The NYU-Depth V2 data set is comprised of video sequences from a variety of indoor scenes as recorded by both the RGB and Depth cameras from the Microsoft Kinect. It features:
838 PAPERS • 20 BENCHMARKS
The SUN RGBD dataset contains 10335 real RGB-D images of room scenes. Each RGB image has a corresponding depth and segmentation map. As many as 700 object categories are labeled. The training and testing sets contain 5285 and 5050 images, respectively.
421 PAPERS • 13 BENCHMARKS
The Matterport3D dataset is a large RGB-D dataset for scene understanding in indoor environments. It contains 10,800 panoramic views inside 90 real building-scale scenes, constructed from 194,400 RGB-D images. Each scene is a residential building consisting of multiple rooms and floor levels, and is annotated with surface construction, camera poses, and semantic segmentation.
378 PAPERS • 5 BENCHMARKS
Diode Dense Indoor/Outdoor DEpth (DIODE) is the first standard dataset for monocular depth estimation comprising diverse indoor and outdoor scenes acquired with the same hardware setup. The training set consists of 8574 indoor and 16884 outdoor samples from 20 scans each. The validation set contains 325 indoor and 446 outdoor samples with each set from 10 different scans. The ground truth density for the indoor training and validation splits are approximately 99.54% and 99%, respectively. The density of the outdoor sets are naturally lower with 67.19% for training and 78.33% for validation subsets. The indoor and outdoor ranges for the dataset are 50m and 300m, respectively.
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The ReDWeb dataset consists of 3600 RGB-RD image pairs collected from the Web. This dataset covers a wide range of scenes and features various non-rigid objects.
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This dataset accompanies our paper on synthesizing the 3D Ken Burns effect from a single image. It consists of 134041 captures from 32 virtual environments where each capture consists of 4 views. Each view contains color-, depth-, and normal-maps at a resolution of 512x512 pixels.
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The HRWSI dataset consists of about 21K diverse high-resolution RGB-D image pairs derived from the Web stereo images. Also, it provides sky segmentation masks, instance segmentation masks as well as invalid pixel masks.
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The MUAD dataset (Multiple Uncertainties for Autonomous Driving), consisting of 10,413 realistic synthetic images with diverse adverse weather conditions (night, fog, rain, snow), out-of-distribution objects, and annotations for semantic segmentation, depth estimation, object, and instance detection. Predictive uncertainty estimation is essential for the safe deployment of Deep Neural Networks in real-world autonomous systems and MUAD allows to a better assess the impact of different sources of uncertainty on model performance.
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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
A synthetic depth estimation dataset for benchmark rendered from a high-quality CAD indoor environment
InfraParis is a novel and versatile dataset supporting multiple tasks across three modalities: RGB, depth, and infrared. From the city to the suburbs, it contains a variety of styles in different areas of the greater Paris area, providing rich semantic information. InfraParis contains 7301 images with bounding boxes and full semantic (19 classes) annotations. We assess various state-of-the-art baseline techniques, encompassing models for the tasks of semantic segmentation, object detection, and depth estimation.
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