Progressive compression of triangle mesh geometry typically exploits spatial coherence to reduce data size while preserving surface detail. In applications where lossy compression is permissible, an effective strategy is to align distortion with the limitations of human visual perception. Existing methods rely on perceptual metrics to steer refinement, but doing so typically incurs additional data overhead to specify where each refinement occurs. We propose a progressive geometry compression algorithm that leverages a perceptually informed model of normal uncertainty to predict where distortion is most likely to be noticeable. This enables the encoder to focus refinements in those regions without explicitly transmitting their locations at each step, thereby reducing overhead.While our initial approach produced reconstructions ranked higher by established perceptual metrics compared to a baseline of Edgebreaker with weighted parallelogram prediction, its high computational cost limited practical deployment. In this extended work, we address this bottleneck by introducing an Adaptive Quasi-Monte Carlo sampling method utilizing a Halton sequence and an early-stopping heuristic. This strategy substantially accelerates the compression pipeline and improves the overall perceptual quality. Furthermore, we explore a neural estimator with the potential to further speed up the normal uncertainty evaluation process. However, while this approach shows potential, further research is required to achieve sufficient accuracy and reliability for practical integration.

Introduction Upper limb (UL) impairment is common in people with multiple sclerosis (pwMS), and functional recovery of the UL is a key rehabilitation goal. Technology-based approaches, like virtual reality (VR), are increasingly promising. While most VR environments are task-oriented, our clinical approach integrates neuroproprioceptive ‘facilitation and inhibition’ (NFI) principles. To advance this, we developed immersive VR software based on NFI principles targeting UL function and sit-to-stand ability. This study aims to evaluate the effectiveness of this VR therapy compared with conventional NFI-based physical therapy in pwMS. Our study uniquely applies advanced imaging techniques, along with biological molecular assessments, to explore adaptive processes induced by VR rehabilitation.Methods and analysis This double-arm, randomised, assessor-blinded, controlled trial runs over 2 months (1 hour, 2 times per week). PwMS with mild to severe disability will receive either VR therapy or real-world physical therapy. Primary outcomes include the nine-hole peg test, box and block test, handgrip strength, tremor and five times sit-to-stand test. Secondary measures include the Multiple Sclerosis Impact Scale, the 5-level EQ-5D questionnaire and kinematic analysis. Adaptive processes will be monitored using imaging techniques (functional MRI and tractography), molecular genetic methods (long non-coding RNAs) and immune system markers (leukocytes, dendritic cells). The International Classification of Functioning, Disability and Health brief set for MS will map the bio-psycho-social context of participants.Ethics and dissemination This project and its amendments were approved by the Ethics Committee of the Institute for Clinical and Experimental Medicine and Thomayer Hospital (1983/21+4772/21 (G-21–02) and the Ethics Committee of Kralovske Vinohrady University Hospital (EK-VP/38/0/2021) in Prague, Czechia (with single enrolment). The findings of this project will be disseminated through scientific publications, conferences, professional networks, public engagement, educational materials and stakeholder briefings to ensure a broad impact across clinical, academic and public domains.Trial registration number clinicaltrials.gov (NCT04807738).

Time-varying meshes (TVMs), characterized by their varying connectivity and number of vertices, hold significant potential in AR/VR applications. However, their practical use is challenging due to their large file sizes and the complexity of time-varying topology. Many time-varying mesh compression methods attempted to exploit redundancy between consecutive meshes to compress TVMs more efficiently, however, most face difficulties in establishing stable vertex and surface correspondence between the frames of a TVM. We propose TVMC, a novel TVM compression method that leverages volume tracking and extracts high-quality reference meshes for inter-frame prediction. Specifically, we use as-rigid-as-possible volume tracking to align consecutive TVMs and track volume centers, followed by multidimensional scaling to refine reference centers. This allows us to precisely deform a group of frames to the reference space and extract the reference mesh which is then deformed to approximate each mesh in the group to get displacement fields for TVM compression. Extensive experiments show that TVMC outperforms state-of-the-art methods (e.g., Google Draco, V-DMC 4.0, etc.), with bitrates of 4-6 Mbps compared to 9-12 Mbps for Draco and 10-15 Mbps for V-DMC 4.0. It reduces the decoding time by 66.1% compared to Draco and enables an increased group of frames (up to 15) without significant distortion.

The paper introduces a new method to describe the surfaces of voxelised solids. It operates in three stages: a hierarchical linked list of chain code sequences is created first; the linked lists are pruned; and, finally, the content of the data structure is stored. The method uses chain codes from either a three- or nine-symbols alphabet. In the first case, two chain code symbols are needed to access the next face, while, in the second case, this is done by one symbol. The pair of chain codes from the three-symbols alphabet, or the individual symbol from the nine-symbols alphabet are considered as tokens. The sets of tokens are, in both cases, extended by two tokens, indicating the beginning and ending of the list. The method processes solids of any shape, including those containing holes, cavities, or multiple components existing in the same voxel space. Edge-connectivity is permitted. The method was compared against the method proposed by Lemus et al., which is designed for solids without holes. Although supporting a broader set of voxelised solids, the proposed method generates sequences of tokens that are, on average, up to 10% shorter. Since the information entropy of the sequences of tokens produced by the proposed method is also smaller, the obtained sequences are more compressible, as confirmed by applying gzip and bzip2 data compressors.

Time-varying meshes (TVMs), that is mesh sequences with varying connectivity, are a greatly versatile representation of shapes evolving in time, as they allow a surface topology to change or details to appear or disappear at any time during the sequence. This, however, comes at the cost of large storage size. Since 2003, there have been attempts to compress such data efficiently. While the problem may seem trivial at first sight, considering the strong temporal coherence of shapes represented by the individual frames, it turns out that the varying connectivity and the absence of implicit correspondence information that stems from it makes it rather difficult to exploit the redundancies present in the data. Therefore, efficient and general TVM compression is still considered an open problem. We describe and categorize existing approaches while pointing out the current challenges in the field and hint at some related techniques that might be helpful in addressing them. We also provide an overview of the reported performance of the discussed methods and a list of datasets that are publicly available for experiments. Finally, we also discuss potential future trends in the field.

We present a comprehensive software system developed for an innovative virtual reality application that leverages the principles of proprioceptive ‘facilitation and inhibition’ to enhance upper limb function, along with the results of a prospective, feasibility and proof-of-concept study conducted in people with multiple sclerosis. The system tracks upper limb movement at two locations, as well as the movement of the chest and head. Each tracked point continuously provides real-time spatial position and orientation data to the system. Participants engage in physical therapy based on the principles of proprioceptive ‘facilitation and inhibition’, interspersed with selected relaxation games, while their performance — relative to predefined exercise templates — is continuously monitored. All session data is also stored for retrospective analysis. The system delivers real-time performance evaluation to the therapist, along with live feedback and stereoscopic 3D guidance for the participant, thereby enhancing engagement in movement. Preliminary measurements indicate promising results, particularly in terms of improved gross motor function compared to the same physiotherapy without the virtual reality component.

Point clouds have become a popular training data for many practical applications of machine learning in the fields of environmental modeling and precision agriculture. In order to reduce high space requirements and the effect of noise in the data, point clouds are often transformed to a structured representation such as a voxel grid. Storing, transmitting and consuming voxelized geometry, however, remains a challenging problem for machine learning pipelines running on devices with limited amount of on-chip memory with low access latency. A viable solution is to store the data in a compact encoded format, and perform on-the-fly decoding when it is needed for processing. Such on-demand expansion must be fast in order to avoid introducing substantial additional delay to the pipeline. This can be achieved by parallel decoding, which is particularly suitable for massively parallel architecture of GPUs on which the majority of machine learning is currently executed. In this paper, we present such method for efficient and parallelizable encoding/decoding of voxelized geometry. The method employs multi-level context-aware prediction of voxel occupancy based on the extracted binary feature prediction table, and encodes the residual grid with a pointerless sparse voxel octree (PSVO). We particularly focused on encoding the datasets of voxelized trees, obtained from both synthetic tree models and LiDAR point clouds of real trees. The method achieved 15.6% and 12.8% reduction of storage size with respect to plain PSVO on synthetic and real dataset, respectively. We also tested the method on a general set of diverse voxelized objects, where an average 11% improvement of storage space was achieved.

Time-varying connectivity of triangle mesh sequences leads to substantial difficulties in their processing. Unlike editing sequences with constant connectivity, editing sequences with varying connectivity requires addressing the problem of temporal correspondence between the frames of the sequence. We present a method for time-consistent editing of triangle mesh sequences with varying connectivity using sparse temporal correspondence, which can be obtained using existing methods. Our method includes a deformation model based on the usage of the sparse temporal correspondence, which is suitable for the temporal propagation of user-specified deformations of the edited surface with respect to the shape and true topology of the surface while preserving the individual connectivity of each frame. Since there is no other method capable of comparable types of editing on time-varying meshes, we compare our method and the proposed deformation model with a baseline approach and demonstrate the benefits of our framework.

We propose a single-rate method for geometry compression of triangle meshes based on using a neural predictor to predict the encoded vertex positions using connectivity and an already known part of the geometry. The method is based on standard traversal-based methods but uses a neural predictor for prediction instead of a hand-crafted prediction scheme. The parameters of the neural predictor are learned on a dataset of existing triangle meshes. The method additionally includes an estimate of the prediction uncertainty, which is used to guide the encoding traversal of the mesh. The results of the proposed method are compared with a benchmark method on the ABC dataset using both mechanistic and perceptual metrics.

Time-Varying meshes (TVMs), characterized by their varying connectivity and number of vertices, hold significant potential in immersive media and other various applications. However, their practical utilization is challenging due to their time-varying features and large file sizes. Creating a reference mesh that contains the most essential information is a promising approach to utilizing shared information within TVMs to reduce storage and transmission costs. We propose a novel method that employs volume tracking to extract reference meshes. First, we adopt as-rigid-as-possible (ARAP) volume tracking on TVMs to get the volume centers for each mesh. Then, we use multidimensional scaling (MDS) to get reference centers that ensure the reference mesh avoids self-contact regions. Finally, we map the vertices of the meshes to reference centers and extract the reference mesh. Our approach offers a feasible solution for extracting reference meshes that can serve multiple purposes such as establishing surface correspondence, deforming the reference mesh to different shapes for I-frame based mesh compression, or defining the global shape of the TVMs.

In certain practical situations, the connectivity of a triangle mesh needs to be transmitted or stored given a fixed set of 3D vertices that is known at both ends of the transaction (encoder/decoder). This task is different from a typical mesh compression scenario, in which the connectivity and geometry (vertex positions) are encoded either simultaneously or in reversed order (connectivity first), usually exploiting the freedom in vertex/triangle re‐indexation. Previously proposed algorithms for encoding the connectivity for a known geometry were based on a canonical mesh traversal and predicting which vertex is to be connected to the part of the mesh that is already processed. In this paper, we take this scheme a step further by replacing the fixed traversal with a priority queue of open expansion gates, out of which in each step a gate is selected that has the most certain prediction, that is one in which there is a candidate vertex that exhibits the largest advantage in comparison with other possible candidates, according to a carefully designed quality metric. Numerical experiments demonstrate that this improvement leads to a substantial reduction in the required data rate in comparison with the state of the art.

The main challenges of collision detection and handling in muscle modelling are demonstrated.Then, a collision handling technique is tested, exploiting the issue of muscle penetrating the bone in some circumstances, mainly when the movement is too rapid or the displacement of the bone is too high.Our approach also detects the problem, using Discregrid to see the immediate direction change towards the penetrated bone.Some alternatives to the described PBD (Position-Based dynamics) technique are presented: PBD with As-Rigid-As-Possible modification and radial basis function approach.

Processing of deforming shapes represented by sequences of triangle meshes with connectivity varying in time is difficult, because of the lack of temporal correspondence information, which makes it hard to exploit the temporal coherence. Establishing surface correspondence is not an easy task either, especially since some surface patches may have no corresponding counterpart in some frames, due to self-contact. Previously, it has been shown that establishing sparse correspondence via tracking volume elements might be feasible, however, previous methods suffer from severe drawbacks, which lead to tracking artifacts that compromise the applicability of the results. In this paper, we propose a new, temporally global optimisation step, which allows to improve the intermediate results obtained via forward tracking. Together with an improved formulation of volume element affinity and a robust means of identifying and removing tracking irregularities, the procedure yields a substantially better model of temporal volume correspondence.

The new generation 3D scanner devices have revolutionized the way information from 3D objects is acquired, making the process of scene capturing and digitization straightforward. However, the effectiveness and robustness of conventional algorithms for real scene analysis are usually deteriorated due to challenging conditions, such as noise, low resolution, and bad perceptual quality. In this work, we present a methodology for identifying and registering partially-scanned and noisy 3D objects, lying in arbitrary positions in a 3D scene, with corresponding high-quality models. The methodology is assessed on point cloud scenes with multiple objects with large missing parts. The proposed approach does not require connectivity information and is thus generic and computationally efficient, thereby facilitating computationally demanding applications, like augmented reality. The main contributions of this work are the introduction of a layered joint registration and indexing scheme of cluttered partial point clouds using a novel multi-scale saliency extraction technique to identify distinctive regions, and an enhanced similarity criterion for object-to-model matching. The processing time of the process is also accelerated through 3D scene segmentation. Comparisons of the proposed methodology with other state-of-the-art approaches highlight its superiority under challenging conditions.