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Recent Submissions
Accelerating Geometric Queries for Computer Graphics: Algorithms, Techniques, and Applications
(0000-08-16) Evangelou Iordanis
In the ever-evolving context of Computer Graphics, the demand for realistic and real-time virtual
environments and interaction with digitised or born-digital content has exponentially grown.
Whether in gaming, production rendering, computer-aided design, reverse engineering, geometry
processing, and understanding or simulation tasks, the ability to rapidly and accurately perform
geometric queries of any type is crucial. The actual form of a geometric query varies depending
on the task at hand, application domain, input representation, and adopted methodology. These
queries may involve intersection tests as in the case of ray tracing, spatial queries, such as
needed for recovering nearest sample neighbours, geometry registration in order to classify
polygonal primitive inputs, or even virtual scene understanding in order to suggest and embed
configurations as in the case of light optimisation and placement. As the applications of these
algorithms and, consequently, their complexity continuously grow, traditional geometric queries
fall short, when naïvely adopted and integrated in practical scenarios. Therefore, these methods
face limitations in terms of computational efficiency and query bandwidth. This is particularly
pronounced in scenarios, where vast amounts of geometric data must be processed in interactive
or even real-time rates. More often than not, one has to inspect and understand the internal
mechanics and theory of the algorithms invoking these geometric queries. This is particularly
useful in order to devise appropriately tailored procedures to the underline task, hence maximise
their efficiency, both in terms of performance and output quality. As a result, there is an enormous
area of research that explores innovative approaches to geometric query acceleration, addressing
the challenges posed.
The primary focus of this research was to develop innovative methods for accelerating
geometric queries within the domain of Computer Graphics. This entails a comprehensive
exploration of algorithmic optimisations that include the development of advanced data structures
and neural network architectures, tailored to efficiently handle geometric collections. This
research addressed not only the computational complexity of individual queries, but also the
adaptability of the proposed solutions to diverse applications and scenarios primary within the
realm of Computer Graphics but also intersecting domains. The outcome of this research holds
the potential to influence the fields that adopt these geometric query methodologies by addressing
the associated computational challenges and unlocking novel directions for real-time rendering,
interactive simulation, and immersive virtual experiences.
More specifically, the contributions of this thesis are divided into two broad directions for
accelerating geometric queries: a) global illumination-related, hardware-accelerated nearestneighbour
queries and b) application of deep learning to the definition of novel data structures
and geometric query methods.
In the first part, we consider the task of real-time global illumination using photon density
estimators. In particular we investigate scenarios where complex illumination effects, such
as caustics, that can mainly be handled from the illumination theory regarding progressive
photon mapping algorithms, require vast amount of rays to be traced from both the eye sensor
and the light sources. Photons emanating from lights are cached into the surface geometry
or volumetric media and must be gathered at query locations on the paths traced from the
camera sensor. To achieve real-time frame rates, gathering, an expensive operation, needs to
be efficiently handled. This is accomplished by adapting screen space ray tracing and splatting
to the hardware-accelerated rasterisation pipeline. Since the gathering phase is an inherent
subcategory of nearest neighbours search, we also propose how to efficiently generalise this
concept to any form of task by exploiting existing low-level hardware accelerated ray tracing
frameworks. Effectively boosting the inference phase by orders of magnitude compared to the
traditional strategies involved.
In the second part, we shift our focus to a more generic class of geometric queries. The
first work involves accurate and fast shape classification using neural networks architectures.
We demonstrate that a hybrid architecture, which processes orientation and a voxel-based
representation of the input, is capable of processing hard-to-distinguish solid geometry from
the context of building information models. Second, we consider the form of geometric queries
in the context of scene understanding. More precisely, optimising the placement and light
intensities of luminaries in urban places can be a computationally intricate task especially for
large inputs and conflicting constraints. Methodologies employed in the literature usually make
assumptions about the input representation to mitigate the intractable nature of this task. In this
thesis, we approach this problem with a holistic solution that can produce feasible and diverse
proposals in real time by adopting a neural-based generative modelling methodology. Finally,
we propose a novel and general approach to solve recursive cost evaluators for the construction
of geometric query acceleration data structures. This work establishes a new research direction
for the construction of data structures guided by recursive cost functions using neural-based
architectures. Our goal is to overcome the exhaustive but intractable evaluation of the cost
function, in order to generate a high-quality data structure for spatial queries.
Towards Computationally Efficient, Photorealistic, and Scalable 3D Generative Modelling
(University College London, 2024-08-01) Animesh Karnewar
GM (Generative Modelling) is a class of self supervised Machine Learning which finds applications in synthetic data generation, semantic representation learning, and various creative and artistic fields. GM (aka. Generative AI) seemingly holds the potential for the next breakthrough in AI; of which, the recent successes in LLMs, text-to-image synthesis and text to-video synthesis serve as formidable testament. The way these generative models have revolutionized the process of 2D content creation, we can expect that 3D generative modelling will also contribute significantly towards simplifying the process of 3D content creation. However, it is non-trivial to extend the 2D generative algorithms to operate on 3D data managing various factors such as the inherent data-sparsity, the growing memory requirements, and the computational complexity. The application of Generative Modelling to 3D data is made even harder due to the pertaining challenges: firstly, finding a large quantity of 3D training data is much more complex than 2D images; and secondly, there is no de-facto representation for 3D assets, where various different representations such as point-clouds, meshes, voxel grids, neural (MLP)s, etc. are used depending on the application. Thus, with the goal of ultimately enabling 3D Generative Models, and considering the aforementioned challenges, I propose this thesis which makes substantial strides “Towards Computationally Efficient, Photorealistic, and Scalable 3D Generative Modelling”.
