S3E is a novel large-scale multimodal dataset captured by a fleet of unmanned ground vehicles along four designed collaborative trajectory paradigms. S3E consists of 7 outdoor and 5 indoor scenes that each exceed 200 seconds, consisting of well synchronized and calibrated high-quality stereo camera, LiDAR, and high-frequency IMU data.
6 PAPERS • NO BENCHMARKS YET
PolyU-BPCoMa: A Dataset and Benchmark Towards Mobile Colorized Mapping Using a Backpack Multisensorial System
1 PAPER • NO BENCHMARKS YET
The Endomapper dataset is the first collection of complete endoscopy sequences acquired during regular medical practice, including slow and careful screening explorations, making secondary use of medical data. Its original purpose is to facilitate the development and evaluation of VSLAM (Visual Simultaneous Localization and Mapping) methods in real endoscopy data. The first release of the dataset is composed of 50 sequences with a total of more than 13 hours of video. It is also the first endoscopic dataset that includes both the computed geometric and photometric endoscope calibration as well as the original calibration videos. Meta-data and annotations associated to the dataset varies from anatomical landmark and description of the procedure labeling, tools segmentation masks, COLMAP 3D reconstructions, simulated sequences with groundtruth and meta-data related to special cases, such as sequences from the same patient. This information will improve the research in endoscopic VSLAM, a
12 PAPERS • NO BENCHMARKS YET
Hilti SLAM Challenge is a dataset for Simultaneous Localization and Mapping (SLAM) algorithms due to sparsity, varying illumination conditions, and dynamic objects. The sensor platform used to collect this dataset contains a number of visual, lidar and inertial sensors which have all been rigorously calibrated. All data is temporally aligned to support precise multi-sensor fusion. Each dataset includes accurate ground truth to allow direct testing of SLAM results. Raw data as well as intrinsic and extrinsic sensor calibration data from twelve datasets in various environments is provided. Each environment represents common scenarios found in building construction sites in various stages of completion.
9 PAPERS • NO BENCHMARKS YET
MAOMaps is a dataset for evaluation of Visual SLAM, RGB-D SLAM and Map Merging algorithms. It contains 40 samples with RGB and depth images, and ground truth trajectories and maps. These 40 samples are joined into 20 pairs of overlapping maps for map merging methods evaluation. The samples were collected using Matterport3D dataset and Habitat simulator.
TUM monoVO is a dataset for evaluating the tracking accuracy of monocular Visual Odometry (VO) and SLAM methods. It contains 50 real-world sequences comprising over 100 minutes of video, recorded across different environments – ranging from narrow indoor corridors to wide outdoor scenes. All sequences contain mostly exploring camera motion, starting and ending at the same position: this allows to evaluate tracking accuracy via the accumulated drift from start to end, without requiring ground-truth for the full sequence. In contrast to existing datasets, all sequences are photometrically calibrated: the dataset creators provide the exposure times for each frame as reported by the sensor, the camera response function and the lens attenuation factors (vignetting).
11 PAPERS • NO BENCHMARKS YET
Virtual KITTI is a photo-realistic synthetic video dataset designed to learn and evaluate computer vision models for several video understanding tasks: object detection and multi-object tracking, scene-level and instance-level semantic segmentation, optical flow, and depth estimation.
121 PAPERS • 1 BENCHMARK
TUM RGB-D is an RGB-D dataset. It contains the color and depth images of a Microsoft Kinect sensor along the ground-truth trajectory of the sensor. The data was recorded at full frame rate (30 Hz) and sensor resolution (640x480). The ground-truth trajectory was obtained from a high-accuracy motion-capture system with eight high-speed tracking cameras (100 Hz).
193 PAPERS • NO BENCHMARKS YET
The New College Data is a freely available dataset collected from a robot completing several loops outdoors around the New College campus in Oxford. The data includes odometry, laser scan, and visual information. The dataset URL is not working anymore.
16 PAPERS • NO BENCHMARKS YET
InteriorNet is a RGB-D for large scale interior scene understanding and mapping. The dataset contains 20M images created by pipeline:
26 PAPERS • NO BENCHMARKS YET