Related papers: A Toolkit for Virtual Reality Data Collection
Machine-learning methods rely on sufficiently large dataset to learn data distributions. They are widely used in research in X-Ray Computed Tomography, from low-dose scan denoising to optimisation of the reconstruction process. The lack of…
Datasets have gained an enormous amount of popularity in the computer vision community, from training and evaluation of Deep Learning-based methods to benchmarking Simultaneous Localization and Mapping (SLAM). Without a doubt, synthetic…
The recent rise of interest in Virtual Reality (VR) came with the availability of commodity commercial VR prod- ucts, such as the Head Mounted Displays (HMD) created by Oculus and other vendors. To accelerate the user adoption of VR…
It is commonly acknowledged that the availability of the huge amount of (training) data is one of the most important factors for many recent advances in Artificial Intelligence (AI). However, datasets are often designed for specific tasks…
Scientists across all disciplines increasingly rely on machine learning algorithms to analyse and sort datasets of ever increasing volume and complexity. Although trends and outliers are easily extracted, careful and close inspection will…
Depth estimation is a fundamental component of spatial perception for autonomous driving and other unmanned systems operating in open urban environments. Existing depth datasets such as KITTI, nuScenes, and DDAD have advanced the field but…
In recent years, advances in immersive multimedia technologies, such as extended reality (XR) technologies, have led to more realistic and user-friendly devices. However, these devices are often bulky and uncomfortable, still requiring…
Datasets of visualization play a crucial role in automating data-driven visualization pipelines, serving as the foundation for supervised model training and algorithm benchmarking. In this paper, we survey the literature on visualization…
We present the first fine-grained dataset of 1,497 3D VR sketch and 3D shape pairs of a chair category with large shapes diversity. Our dataset supports the recent trend in the sketch community on fine-grained data analysis, and extends it…
There is a growing need for authentication methodology in virtual reality applications. Current systems assume that the immersive experience technology is a collection of peripheral devices connected to a personal computer or mobile device.…
Learning from demonstrations is effective for robotic manipulation, but collecting sufficient task-specific data remains a major bottleneck. Under distribution shift, small errors compound, performance degrades, and expert time is often…
Dataset distillation methods reduce large-scale datasets to smaller sets of synthetic data, preserving sufficient information to quickly train a new model from scratch. However, prior work on dataset distillation has focused exclusively on…
Recent years have witnessed a rapid development of immersive multimedia which bridges the gap between the real world and virtual space. Volumetric videos, as an emerging representative 3D video paradigm that empowers extended reality, stand…
Objects play a crucial role in our everyday activities. Though multisensory object-centric learning has shown great potential lately, the modeling of objects in prior work is rather unrealistic. ObjectFolder 1.0 is a recent dataset that…
In this paper, I will attempt to establish a framework for representation in virtual worlds that may allow for input data from many different scales and virtual physics to be merged. For example, a typical virtual environment must…
The adoption of virtual reality (VR) technologies has rapidly gained momentum in recent years as companies around the world begin to position the so-called "metaverse" as the next major medium for accessing and interacting with the…
This paper addresses the scarcity of large-scale datasets for accurate object-in-hand pose estimation, which is crucial for robotic in-hand manipulation within the ``Perception-Planning-Control" paradigm. Specifically, we introduce VinT-6D,…
Collecting embodied interaction data at scale remains costly and difficult due to the limited accessibility of conventional interfaces. We present a gamified data collection framework based on Unity that combines procedural scene…
The success of deep learning in speaker recognition relies heavily on the use of large datasets. However, the data-hungry nature of deep learning methods has already being questioned on account the ethical, privacy, and legal concerns that…
Despite their importance in training artificial intelligence systems, large datasets remain challenging to acquire. For example, the ImageNet dataset required fourteen million labels of basic human knowledge, such as whether an image…