Download or read book Human like Holistic 3D Scene Understanding written by Siyuan Huang and published by . This book was released on 2021 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt: Building an intelligent machine with human-like perception, interaction, learning, and reasoning remains a significant and challenging problem. Despite the recent remarkable progress in artificial intelligence, especially the deep learning techniques, we are still far from reaching this goal. Human intelligence exhibits unique advantages in learning to solve multiple tasks from limited data, acquiring skills and knowledge from interactions, learning efficiently with stages, and generalizing concepts to novel domains and environments. Merely combining individual algorithms without a human-centric architecture is hopeless for achieving such comprehensive capabilities. In this dissertation, we study the human-like holistic understanding in 3D scenes, which is the most related scenario to the real world. The core idea is to imitate the human's capability in perception, interaction, learning, and reasoning for solving holistic tasks. We first propose a framework for human-centric 3D scene parsing, reconstruction, and synthesis, focusing on integrating imagined humans into the perception system for interpreting the underlying human activities and intentions beyond the pixels. Then we describe several works on human-centric interaction understanding, including the human-object interactions and human-human interactions. Finally, we imitate the human-like learning and reasoning abilities by studying how to learn concepts with curriculum, design efficient closed-loop neural-grammar-symbolic learning algorithm, and build a concept learning framework that achieves systematic generalization.

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 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 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 2020 written by Andrea Vedaldi and published by Springer Nature. This book was released on 2020-11-04 with total page 861 pages. Available in PDF, EPUB and Kindle. Book excerpt: The 30-volume set, comprising the LNCS books 12346 until 12375, constitutes the refereed proceedings of the 16th European Conference on Computer Vision, ECCV 2020, which was planned to be held in Glasgow, UK, during August 23-28, 2020. The conference was held virtually due to the COVID-19 pandemic. The 1360 revised papers presented in these proceedings were carefully reviewed and selected from a total of 5025 submissions. The papers deal with topics such as computer vision; machine learning; deep neural networks; reinforcement learning; object recognition; image classification; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation.

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 Computer Vision ECCV 2022 written by Shai Avidan and published by Springer Nature. This book was released on 2022-10-20 with total page 806 pages. Available in PDF, EPUB and Kindle. Book excerpt: The 39-volume set, comprising the LNCS books 13661 until 13699, constitutes the refereed proceedings of the 17th European Conference on Computer Vision, ECCV 2022, held in Tel Aviv, Israel, during October 23–27, 2022. The 1645 papers presented in these proceedings were carefully reviewed and selected from a total of 5804 submissions. The papers deal with topics such as computer vision; machine learning; deep neural networks; reinforcement learning; object recognition; image classification; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation.

Download or read book Seeing the World Behind the Image written by Derek Hoiem and published by . This book was released on 2007 with total page 147 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: "When humans look at an image, they see not just a pattern of color and texture, but the world behind the image. In the same way, computer vision algorithms must go beyond the pixels and reason about the underlying scene. In this dissertation, we propose methods to recover the basic spatial layout from a single image and begin to investigate its use as a foundation for scene understanding. Our spatial layout is a description of the 3D scene in terms of surfaces, occlusions, camera viewpoint, and objects. We propose a geometric class representation, a coarse categorization of surfaces according to their 3D orientations, and learn appearance-based models of geometry to identify surfaces in an image. These surface estimates serve as a basis for recovering the boundaries and occlusion relationships of prominent objects. We further show that simple reasoning about camera viewpoint and object size in the image allows accurate inference of the viewpoint and greatly improves object detection. Finally, we demonstrate the potential usefulness of our methods in applications to 3D reconstruction, scene synthesis, and robot navigation. Scene understanding from a single image requires strong assumptions about the world. We show that the necessary assumptions can be modeled statistically and learned from training data. Our work demonstrates the importance of robustness through a wide variety of image cues, multiple segmentations, and a general strategy of soft decisions and gradual inference of image structure. Above all, our work manifests the tremendous amount of 3D information that can be gleaned from a single image. Our hope is that this dissertation will inspire others to further explore how computer vision can go beyond pattern recognition and produce an understanding of the environment."

