3d Panoptic Segmentation With Unsupervised Clustering For Visual Perception In Autonomous Driving

The understanding and interpretation of complex 3D environments is a key challenge of autonomous driving. Lidar sensors and their recorded point clouds are particularly interesting for this challenge since they provide accurate 3D information about the environment. This work presents a multimodal approach based on deep learning for panoptic segmentation of 3D point clouds. It builds upon and combines the three key aspects multi view architecture, temporal feature fusion, and deep sensor fusion.

Visual perception is the ability to perceive the environment, which is a critical component in decision-making that builds safer automated driving. Recent progress in computer vision and deep learning paired with high-quality sensors like cameras and LiDARs fueled mature visual perception solutions. The main bottleneck for these solutions is the limited processing power available to build real-time applications. This bottleneck often leads to a trade-off between performance and run-time efficiency. To address these bottlenecks, we focus on: 1) building optimized architectures for different visual perception tasks like semantic segmentation, panoptic segmentation, etc. using convolutional neural networks that have high performance and low computational complexity, 2) using multi-task learning to overcome computational bottlenecks by sharing the initial convolutional layers between different tasks while developing advanced learning strategies that achieve balanced learning between tasks

Discover the captivating world of computer vision and deep learning for autonomous driving with our comprehensive and in-depth guide. Immerse yourself in an in-depth exploration of cutting-edge topics, carefully crafted to engage tertiary students and ignite the curiosity of researchers and professionals in the field. From fundamental principles to practical applications, this comprehensive guide offers a gentle introduction, expert evaluations of state-of-the-art methods, and inspiring research directions. With a broad range of topics covered, it is also an invaluable resource for university programs offering computer vision and deep learning courses. This book provides clear and simplified algorithm descriptions, making it easy for beginners to understand the complex concepts. We also include carefully selected problems and examples to help reinforce your learning. Don't miss out on this essential guide to computer vision and deep learning for autonomous driving.

The automation of mechanical tasks brought the modern world unprecedented prosperity and comfort. However, the majority of automated tasks have been simple mechanical tasks that only require repetitive motion. Tasks that require visual perception and high-level cognition still have become the last frontiers of automation. Many of these tasks require visual perception such as automated warehouses where robots package items in disarray, autonomous driving where autonomous agents localize themselves, identify and track other dynamic objects in the 3D world. This ability to represent, identify, and interpret visual three-dimensional data to understand the underlying three-dimensional structure in the real world is known as 3D perception. In this dissertation, we propose learning-based approaches to tackle challenges in 3D perception. Specifically, we propose a set of high-dimensional convolutional neural networks for three categories of problems in 3D perception: reconstruction, representation learning, and registration. Reconstruction is the first step that generates 3D point clouds or meshes from a set of sensory inputs. We present supervised reconstruction methods using 3D convolutional neural networks that take a set of images as input and generate 3D occupancy patterns in a grid as output. We train the networks with a large-scale 3D shape dataset to generate a set of images rendered from various viewpoints validate the approach on real image datasets. However, supervised reconstruction requires 3D shapes as labels for all images, which are expensive to generate. Instead, we propose using a set of foreground masks and unlabeled real 3D shapes to train the reconstruction network as weaker supervision. Combined with the learned constraint, we train the reconstruction system with as few as 1 image and show that the proposed model without direct 3D supervision. In the second part of the dissertation, we present sparse tensor networks, neural networks for spatially sparse tensors. As we increase the spatial dimension, the sparsity of input data decreases drastically as the volume of the space increases exponentially. Sparse tensor networks exploit such inherent sparsity in the input data and efficiently process them. With the sparse tensor network, we create a 4-dimensional convolutional network for spatio-temporal perception for 3D scans or a sequence of 3D scans (3D video). We show that 4-dimensional convolutional neural networks can effectively make use of temporal consistency and improve the accuracy of segmentation. Next, we use the sparse tensor networks for geometric representation learning to capture both local and global 3D structures accurately for correspondences and registration. We propose fully convolutional networks and new types of metric learning losses that allow neurons to capture large context while capturing local spatial geometry. We experimentally validate our approach on both indoor and outdoor datasets and show that the network outperforms the state-of-the-art method while being a few orders of magnitude faster. In the third and the last part of the dissertation, we discuss high-dimensional pattern recognition problems in image and 3D registration. We first propose high-dimensional convolutional networks from 4 to 32-dimensional spaces and analyze the geometric pattern recognition capacity of these high-dimensional convolutional networks for linear regression problems. Next, we show that the 3D correspondences form a hyper-surface in 6-dimensional space; and 2D correspondences form a 4-dimensional hyper-conic section, which we detect using high-dimensional convolutional networks. We extend the proposed high-dimensional convolutional networks for differentiable 3D registration and propose three core modules for this: a 6-dimensional convolutional neural network for correspondence confidence prediction; a differentiable Weighted Procrustes method for closed-form pose estimation; and a robust gradient-based 3D rigid transformation optimizer for pose refinement. Experiments demonstrate that our approach outperforms state-of-the-art learning-based and classical methods on real-world data while maintaining efficiency.

The first book of its kind dedicated to the challenge of person re-identification, this text provides an in-depth, multidisciplinary discussion of recent developments and state-of-the-art methods. Features: introduces examples of robust feature representations, reviews salient feature weighting and selection mechanisms and examines the benefits of semantic attributes; describes how to segregate meaningful body parts from background clutter; examines the use of 3D depth images and contextual constraints derived from the visual appearance of a group; reviews approaches to feature transfer function and distance metric learning and discusses potential solutions to issues of data scalability and identity inference; investigates the limitations of existing benchmark datasets, presents strategies for camera topology inference and describes techniques for improving post-rank search efficiency; explores the design rationale and implementation considerations of building a practical re-identification system.

This book introduces the applications of deep learning in various human centric visual analysis tasks, including classical ones like face detection and alignment and some newly rising tasks like fashion clothing parsing. Starting from an overview of current research in human centric visual analysis, the book then presents a tutorial of basic concepts and techniques of deep learning. In addition, the book systematically investigates the main human centric analysis tasks of different levels, ranging from detection and segmentation to parsing and higher-level understanding. At last, it presents the state-of-the-art solutions based on deep learning for every task, as well as providing sufficient references and extensive discussions. Specifically, this book addresses four important research topics, including 1) localizing persons in images, such as face and pedestrian detection; 2) parsing persons in details, such as human pose and clothing parsing, 3) identifying and verifying persons, such as face and human identification, and 4) high-level human centric tasks, such as person attributes and human activity understanding. This book can serve as reading material and reference text for academic professors / students or industrial engineers working in the field of vision surveillance, biometrics, and human-computer interaction, where human centric visual analysis are indispensable in analysing human identity, pose, attributes, and behaviours for further understanding.

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

Deep Learning for Robot Perception and Cognition introduces a broad range of topics and methods in deep learning for robot perception and cognition together with end-to-end methodologies. The book provides the conceptual and mathematical background needed for approaching a large number of robot perception and cognition tasks from an end-to-end learning point-of-view. The book is suitable for students, university and industry researchers and practitioners in Robotic Vision, Intelligent Control, Mechatronics, Deep Learning, Robotic Perception and Cognition tasks. Presents deep learning principles and methodologies Explains the principles of applying end-to-end learning in robotics applications Presents how to design and train deep learning models Shows how to apply deep learning in robot vision tasks such as object recognition, image classification, video analysis, and more Uses robotic simulation environments for training deep learning models Applies deep learning methods for different tasks ranging from planning and navigation to biosignal analysis

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.