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21 dataset results for Novel View Synthesis

KITTI (Karlsruhe Institute of Technology and Toyota Technological Institute) is one of the most popular datasets for use in mobile robotics and autonomous driving. It consists of hours of traffic scenarios recorded with a variety of sensor modalities, including high-resolution RGB, grayscale stereo cameras, and a 3D laser scanner. Despite its popularity, the dataset itself does not contain ground truth for semantic segmentation. However, various researchers have manually annotated parts of the dataset to fit their necessities. Álvarez et al. generated ground truth for 323 images from the road detection challenge with three classes: road, vertical, and sky. Zhang et al. annotated 252 (140 for training and 112 for testing) acquisitions – RGB and Velodyne scans – from the tracking challenge for ten object categories: building, sky, road, vegetation, sidewalk, car, pedestrian, cyclist, sign/pole, and fence. Ros et al. labeled 170 training images and 46 testing images (from the visual odome

2,430 PAPERS • 120 BENCHMARKS

ShapeNet

ShapeNet is a large scale repository for 3D CAD models developed by researchers from Stanford University, Princeton University and the Toyota Technological Institute at Chicago, USA. The repository contains over 300M models with 220,000 classified into 3,135 classes arranged using WordNet hypernym-hyponym relationships. ShapeNet Parts subset contains 31,693 meshes categorised into 16 common object classes (i.e. table, chair, plane etc.). Each shapes ground truth contains 2-5 parts (with a total of 50 part classes).

1,195 PAPERS • 11 BENCHMARKS

NeRF

NeRF (Neural Radiance Fields)

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.

807 PAPERS • 1 BENCHMARK

SYNTHIA (SYNTHetic Collection of Imagery and Annotations)

The SYNTHIA dataset is a synthetic dataset that consists of 9400 multi-viewpoint photo-realistic frames rendered from a virtual city and comes with pixel-level semantic annotations for 13 classes. Each frame has resolution of 1280 × 960.

392 PAPERS • 9 BENCHMARKS

LLFF

LLFF (Local Light Field Fusion)

Local Light Field Fusion (LLFF) is a practical and robust deep learning solution for capturing and rendering novel views of complex real-world scenes for virtual exploration. The dataset consists of both renderings and real images of natural scenes. The synthetic images are rendered from the SUNCG and UnrealCV where SUNCG contains 45000 simplistic house and room environments with texture-mapped surfaces and low geometric complexity. UnrealCV contains a few large-scale environments modeled and rendered with extreme detail. The real images are 24 scenes captured from a handheld cellphone.

100 PAPERS • 1 BENCHMARK

KITTI-360

KITTI-360 is a large-scale dataset that contains rich sensory information and full annotations. It is the successor of the popular KITTI dataset, providing more comprehensive semantic/instance labels in 2D and 3D, richer 360 degree sensory information (fisheye images and pushbroom laser scans), very accurate and geo-localized vehicle and camera poses, and a series of new challenging benchmarks.

40 PAPERS • 1 BENCHMARK

BlendedMVS

BlendedMVS is a novel large-scale dataset, to provide sufficient training ground truth for learning-based MVS. The dataset was created by applying a 3D reconstruction pipeline to recover high-quality textured meshes from images of well-selected scenes. Then, these mesh models were rendered to color images and depth maps.

38 PAPERS • NO BENCHMARKS YET

Spaces

We introduce our new dataset, Spaces, to provide a more challenging shared dataset for future view synthesis research. Spaces consists of 100 indoor and outdoor scenes, captured using a 16-camera rig. For each scene, we captured image sets at 5-10 slightly different rig positions (within ∼10cm of each other). This jittering of the rig position provides a flexible dataset for view synthesis, as we can mix views from different rig positions for the same scene during training. We calibrated the intrinsics and the relative pose of the rig cameras using a standard structure from motion approach, using the nominal rig layout as a prior. We corrected exposure differences . For our main experiments we undistort the images and downsample them to a resolution of 800 × 480. We use 90 scenes from the dataset for training and hold out 10 for evaluation.

16 PAPERS • NO BENCHMARKS YET

Mip-NeRF 360

Mip-NeRF 360 (Unbounded Anti-Aliased Neural Radiance Fields)

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.

