Factorized Inverse Path Tracing for Efficient and Accurate Material-Lighting Estimation

Inverse path tracing has recently been applied to joint material and lighting estimation, given geometry and multi-view HDR observations of an indoor scene. However, it has two major limitations: path tracing is expensive to compute, and ambiguities exist between reflection and emission. Our Factorized Inverse Path Tracing (FIPT) addresses these challenges by using a factored light transport formulation and finds emitters driven by rendering errors. Our algorithm enables accurate material and lighting optimization faster than previous work, and is more effective at resolving ambiguities. The exhaustive experiments on synthetic scenes show that our method (1) outperforms state-of-the-art indoor inverse rendering and relighting methods particularly in the presence of complex illumination effects; (2) speeds up inverse path tracing optimization to less than an hour. We further demonstrate robustness to noisy inputs through material and lighting estimates that allow plausible relighting in a real scene. The source code is available at: https://github.com/lwwu2/fiptComment: Updated experiment results; modified real-world section

Neural Rendering and Its Hardware Acceleration: A Review

Neural rendering is a new image and video generation method based on deep learning. It combines the deep learning model with the physical knowledge of computer graphics, to obtain a controllable and realistic scene model, and realize the control of scene attributes such as lighting, camera parameters, posture and so on. On the one hand, neural rendering can not only make full use of the advantages of deep learning to accelerate the traditional forward rendering process, but also provide new solutions for specific tasks such as inverse rendering and 3D reconstruction. On the other hand, the design of innovative hardware structures that adapt to the neural rendering pipeline breaks through the parallel computing and power consumption bottleneck of existing graphics processors, which is expected to provide important support for future key areas such as virtual and augmented reality, film and television creation and digital entertainment, artificial intelligence and the metaverse. In this paper, we review the technical connotation, main challenges, and research progress of neural rendering. On this basis, we analyze the common requirements of neural rendering pipeline for hardware acceleration and the characteristics of the current hardware acceleration architecture, and then discuss the design challenges of neural rendering processor architecture. Finally, the future development trend of neural rendering processor architecture is prospected

Neural Radiance Fields: Past, Present, and Future

The various aspects like modeling and interpreting 3D environments and surroundings have enticed humans to progress their research in 3D Computer Vision, Computer Graphics, and Machine Learning. An attempt made by Mildenhall et al in their paper about NeRFs (Neural Radiance Fields) led to a boom in Computer Graphics, Robotics, Computer Vision, and the possible scope of High-Resolution Low Storage Augmented Reality and Virtual Reality-based 3D models have gained traction from res with more than 1000 preprints related to NeRFs published. This paper serves as a bridge for people starting to study these fields by building on the basics of Mathematics, Geometry, Computer Vision, and Computer Graphics to the difficulties encountered in Implicit Representations at the intersection of all these disciplines. This survey provides the history of rendering, Implicit Learning, and NeRFs, the progression of research on NeRFs, and the potential applications and implications of NeRFs in today's world. In doing so, this survey categorizes all the NeRF-related research in terms of the datasets used, objective functions, applications solved, and evaluation criteria for these applications.Comment: 413 pages, 9 figures, 277 citation

A Study Into Life Modelling for Elastomeric Tubes

The performance of a tube within a peristaltic pump is as a result of complex interactions between material, the process used to produce the tube, the pump it is in and the environment to which it is subjected. This research examines a possible methodology for the development of life modeling for elastomeric tubes used in peristaltic pumps. It is shown that predicting life within a peristaltic pump is a complex process but that the ability to predict the life cycle of the tube can be enhanced through material and process understanding and innovation. A systematic approach is detailed for the analysis of a tube life cycle from raw material through to failure. This tube life cycle could be used as the basis of a life modeling algorithm, a conceptual design for this is suggested. Using two materials highlighted as important to the peristaltic pump industry, detailed tube life analysis is carried out to show how the methodology can be implemented. The approach suggests key indicators that can be used to identify material characteristics which influence the life of a tube. This is shown for the two materials studied and how they differ from material to material. Extrusion methods for each of the materials is analysed in some detail and changes to, or controls for, the extrusion process to produce tubing are put forward. It is suggested that this will produce tubing which will perform more consistently within a peristaltic pump. This consistency of performance is put forward as a key facilitator for life modeling. Environmental factors which influence life are identified; with system pressure and temperature being the most influential on life. The way all the factors identified interact is discussed. From the identification of these factors appropriate sensor inputs are put forward which will enable them to be monitored and used within an algorithm

Physics-based vision meets deep learning

Physics-based vision explores computer vision and graphics problems by applying methods based upon physical models. On the other hand, deep learning is a learning-based technique, where a substantial number of observations are used to train an expressive yet unexplainable neural network model. In this thesis, we propose the concept of a model-based decoder, which is an unlearnable and differentiable neural layer being designed according to a physics-based model. Constructing neural networks with such model-based decoders afford the model strong learning capability as well as the potential to respect the underlying physics. We start the study by developing a toolbox of differentiable photometric layers ported from classical photometric techniques. This enables us to perform the image formation process given geometry, illumination and reflectance function. Applying these differentiable photometric layers into a bidirectional reflectance distribution function (BRDF) estimation network training, we show the network could be trained in a self-supervised manner without the knowledge of ground truth BRDFs. Next, in a more general setting, we attempt to solve inverse rendering problems in a self-supervised fashion by making use of model-based decoders. Here, an inverse rendering network decomposes a single image into normal and diffuse albedo map and illumination. In order to achieve self-supervised training, we draw inspiration from multiview stereo (MVS) and employ a Lambertian model and a cross-projection MVS model to generate model-based supervisory signals. Finally, we seek potential hybrids of a neural decoder and a model-based decoder on a pair of practical problems: image relighting, and fine-scale depth prediction and novel view synthesis. In contrast to using model-based decoders to only supervise the training, the model-based decoder in our hybrid model serves to disentangle the intricate problem into a set of physically connected solvable ones. In practice, we develop a hybrid model that can estimate a fine-scale depth map and generate novel view synthesis from a single image by using a physical subnet to combine results from an inverse rendering network with a monodepth prediction network. As for neural image relighting, we propose another hybrid model using a Lambertian renderer to generate initial estimates of relighting results followed by a neural renderer performing corrections over deficits in initial renderings. We demonstrate the model-based decoder can significantly improve the quality of results and relax the demands for labelled data

Hand Gesture and Activity Recognition in Assisted Living Through Wearable Sensing and Computing

With the growth of the elderly population, more seniors live alone as sole occupants of a private dwelling than any other population groups. Helping them to live a better life is very important and has great societal benefits. Assisted living systems can provide support to elderly people in their houses or apartments. Since automated recognition of human gestures and activities is indispensable for human-robot interaction (HRI) in assisted living systems, this dissertation focuses on developing a theoretical framework for human gesture, daily activity recognition and anomaly detection. First, we introduce two prototypes of wearable sensors for motion data collection used in this project. Second, gesture recognition algorithms are developed to recognize explicit human intention. Third, body activity recognition algorithms are presented with different sensor setups. Fourth, complex daily activities, which consist of body activities and hand gestures simultaneously, are recognized using a dynamic Bayesian network (DBN). Fifth, a coherent anomaly detection framework is built to detect four types of abnormal behaviors in human's daily life. Our work can be extended in several directions in the future.School of Electrical & Computer Engineerin

Computational Approaches to Explainable Artificial Intelligence:Advances in Theory, Applications and Trends

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9 International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications

Computational approaches to explainable artificial intelligence: Advances in theory, applications and trends

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9th International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications.MCIU - Nvidia(UMA18-FEDERJA-084