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Neural Radiance Fields (NeRF) is a method for synthesizing novel views of complex scenes by optimizing an underlying continuous volumetric scene function using a sparse set of input views. The dataset contains three parts with the first 2 being synthetic renderings of objects called Diffuse Synthetic 360◦ and Realistic Synthetic 360◦ while the third is real images of complex scenes. Diffuse Synthetic 360◦ consists of four Lambertian objects with simple geometry. Each object is rendered at 512x512 pixels from viewpoints sampled on the upper hemisphere. Realistic Synthetic 360◦ consists of eight objects of complicated geometry and realistic non-Lambertian materials. Six of them are rendered from viewpoints sampled on the upper hemisphere and the two left are from viewpoints sampled on a full sphere with all of them at 800x800 pixels. The real images of complex scenes consist of 8 forward-facing scenes captured with a cellphone at a size of 1008x756 pixels.

Mip-NeRF 360 is an extension to the Mip-NeRF that uses a non-linear parameterization, online distillation, and a novel distortion-based regularize to overcome the challenge of unbounded scenes. The dataset consists of 9 scenes with 5 outdoors and 4 indoors, each containing a complex central object or area with a detailed background.

This dataset focus on two blur types: camera motion blur and defocus blur. For each type of blur we synthesize $5$ scenes using Blender. We manually place multi-view cameras to mimic real data capture. To render images with camera motion blur, we randomly perturb the camera pose, and then linearly interpolate poses between the original and perturbed poses for each view. We render images from interpolated poses and blend them in linear RGB space to generate the final blurry images. For defocus blur, we use the built-in functionality to render depth-of-field images. We fix the aperture and randomly choose a focus plane between the nearest and furthest depth. We also captured $20$ real world scenes with $10$ scenes for each blur type for a qualitative study. The camera used was a Canon EOS RP with manual exposure mode. We captured the camera motion blur images by manually shaking the camera during exposure, while the reference images are taken using a tripod. To capture defocus images, we choose a large aperture. We compute the camera poses of blurry and reference images in the real world scenes using the COLMAP.

We present a large-scale dataset for 3D urban scene understanding. Compared to existing datasets, our dataset consists of 75 outdoor urban scenes with diverse backgrounds, encompassing over 15,000 images. These scenes offer 360◦ hemispherical views, capturing diverse foreground objects illuminated under various lighting conditions. Additionally, our dataset encompasses scenes that are not limited to forward-driving views, addressing the limitations of previous datasets such as limited overlap and coverage between camera views. The closest pre-existing dataset for generalizable evaluation is DTU [2] (80 scenes) which comprises mostly indoor objects and does not provide multiple foreground objects or background scenes. We use the Parallel Domain synthetic data generation to render high-fidelity 360◦ scenes. We select 3 different maps i.e. SF 6thAndMission, SF GrantAndCalifornia and SF VanNessAveAndTurkSt and sample 75 different scenes across all 3 maps as our backgrounds (All 75 scenes across 3 maps are significantly different road scenes from each other, captured at different viewpoints in the city). We select 20 different cars in 50 different textures for training and randomly sample from 1 to 4 cars to render in a scene. We refer to this dataset as NeRDS 360: NeRF for Reconstruction, Decomposition and Scene Synthesis of 360◦ outdoor scenes. In total, we generate 15k renderings by sampling 200 cameras in a hemispherical dome at a fixed distance from the center of cars. We held out 5 scenes with 4 different cars and different backgrounds for testing, comprising 100 cameras distributed uniformly sampled in the upper hemisphere, different from the camera distributions used for training. We use the diverse validation camera distribution to test our approach’s ability to generalize to unseen viewpoints as well as unseen scenes during training. Our dataset and the corresponding task is extremely challenging due to occlusions, diversity of backgrounds, and rendered objects with various lightning and shadows. Our task entails reconstructing 360◦ hemispherical views of complete scenes using a handful of observations i.e. 1 to 5 as shown by red cameras whereas evaluating using all 100 hemispherical views, hence our task requires strong priors for novel view synthesis of outdoor scenes. Tasks our dataset support: - Generaliazable Novel view synthesis (Few shot evaluation) - Novel view synthesis (Overfitting evaluation) - 6D pose estimation - Object editing - Depth estimation - Semantic Segmentation - Instance Segmentation