Download or read book 3D Scene Understanding from a Single Image written by Wei Zeng and published by . This book was released on 2021 with total page 101 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Computer Vision written by Li Fei-Fei and published by Morgan & Claypool. This book was released on 2013-02-01 with total page 120 pages. Available in PDF, EPUB and Kindle. Book excerpt: When a 3-dimensional world is projected onto a 2-dimensional image, such as the human retina or a photograph, reconstructing back the layout and contents of the real-world becomes an ill-posed problem that is extremely difficult to solve. Humans possess the remarkable ability to navigate and understand the visual world by solving the inversion problem going from 2D to 3D. Computer Vision seeks to imitate such abilities of humans to recognize objects, navigate scenes, reconstruct layouts, and understand the geometric space and semantic meaning of the visual world. These abilities are critical in many applications including robotics, autonomous driving and exploration, photo organization, image, or video retrieval, and human-computer interaction. This book delivers a systematic overview of computer vision, comparable to that presented in an advanced graduate level class. The authors emphasize two key issues in modeling vision: space and meaning, and focus upon the main problems vision needs to solve, including: * mapping out the 3D structure of objects and scenes* recognizing objects* segmenting objects* recognizing meaning of scenes* understanding movements of humansMotivated by these important problems and centered on the understanding of space and meaning, the book explores the fundamental theories and important algorithms of computer vision, starting from the analysis of 2D images, and culminating in the holistic understanding of a 3D scene

Download or read book A Cognition Platform for Joint Inference of 3D Geometry Object States and Human Belief written by Tao Yuan and published by . This book was released on 2019 with total page 95 pages. Available in PDF, EPUB and Kindle. Book excerpt: Humans can extract rich information from visual scenes, such as the 3D locations of objects and humans, the actions of humans, the states of objects, the belief of humans. Although various state-of-the-art algorithms can achieve good results for solving individual tasks, building a system to jointly infer these different tasks for scene understanding is still an underexplored area. Most of these tasks are not independent with each other, and humans can jointly infer hidden information with their commonsense knowledge among these tasks. In this dissertation, we propose a spatio-temporal framework to jointly infer and optimize multiple tasks across different times and views with a unified explicit probabilistic graphical representation. This dissertation contains four main parts. 1) we describe the system overview, the data flow in the system, and engineering efforts to make the system scalable under different scenarios. 2) we propose an algorithm for holistic 3D scene parsing and human pose estimation with human-object interaction and physical commonsense. Human-object interaction can model the fine-grained relations between agents and objects, and physical commonsense can model the physical plausibility of the reconstructed scene. 3) we introduce a joint parsing framework that integrates view-centric proposals into scene-centric parse graphs that represent a coherent scene-centric understanding of cross-view scenes. 4) we present a joint inference algorithm to understanding object states, robot knowledge, and human beliefs under multi-view settings by maintaining three types of parse graphs. The algorithm can be applied to the cross-view small object tracking problem and some false-belief problems. Experiments show that our joint inference framework can achieve better results than individual algorithms.

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 Computer Vision ECCV 2018 written by Vittorio Ferrari and published by Springer. This book was released on 2018-10-05 with total page 881 pages. Available in PDF, EPUB and Kindle. Book excerpt: The sixteen-volume set comprising the LNCS volumes 11205-11220 constitutes the refereed proceedings of the 15th European Conference on Computer Vision, ECCV 2018, held in Munich, Germany, in September 2018.The 776 revised papers presented were carefully reviewed and selected from 2439 submissions. The papers are organized in topical sections on learning for vision; computational photography; human analysis; human sensing; stereo and reconstruction; optimization; matching and recognition; video attention; and poster sessions.