14 PAPERS • 1 BENCHMARK

PhotoShape

The PhotoShape dataset consists of photorealistic, relightable, 3D shapes produced by the work proposed in the work of Park et al. (2021).

12 PAPERS • 1 BENCHMARK

Shiny dataset

The shiny folder contains 8 scenes with challenging view-dependent effects used in our paper. We also provide additional scenes in the shiny_extended folder. The test images for each scene used in our paper consist of one of every eight images in alphabetical order.

8 PAPERS • NO BENCHMARKS YET

ACID (Aerial Coastline Imagery Dataset)

ACID consists of thousands of aerial drone videos of different coastline and nature scenes on YouTube. Structure-from-motion is used to get camera poses.

6 PAPERS • 1 BENCHMARK

RealEstate10K

RealEstate10K is a large dataset of camera poses corresponding to 10 million frames derived from about 80,000 video clips, gathered from about 10,000 YouTube videos. For each clip, the poses form a trajectory where each pose specifies the camera position and orientation along the trajectory. These poses are derived by running SLAM and bundle adjustment algorithms on a large set of videos.

4 PAPERS • 1 BENCHMARK

UASOL (A large-scale high-resolution outdoor stereo dataset)

The UASOL an RGB-D stereo dataset, that contains 160902 frames, filmed at 33 different scenes, each with between 2 k and 10 k frames. The frames show different paths from the perspective of a pedestrian, including sidewalks, trails, roads, etc. The images were extracted from video files with 15 fps at HD2K resolution with a size of 2280 × 1282 pixels. The dataset also provides a GPS geolocalization tag for each second of the sequences and reflects different climatological conditions. It also involved up to 4 different persons filming the dataset at different moments of the day.

3 PAPERS • 1 BENCHMARK

DONeRF: Evaluation Dataset

This is the dataset for the CGF 2021 paper "DONeRF: Towards Real-Time Rendering of Compact Neural Radiance Fields using Depth Oracle Networks".

2 PAPERS • 1 BENCHMARK

RTMV

RTMV is a large-scale synthetic dataset for novel view synthesis consisting of ∼300k images rendered from nearly 2000 complex scenes using high-quality ray tracing at high resolution (1600 × 1600 pixels). The dataset is orders of magnitude larger than existing synthetic datasets for novel view synthesis, thus providing a large unified benchmark for both training and evaluation. Using 4 distinct sources of high-quality 3D meshes, the scenes of our dataset exhibit challenging variations in camera views, lighting, shape, materials, and textures.

2 PAPERS • 1 BENCHMARK

SWORD ('Scenes with occluded regions' dataset)

The new dataset contains around 1,500 train videos and 290 test videos, with 50 frames per video on average. The dataset was obtained after processing the manually captured video sequences of static real-life urban scenes. The main property of the dataset is the abundance of close objects and, consequently, the larger prevalence of occlusions. According to the introduced heuristic, the mean area of occluded image parts for SWORD is approximately five times larger than for RealEstate10k data (14% vs 3% respectively). This rationalizes the collection and usage of SWORD and explains that SWORD allows training more powerful models despite being of smaller size.

2 PAPERS • 1 BENCHMARK

OMMO

OMMO is a new benchmark for several outdoor NeRF-based tasks, such as novel view synthesis, surface reconstruction, and multi-modal NeRF. It contains complex objects and scenes with calibrated images, point clouds and prompt annotations.

1 PAPER • NO BENCHMARKS YET

OmniObject3D

OmniObject3D is a large vocabulary 3D object dataset with massive high-quality real-scanned 3D objects. OmniObject3D has several appealing properties:

1 PAPER • NO BENCHMARKS YET

SILVR (A Synthetic Immersive Large-Volume Plenoptic Dataset)

We present SILVR, a dataset of light field images for six-degrees-of-freedom navigation in large fully-immersive volumes. The SILVR dataset is short for "Synthetic Immersive Large-Volume Ray" dataset.

1 PAPER • NO BENCHMARKS YET

SPARF

SPARF is a large-scale ShapeNet-based synthetic dataset for novel view synthesis consisting of ~17 million images rendered from nearly 40,000 shapes at high resolution (400×400 pixels).

1 PAPER • NO BENCHMARKS YET

Datasets

21 dataset results for Novel View Synthesis