Given a representation of a 3D scene of some kind (point cloud, mesh, voxels, etc.), the task is to create an algorithm that can produce photorealistic renderings of this scene from an arbitrary viewpoint. Sometimes, the task is accompanied by image/scene appearance manipulation.
데이터셋
출처에 같은 태스크 이름으로 연결된 데이터셋입니다. 동명 태스크가 포함될 수 있으므로, 비교할 때는 평가 결과의 조건도 확인하세요.
We build a large-scale, comprehensive, and high-quality synthetic dataset for city-scale neural rendering researches. Leveraging the Unreal Engine 5 City Sample project, we developed a pipeline to easily collect aerial and street city views with ground-truth camera poses, as well as a series of additional data modalities. Flexible control on environmental factors like light, weather, human and car crowd is also available in our pipeline, supporting the need of various tasks covering city-scale neural rendering and beyond. The resulting pilot dataset, MatrixCity, contains 67k aerial images and 452k street images from two city maps of total size 28km^2.
Video sequences from a glasshouse environment in Campus Kleinaltendorf(CKA), University of Bonn, captured by PATHoBot, a glasshouse monitoring robot. - 10 Video sequences, 120s long. - 2 cultivar Mavera (yellow) and All- rounder (red). - RGB-D images (Intel RealSense D435i cameras). - Robot odometry and IMU. - High quality sparese instance segmentation labels.
We present a new large-scale photorealistic panoramic dataset named FutureHouse, which has the following characteristics. 1) It contains over 70,000 high-quality models with high-resolution meshes and physical material. All models are measured in real world standards. 2) Selected scene layouts are carefully designed by over 100 excellent artists. All of selected layouts are used in realworld display. 3) It contains 28,579 good panoramic views from 1,752 house-scale scenes. Therefore, it can be used for perspective image tasks as well as omnidirectional image tasks. 4) More physical material representation. Most materials are represent by microfacet BRDF modeling metalness, and the rest are represent by special shading models, e.g., cloth material and transmission material. 5) High rendering quality. Benefiting from commercial rendering engine, Unreal engine 4, and powerful deep learning super sampling (DLSS), our renderings have less noise. Our SVBRDF representation including base color and metalness is capable of producing nonmonochrome specular reflectance.
Video sequences captured at a field on Campus Kleinaltendorf (CKA), University of Bonn, captured by BonBot-I, an autonomous weeding robot. The data was captured by mounting an Intel RealSense D435i sensor with a nadir view of the ground. - RGB-D video sequences (Intel RealSense D435i cameras). - Robot odometry and IMU. - Crops and 8 different categories of weeds at different growth stages. - Different illumination conditions. - Three herbicide treatment regimes (30%, 70%, 100%), impacting weed density directly. - High quality sparese instance segmentation labels.
SyntheticFur is a dataset for neural rendering. Collecting and generating high quality fur images is an expensive and difficult process that requires content specialists to generate. By releasing this unique dataset with high quality lighting simulation via ray tracing, this can save time for researchers seeking to advance studies of fur rendering and simulation, without having to recreate this laborious process. The dataset was used for neural rendering research at Google that takes advantage of rasterized image buffers and converts them into high quality raytraced fur renders. We believe that this dataset can contribute to the computer graphics and machine learning community to develop more advanced techniques with fur rendering. It contains approximately 140,000 procedurally generated images and 15 simulations with Houdini. The images consist of fur groomed with different skin primitives and move with various motions in a predefined set of lighting environments.
Dataset of paired thermal and RGB images comprising ten diverse scenes—six indoor and four outdoor scenes— for 3D scene reconstruction and novel view synthesis (e.g. with NeRF).