This is the dataset for the CGF 2021 paper "DONeRF: Towards Real-Time Rendering of Compact Neural Radiance Fields using Depth Oracle Networks". Please note the original creators of the individual 3D scenes themselves (individual license files can be found in the individual .zip archives in the dataset): Bulldozer by "Heinzelnisse" (CC-BY-NC): https://www.blendswap.com/blend/11490 Forest by Robin Tran (CC-BY-SA 3.0): https://cloud.blender.org/p/gallery/5fbd186ec57d586577c57417 Classroom by Christophe Seux (CC-0): https://download.blender.org/demo/test/classroom.zip San Miguel by Guillermo M. Leal Llaguno (CC-BY 3.0): https://casual-effects.com/g3d/data10/index.html# Pavillon by Hamza Cheggour / "eMirage" (CC-BY): https://download.blender.org/demo/test/pabellon_barcelona_v1.scene_.zip Barbershop by Blender Animation Studio (CC-BY): https://svn.blender.org/svnroot/bf-blender/trunk/lib/benchmarks/cycles/barbershop_interior/

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IBL-NeRF Dataset. Contains multi-view images with its intrinsic components.

LLNeRF Dataset is a real-world dataset as a benchmark for model learning and evaluation. To obtain real low-illumination images with real noise distributions, photos are taken at nighttime outdoor scenes or low-light indoor scenes containing diverse objects. Since the ISP operations are device dependent and the noise distributions across devices are also different, the data is collected using a mobile phone camera and a DSLR camera to enrich the diversity of the dataset.

4 different synthetic datasets generated by Blender

We establish an object-centric multi-view LiDAR dataset, which we dub the NeRF-MVL dataset, containing carefully calibrated sensor poses, acquired from multi-LiDAR sensor data from real autonomous vehicles. It contains more than 76k frames covering two types of collecting vehicles, three LiDAR settings, two collecting paths, and nine object categories.

This synthetic event dataset is used in Robust e-NeRF to study the collective effect of camera speed profile, contrast threshold variation and refractory period on the quality of NeRF reconstruction from a moving event camera. It is simulated using an improved version of ESIM with three different camera configurations of increasing difficulty levels (i.e. easy, medium and hard) on seven Realistic Synthetic $360^{\circ}$ scenes (adopted in the synthetic experiments of NeRF), resulting in a total of 21 sequence recordings. Please refer to the Robust e-NeRF paper for more details. The dataset allows for a retrospective comparison between event-based and image-based NeRF reconstruction methods, as the event sequences were simulated under highly similar conditions as the NeRF synthetic dataset. In particular, we adopt the same camera intrinsics and camera distance to the object at the origin. Furthermore, the event camera travels in a hemi-/spherical spiral motion about the object, thereby having a similar camera pose distribution for training. Apart from that, we also use the same test camera poses/views. Nonetheless, this new synthetic event dataset is not only specific to NeRF reconstruction, but also suitable for novel view synthesis, 3D reconstruction, localization and SLAM in general.

Synthetic dataset comprising three different environments for multi-camera dynamic novel view synthesis for soccer. This dataset is made compatible for Nerfstudio, and includes data parsers with various settings to reproduce the settings of our paper "Dynamic NeRFs for Soccer Scenes" and more. The data consists of 3 synthetic scenes built using Blender. Each scene contains from 20 to 30 synchronized and calibrated cameras that capture a 4-second long scene at 25 FPS. For each scene, the downloadable data consists of images and corresponding poses. These are ready to use either with our code, either with Nerfstudio using the slightly different data parsers available in this folder. Please contact the corresponding author (sacha.lewin@uliege.be) if you need the Blender scenes.

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E2NeRF