Reconstruction

Event cameras are sensors that are inspired by biological systems and specialize in capturing changes in brightness. These emerging cameras offer numerous advantages over conventional frame-based cameras, including high dynamic range, high frame rates, and extremely low power consumption. As a result, event cameras are increasingly being used in various fields, such as object detection and tracking, autonomous driving, 3D reconstruction, visual odometry, and SLAM. We have created the first large-scale synthetic event camera voxel 3D reconstruction dataset, comprising over 39,739 simulated event camera 3D object scans from 13 different object categories. Each entry in the dataset contains a 0.5-second, 240fps high frame rate RGB video scan, simulated event camera data, the original 3D model, and a converted 32x32x32 voxel model. The 3D models used in this dataset are from ShapeNet (Link: https://shapenet.org/). Although this dataset only provides voxel representation for ground truth, obtaining other types of representation such as point cloud as ground truth will be trivial with the provided gltf 3D model. We hope that by publishing this dataset, we can accelerate the advancement of event-based 3D reconstruction. The original paper is available at: IEEE Xplore - https://ieeexplore.ieee.org/document/10169359 ArXiv - https://arxiv.org/abs/2309.00385 !!! Due to limited resources, we are unable to release the full dataset, which is around 1.2 TB in size. We would greatly appreciate any organizations willing to host the full dataset for us. (Contact: haodong.chen@sydney.edu.au) !!! The released SynthEVox3D-Tiny dataset, which is the dataset used in the original paper, is around 32 GB. We also provide scripts in the utils folder of the dataset to reproduce our results, making it possible for other researchers to recreate the entire dataset from scratch.

Fashion trends are constantly evolving, but a trained eye can estimate with some accuracy the signature elements of a particular designer's style. 50,000 runway images, spanning 50 fashion designers, from our large repository of proprietary front row images from fashion shows over the past 15 years. The data includes a variety of fashion items, including: shoes, bags, dress, jackets etc.

A synthetic dataset for evaluating non-rigid 3D human reconstruction based on conventional RGB-D cameras. The dataset consist of seven motion sequences of a single human model. Source: Synthetic Human Model Dataset for Skeleton Driven Non-rigid Motion Tracking and 3D Reconstruction

The dataset contains procedurally generated images of transparent vessels containing liquid and objects . The data for each image includes segmentation maps, 3d depth maps, and normal maps of of the liquid or object inside the transparent vessel, and the vessel. In addition, the properties of the materials inside the containers are given(color/transparency/roughness/metalness). In addition, a natural image benchmark for the 3d/depth estimation of objects inside transparent containers is supplied. 3d models of the objects (GTLF) are also supplied.

The volumetric representation of human interactions is one of the fundamental domains in the development of immersive media productions and telecommunication applications. Particularly in the context of the rapid advancement of Extended Reality (XR) applications, this volumetric data has proven to be an essential technology for future XR elaboration. In this work, we present a new multimodal database to help advance the development of immersive technologies. Our proposed database provides ethically compliant and diverse volumetric data, in particular 27 participants displaying posed facial expressions and subtle body movements while speaking, plus 11 participants wearing head-mounted displays (HMDs). The recording system consists of a volumetric capture (VoCap) studio, including 31 synchronized modules with 62 RGB cameras and 31 depth cameras. In addition to textured meshes, point clouds, and multi-view RGB-D data, we use one Lytro Illum camera for providing light field (LF) data simultaneously. Finally, we also provide an evaluation of our dataset employment with regard to the tasks of facial expression classification, HMDs removal, and point cloud reconstruction. The dataset can be helpful in the evaluation and performance testing of various XR algorithms, including but not limited to facial expression recognition and reconstruction, facial reenactment, and volumetric video. HEADSET and its all associated raw data and license agreement will be publicly available for research purposes.