Rethinking multiple importance sampling for general and efficient Monte Carlo rendering
(Saarländische Universitäts- und Landesbibliothek, 2024-03-14) Grittmann, Pascal
Computer generated images are essential for many applications from art to engineering. Unfortunately, rendering such images is costly, with render times easily in the hours, days, or even weeks. On top of that, the demands regarding complexity and visual fidelity are ever rising. Consequently, there is an insatiable need for faster rendering. Efficient render times are often achieved through user intervention. For example, modifying the scene and removing difficult lighting effects can keep render times below an acceptable threshold. Also, algorithm parameters can be tuned manually. For instance, diffuse outdoor scenes are best rendered by unidirectional path tracing, while interiors featuring caustics benefit greatly from bidirectional sampling. Such manual tuning, however, is unfortunate as it puts much burden on the user and poses a hurdle for novices. In this thesis, we pave the way for more universal rendering algorithms with less need of user intervention. For that, we revisit multiple importance sampling (MIS), an essential tool to universalize rendering algorithms by combining diverse sampling techniques. We identify hitherto unknown shortcomings of MIS and propose practical solutions and improvements. As a tangible result, we achieve adaptive bidirectional rendering with performance never worse than unidirectional path tracing.
Learning Structured Representations of 3D CAD Models
(Simon Fraser University, 2024-05-21) Fenggen Yu
Computer-Aided-Design (CAD) models have become widespread in engineering and manufacturing, driving decision-making and product evolution related to 3D models. Understanding the structure of 3D CAD models is crucial for various applications, as it can significantly benefit 3D shape analysis, modeling, and manipulation. With the rapid advancements in AI-powered solutions across all relevant fields, several CAD datasets have emerged to support research in 3D geometric deep learning. However, learning the structure of 3D CAD models presents a challenging task. The primary reason is the significant structure variations among small, intricate parts and the limited availability of labeled datasets to support structure learning of 3D CAD models.
This thesis proposes several methods to learn structured representations for 3D CAD models to address these challenges. Firstly, we introduce CAPRI-Net, a self-supervised neural network that learns compact 3D CAD models with adaptive primitive assembly. CAPRI-Net can be trained without ground-truth primitive assembly, and it can reconstruct an input shape by assembling quadric surface primitives via Constructive Solid Geometry (CSG) operations. In our subsequent work, D2CSG, we modify the architecture of CAPRI-Net by assembling the primitives in two dual and complementary network branches, with network weights dropout strategy, to reconstruct 3D CAD models with dedicated details and high genus. Compared to CAPRI-Net, D2CSG is provably general and can produce more compact CSG trees. We further introduce DPA-Net, inspired by the volume rendering algorithm in Neural Radiance Fields (NeRF). DPA-Net uses primitive assembly and differentiable rendering to reconstruct 3D CAD models with textures from sparse views. Finally, we introduce HAL3D, the first active learning tool for fine-grained 3D part labeling. HAL3D can take the output of previous methods as input and assign fine-grained semantic labels to part sets of 3D CAD models along a pre-defined hierarchy tree. We develop two novel features to reduce human efforts: hierarchical and symmetry-aware active labeling. Our human-in-the-loop approach achieves close to error-free fine-grained annotations on any test set with pre-defined hierarchical part labels, with 80% time-saving over manual effort.
Conversational Agent for Procedural Building Design in Virtual Reality
(The Eurographics Association, 2024) Bosco, Matteo; Kán, Peter; Kaufmann, Hannes; Hasegawa, Shoichi; Sakata, Nobuchika; Sundstedt, Veronica
With the emergence of large language models (LLMs), conversational agents have gained significant attention across various domains, including virtual reality (VR). This paper investigates the use of conversational agents as an interface for procedural building design in VR. We propose a voice interface that allows a user to control parameters of procedural generation and gain insights about the building construction metrics through natural conversation. The pipeline introduced for the conversational agent involves utilizing LLMs in two separate API calls for natural language understanding and natural language generation. This separation enables the invocation of various actions in procedural generation as well as meaningful agent responses to building-related questions. Furthermore, we conducted a user study to assess our proposed conversational interface in comparison to a traditional graphical user interface (GUI) in a VR architectural design task focused on circular economy. The study scrutinize the user-reported usability, presence, realism, errors, and effectiveness of both interfaces. Results suggest that while the non-embodied conversational agent enhances effectiveness due to its explanatory capabilities, it surprisingly decreases realism compared to the GUI. Overall, the preference between the conversational agent and the GUI varied greatly among participants, highlighting the need for further research into the evolving shift towards speech interaction in VR.