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 Computer Vision ECCV 2022 Workshops written by Leonid Karlinsky and published by Springer Nature. This book was released on 2023-02-18 with total page 805 pages. Available in PDF, EPUB and Kindle. Book excerpt: The 8-volume set, comprising the LNCS books 13801 until 13809, constitutes the refereed proceedings of 38 out of the 60 workshops held at the 17th European Conference on Computer Vision, ECCV 2022. The conference took place in Tel Aviv, Israel, during October 23-27, 2022; the workshops were held hybrid or online. The 367 full papers included in this volume set were carefully reviewed and selected for inclusion in the ECCV 2022 workshop proceedings. They were organized in individual parts as follows: Part I: W01 - AI for Space; W02 - Vision for Art; W03 - Adversarial Robustness in the Real World; W04 - Autonomous Vehicle Vision Part II: W05 - Learning With Limited and Imperfect Data; W06 - Advances in Image Manipulation; Part III: W07 - Medical Computer Vision; W08 - Computer Vision for Metaverse; W09 - Self-Supervised Learning: What Is Next?; Part IV: W10 - Self-Supervised Learning for Next-Generation Industry-Level Autonomous Driving; W11 - ISIC Skin Image Analysis; W12 - Cross-Modal Human-Robot Interaction; W13 - Text in Everything; W14 - BioImage Computing; W15 - Visual Object-Oriented Learning Meets Interaction: Discovery, Representations, and Applications; W16 - AI for Creative Video Editing and Understanding; W17 - Visual Inductive Priors for Data-Efficient Deep Learning; W18 - Mobile Intelligent Photography and Imaging; Part V: W19 - People Analysis: From Face, Body and Fashion to 3D Virtual Avatars; W20 - Safe Artificial Intelligence for Automated Driving; W21 - Real-World Surveillance: Applications and Challenges; W22 - Affective Behavior Analysis In-the-Wild; Part VI: W23 - Visual Perception for Navigation in Human Environments: The JackRabbot Human Body Pose Dataset and Benchmark; W24 - Distributed Smart Cameras; W25 - Causality in Vision; W26 - In-Vehicle Sensing and Monitorization; W27 - Assistive Computer Vision and Robotics; W28 - Computational Aspects of Deep Learning; Part VII: W29 - Computer Vision for Civil and Infrastructure Engineering; W30 - AI-Enabled Medical Image Analysis: Digital Pathology and Radiology/COVID19; W31 - Compositional and Multimodal Perception; Part VIII: W32 - Uncertainty Quantification for Computer Vision; W33 - Recovering 6D Object Pose; W34 - Drawings and Abstract Imagery: Representation and Analysis; W35 - Sign Language Understanding; W36 - A Challenge for Out-of-Distribution Generalization in Computer Vision; W37 - Vision With Biased or Scarce Data; W38 - Visual Object Tracking Challenge.

Download or read book Handbook of Deep Learning Applications written by Valentina Emilia Balas and published by Springer. This book was released on 2019-02-25 with total page 383 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a broad range of deep-learning applications related to vision, natural language processing, gene expression, arbitrary object recognition, driverless cars, semantic image segmentation, deep visual residual abstraction, brain–computer interfaces, big data processing, hierarchical deep learning networks as game-playing artefacts using regret matching, and building GPU-accelerated deep learning frameworks. Deep learning, an advanced level of machine learning technique that combines class of learning algorithms with the use of many layers of nonlinear units, has gained considerable attention in recent times. Unlike other books on the market, this volume addresses the challenges of deep learning implementation, computation time, and the complexity of reasoning and modeling different type of data. As such, it is a valuable and comprehensive resource for engineers, researchers, graduate students and Ph.D. scholars.

Download or read book Computer Vision ECCV 2024 written by Aleš Leonardis and published by Springer Nature. This book was released on with total page 590 pages. Available in PDF, EPUB and Kindle. Book excerpt: