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UVG-CWI-DQPC: Dual-quality point cloud dataset for volumetric video applications
No full textVolumetric video is a key enabler of immersive extended reality (XR) experiences and is often represented using point clouds for their structural simplicity. However, capturing volumetric content through multi-view acquisition and depth sensing poses many challenges, such as occlusions and depth mismatches. To foster research in this field, we introduce a unique dual-quality point cloud dataset, named UVG-CWI-DQPC, which is designed to support the development of point cloud enhancement, compression, and quality assessment. Our dataset includes 12 dynamic sequences captured simultaneously by: 1) a high-end capture system producing high-fidelity point clouds with extensive processing; and 2) a consumer-grade capture system relying on affordable RGB-D cameras, lightweight processing, and open-source tools. For each sequence, our dataset provides ground-truth point clouds from the high-end capture system and raw RGB-D footage from the consumer-grade capture system, along with calibration data and tools for point cloud generation. This dual-quality setup enables direct comparison and benchmarking of algorithms for densification, occlusion removal, registration, and quality enhancement. Our dataset is publicly available under a permissive license to support reproducible research and standardization work in Moving Picture Experts Group (MPEG) and 3rd Generation Partnership Project (3GPP)
Automated data curation for self-supervised learning in underwater acoustic analysis
Get PDFThe sustainability of the ocean ecosystem is threatened by increased levels of sound pollution, making monitoring crucial to understand its variability and impact. Passive acoustic monitoring (PAM) systems collect a large amount of underwater sound recordings, but the large volume of data makes manual analysis impossible, creating the need for automation. Although machine learning offers a potential solution, most underwater acoustic recordings are unlabeled. Self-supervised learning models have demonstrated success in learning from large-scale unlabeled data in various domains like computer vision, Natural Language Processing, and audio. However, these models require large, diverse, and balanced datasets for training in order to generalize well. To address this, a fully automated self-supervised data curation pipeline is proposed to create a diverse and balanced dataset from raw PAM data. It integrates Automatic Identification System (AIS) data with recordings from various hydrophones in the U.S. waters. Using hierarchical k-means clustering, the raw audio data is sampled and then combined with AIS samples to create a balanced and diverse dataset. The resulting curated dataset enables the development of selfsupervised learning models, facilitating various tasks such as monitoring marine mammals and assessing sound pollution
Dynamic angle selection in X-Ray CT: A reinforcement learning approach to optimal stopping
Get PDFn industrial X-ray Computed Tomography (CT), the need for rapid in-line inspection is critical. Sparse-angle tomography plays a significant role in this by reducing the required number of projections, thereby accelerating processing and conserving resources. Most existing methods aim to balance reconstruction quality and scanning time, typically relying on fixed scan durations. Adaptive adjustment of the number of angles is essential; for instance, more angles may be required for objects with complex geometries or noisier projections. The concept of optimal stopping, which dynamically adjusts this balance according to varying industrial needs, remains overlooked. Building on our previous work, we integrate optimal stopping into sequential Optimal Experimental Design (sOED) and Reinforcement Learning (RL). We propose a novel method for computing the policy gradient within the Actor–Critic framework, enabling the development of adaptive policies for informative angle selection and scan termination. Additionally, we evaluate whether policies trained in simulation transfer to experimental X-ray CT data and provide initial evidence on laboratory data. Trained on synthetic data, the model shows consistent behavior on experimental scans. This supports flexible CT operation and expands the applicability of sparse-view tomography in industrial settings
Discreteness of asymptotic tensor ranks
Get PDFTensor parameters that are amortized or regularized over large tensor powers, often called "asymptotic" tensor parameters, play a central role in several areas including algebraic complexity theory (constructing fast matrix multiplication algorithms), quantum information (entanglement cost and distillable entanglement), and additive combinatorics (bounds on cap sets, sunflower-free sets, etc.). Examples are the asymptotic tensor rank, asymptotic slice rank and asymptotic subrank. Recent works (Costa–Dalai, Blatter–Draisma–Rupniewski, Christandl–Gesmundo–Zuiddam) have investigated notions of discreteness (no accumulation points) or "gaps" in the values of such tensor parameters.
We prove a general discreteness theorem for asymptotic tensor parameters of order-three tensors and use this to prove that (1) over any finite field (and in fact any finite set of coefficients in any field), the asymptotic subrank and the asymptotic slice rank have no accumulation points, and (2) over the complex numbers, the asymptotic slice rank has no accumulation points.
Central to our approach are two new general lower bounds on the asymptotic subrank of tensors, which measures how much a tensor can be diagonalized. The first lower bound says that the asymptotic subrank of any concise three-tensor is at least the cube root of the smallest dimension. The second lower bound says that any concise three-tensor that is "narrow enough" (has one dimension much smaller than the other two) has maximal asymptotic subrank
Controlling the low-temperature Ising model using spatiotemporal Markov decision theory
Get PDFWe introduce the spatiotemporal Markov decision process (STMDP), a special type of Markov decision process that models sequential decision-making problems which are not only characterized by temporal, but also by spatial interaction structures. To illustrate the framework, we construct an STMDP inspired by the low-temperature two-dimensional Ising model on a finite, square lattice, evolving according to the Metropolis dynamics. We consider the situation in which an external decision maker aims to drive the system towards the all-plus configuration by flipping spins at specified moments in time. In order to analyze this problem, we construct an auxiliary MDP by means of a reduction of the configuration space to the local minima of the Hamiltonian. Leveraging the convenient form of this auxiliary MDP, we uncover the structure of the optimal policy by solving the Bellman equations in a recursive manner. Finally, we conduct a numerical study on the performance of the optimal policy obtained from the auxiliary MDP in the original Ising STMDP