Human-Centric Quality Assessment and Visual Attention Modeling for Point Clouds addresses a central challenge in immersive three-dimensional media: how to measure, understand, and predict human perceptual experience when interacting with high-fidelity point-cloud content in virtual and mixed-reality environments. Point clouds are increasingly adopted in extended reality (XR), telepresence, free-viewpoint video, and autonomous systems due to their flexibility in representing complex 3D scenes. However, their irregular structure, large data volume, and the coupled effects of geometric and texture distortions make perceptual quality assessment particularly challenging. Widely used image and video quality assessment metrics are known to correlate poorly with human Mean Opinion Scores (MOS) when directly applied to point clouds, highlighting the need for perceptually grounded quality models tailored to this media type.

This dissertation develops methodologies, datasets, and objective metrics that bridge signal processing techniques with human-centred evaluation in order to improve perceptual alignment in Point Cloud Quality Assessment (PCQA). Beyond metric design, the thesis also provides a human-centred experimental framework and prototype system to support subjective quality assessment and visual saliency analysis in immersive environments, enabling controlled yet valid perceptual studies.

The thesis is organized into three complementary parts. The first part focuses on objective PCQA. Novel quality metrics are proposed to capture the combined influence of geometry and texture on perceived quality. A full-reference metric, PointPCA+, and a no-reference model, M3-Unity, are introduced. These approaches employ modalityaware feature representations that explicitly account for the characteristics of point-cloud geometry and color attributes, together with carefully designed similarity measures or learning-based regression models. Extensive evaluations on public benchmark datasets demonstrate that the proposed metrics achieve improved correlation with human MOS compared to state-of-the-art methods, while also exhibiting robustness across different compression schemes and distortion types. These results confirm the importance of modality-specific modeling and perceptually motivated feature design for PCQA.

The second part of the thesis investigates human visual attention for dynamic pointcloud content in immersive environments. Eye-tracking experiments were conducted in six-degrees-of-freedom virtual reality settings to capture gaze behavior during the viewing of dynamic point clouds. Two datasets were constructed: a task-dependent dataset (QAVA-DPC), in which participants performed explicit visual tasks, and a task-free dataset (TF-DPC), designed to capture natural viewing behavior. The experimental design incorporates systematic preprocessing, stimulus normalization, and error profiling of headmounted display eye trackers to ensure data reliability and reproducibility. These datasets enable a detailed analysis of how task demands, motion, and temporal dynamics influence visual saliency and viewing behavior in immersive 3D scenes.

The third part explores the integration of visual saliency into objective quality assessment. By incorporating both ground-truth and predicted saliency maps into PCQA pipelines, the thesis examines how attention-guided feature weighting and perceptual pooling strategies affect quality prediction performance. Experimental results show that saliency-aware approaches can improve predictive accuracy, although the magnitude of improvement depends on the underlying quality metric and pooling strategy. These findings highlight that visual attention is a valuable perceptual cue, but must be integrated in a principled and task-aware manner. Overall, the results demonstrate that attention-aware models can better prioritize perceptually relevant distortions, which is particularly beneficial for applications such as point-cloud compression, adaptive streaming, and immersive media delivery.

In addition to methodological advances, this dissertation contributes a range of resources that support reproducible research and open science for the research community. The gaze-annotated datasets for dynamic point clouds are made available to support reproducible research and facilitate further investigation of visual attention in immersive media. The thesis also documents detailed experimental protocols inspired by ITU recommendations and adapted to extended-reality settings, contributing to ongoing efforts toward standardization and open science in volumetric media research.

Remote communication applications have become a necessity in a globalised and connectedworld exemplified by the popularity of video conferencing applications. In recent years, virtual reality (VR) remote communication applications have emerged that aim to deliver a greater sense of co-presence and immersion in a shared virtual space where users are able to navigate freely while employing both verbal and non-verbal communication. Such applications require a volumetric user representation and point clouds have emerged as a popular format to represent real-time user reconstructions. However, volumetric point clouds are challenging to deliver over bandwidth-limited networks owing to the large volume of data required for dynamic streams. Adaptive streaming is the process of segmenting an object spatially and temporally in order to optimize the delivery of content by prioritizing the quality of spatial segments that are visible from a given viewport. In this thesis, we embed our research in the application scenario of VR remote communication with real-time point cloud user reconstructions and explore adaptive delivery optimizations. Previous work in the field mainly focused on using the entire point cloud object as the unit of bandwidth allocation in scenes containing multiple point clouds. Other recent work has relied on computationally complex surface estimation in order to spatially segment the point cloud that is unsuited to real-time applications. This thesis focuses on investigating if a user-centred approach combined with a low complexity adaptive streaming method can improve the quality of experience of interacting with point cloud user reconstructions in VR remote communication. We focus on optimizing the delivery of a remote user’s reconstruction with spatial segmentation and surface orientation estimation in real-time combinedwith an auxiliary utility function to allocate the available bandwidth across available segments. The utility is defined based on the position and surface orientation of the point cloud segment and the position and orientation of the user’s viewport.

The need to capture environments and objects in 3 dimensions to produce a high quality digital representation is proving to be useful in many applications in the world, where there is an increasing dependence on digital spaces. Point Clouds are a data type to represent 3D objects and scenes. During the processing of the point cloud data, undesired changes to the point cloud can be introduced or information can get lost. Automatic detection and classifying of the quality of a point cloud is needed, to regulate this data type for use in 3D environments, and speed up the processing of point cloud objects in real-time applications.

In this thesis, we look into the problem of no-reference point cloud quality assessment. Full-reference quality metrics compare a distorted point cloud with its original, while no-reference or blind metrics only look at a single point cloud. This task is more challenging than full-reference, as there is less direct information available to work with. We approach the challenge of how the perceptual quality of a point cloud can be estimated when a high-quality reference point cloud is not available, by analyzing if any point cloud properties correlate with the perceptual quality.

We try to find point cloud properties that are effective to use in a model to predict a user given quality score. We select multiple local properties to transform into global descriptors, by taking multiple statistical attributes of the data on all the points to combine into scalars. We analyze the created global descriptors to find linear correlations with the quality score, and found many features that to some measure linearly correlate with some distortion types. With the selected global descriptors, we use Support Vector Regression to find the fitting of the features to the quality score. Varying performances are achieved by training the models on different data subsets, and different fitting kernel functions.

The results show some distortions on unknown objects are properly predictable using fitted weights on this distortion. However, a single defining quality descriptor is not yet found. While the results show potential, our model is not robust and fast enough to perform a reliable assessment of quality in a real-time environment. We are just at the beginning of exploring no-reference quality assessment, and while the current methods are not yet appliable in real-time scenarios, future work can fill many knowledge gaps to reach this goal.