulate the falling risk as a probability or a potential that an object may fall given human action or certain natural disturbances, such as earthquake and wind. Our approach differs from traditional object detection paradigm, it first infers hidden and situated "causes (disturbance) of the scene, and then introduces intuitive physical mechanics to predict possible "effects (falls) as consequences of the causes. In particular, we infer a disturbance field by making use of motion capture data as a rich source of common human pose movement. We show that, by applying various disturbance fields, our model achieves a human level recognition rate of potential falling objects on a dataset of challenging and realistic indoor scenes.

Download or read book Holistic Scene Understanding and Goal directed Multi agent Event Parsing written by Yixin Chen and published by . This book was released on 2022 with total page 142 pages. Available in PDF, EPUB and Kindle. Book excerpt: Humans, even young infants, are adept at perceiving and understanding complex indoor scenes and events. Holistic scene understanding involves abundant aspects, including 3D human pose, objects, physical relations, functionality, etc. Besides the physical and functional configuration of the scene, interpreting human actions and goal-oriented tasks is a higher-level goal, and requires reasoning about the complex structures in activities along the temporal dimension. When multiple people are in the scene, collaborations and communications inevitably happen, in both verbal and non-verbal forms. Despite the recent remarkable progress in artificial intelligence, building an intelligent machine with human-like perception and reasoning capability for the aforementioned complex tasks remains a significant and challenging problem.In this dissertation, we study the holistic scene understanding and goal-directed multi-agent event parsing by identifying the critical problems from various perspectives. We first propose a framework for holistic 3D scene parsing and human pose estimation, with a particular focus on human-object interaction and physical commonsense reasoning. Contact information is critical in modeling the fine-grained human-object relations from visual cues. We demonstrate how to extract meaningful contact information from 2D images and its usefulness in 3D human pose estimation. Then we introduce our efforts in understanding goal-directed actions, concurrent multi-tasks, and collaborations among multi-agents. Finally, we investigate the two typical types of human communications by proposing a spatial and temporal model for shared attention and examining the power of both language and gesture under the embodied reference setting.

Download or read book Machine Vision for Three dimensional Scenes written by Herbert Freeman and published by . This book was released on 1990 with total page 480 pages. Available in PDF, EPUB and Kindle. Book excerpt: A framework for 3D recognition / Ruud M. Bolle and Andrea Califano -- The free-form surface matching problem / Paul J. Besl -- Object recognition by constrained search / W. Eric L. Grimson -- The use of characteristic-view classes for 3D object recognition / Ruye Wang and Herbert Freeman -- Interpretation of 3D medical scenes / C. Smets [and others] -- 3D motion estimation / T.S. Huang and A.N. Netravali -- Project LESTRADE : the design of a trainable machine vision inspection system / Herbert Freeman -- Fast 3D integrated circuit inspection / Arend van de Stadt and Albert Sicignano -- Segmentation and analysis of multi-sensor images / J.K. Aggarwal -- Occlusion-free sensor placement planning / Roger Y. Tsai and Kostantino TarabanisThe state of the art in real-time range mapping : a panel discussion / Joseph Wilder -- Generalized and separable Sobel operators / Per-Erik Danielsson and Olle Seger -- A fast lightstripe rangefinding system with smart VLSI sensor / Andrew Gruss, Takeo Kana ...

Download or read book Machine Vision for Three Dimensional Scenes written by Herbert Freeman and published by Elsevier. This book was released on 2012-12-02 with total page 432 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Vision for Three-Dimensional Scenes contains the proceedings of the workshop "Machine Vision - Acquiring and Interpreting the 3D Scene" sponsored by the Center for Computer Aids for Industrial Productivity (CAIP) at Rutgers University and held in April 1989 in New Brunswick, New Jersey. The papers explore the applications of machine vision in image acquisition and 3D scene interpretation and cover topics such as segmentation of multi-sensor images; the placement of sensors to minimize occlusion; and the use of light striping to obtain range data. Comprised of 14 chapters, this book opens with a discussion on 3D object recognition and the problems that arise when dealing with large object databases, along with solutions to these problems. The reader is then introduced to the free-form surface matching problem and object recognition by constrained search. The following chapters address the problem of machine vision inspection, paying particular attention to the use of eye tracking to train a vision system; images of 3D scenes and the attendant problems of image understanding; the problem of object motion; and real-time range mapping. The final chapter assesses the relationship between the developing machine vision technology and the marketplace. This monograph will be of interest to practitioners in the fields of computer science and applied mathematics.

Download or read book Computer Vision ECCV 2012 written by Andrew Fitzgibbon and published by Springer. This book was released on 2012-09-26 with total page 913 pages. Available in PDF, EPUB and Kindle. Book excerpt: The seven-volume set comprising LNCS volumes 7572-7578 constitutes the refereed proceedings of the 12th European Conference on Computer Vision, ECCV 2012, held in Florence, Italy, in October 2012. The 408 revised papers presented were carefully reviewed and selected from 1437 submissions. The papers are organized in topical sections on geometry, 2D and 3D shapes, 3D reconstruction, visual recognition and classification, visual features and image matching, visual monitoring: action and activities, models, optimisation, learning, visual tracking and image registration, photometry: lighting and colour, and image segmentation.

Download or read book On Hierarchical Models for Visual Recognition and Learning of Objects Scenes and Activities written by Jens Spehr and published by Springer. This book was released on 2014-11-13 with total page 210 pages. Available in PDF, EPUB and Kindle. Book excerpt: In many computer vision applications, objects have to be learned and recognized in images or image sequences. This book presents new probabilistic hierarchical models that allow an efficient representation of multiple objects of different categories, scales, rotations, and views. The idea is to exploit similarities between objects and object parts in order to share calculations and avoid redundant information. Furthermore inference approaches for fast and robust detection are presented. These new approaches combine the idea of compositional and similarity hierarchies and overcome limitations of previous methods. Besides classical object recognition the book shows the use for detection of human poses in a project for gait analysis. The use of activity detection is presented for the design of environments for ageing, to identify activities and behavior patterns in smart homes. In a presented project for parking spot detection using an intelligent vehicle, the proposed approaches are used to hierarchically model the environment of the vehicle for an efficient and robust interpretation of the scene in real-time.