135 machine learning datasets
135 dataset results
SOTIF-PCOD is a dataset generated using the CARLA simulator, specifically designed for Safety of the Intended Functionality (SOTIF) research. It consists of 547 frames of LiDAR point cloud data formatted in the KITTI standard, representing a single SOTIF-related use case.
This dataset contains Material-Point-Method (MPM) simulations for various materials, including water, sand, plasticine, elasticity, jelly, rigid collisions, and melting. Each material is represented as point-clouds that evolve over time. The dataset is designed for learning and predicting MPM-based physical simulations.
This dataset contains Material-Point-Method (MPM) simulations for various materials, including water, sand, plasticine, jelly, and rigid collisions. Each material is represented as point-clouds that evolve over time. The dataset is designed for learning and predicting MPM-based physical simulations. Each material contains 50 trajectories with different initial velocity field.
The Watch Your Mouth dataset is a custom silent speech dataset consisting of depth-only recordings of users silently mouthing full English sentences, captured using consumer-grade depth cameras such as the iPhone TrueDepth sensor. Sentences were carefully curated to cover diverse visemic and phonetic patterns, supporting the development of models capable of generalizing across varied speech content. Each sentence-level utterance provides a temporally aligned depth sequence and corresponding ground truth text. Please see more details in the paper Watch Your Mouth: Silent Speech Recognition with Depth Sensing.
This dataset includes 3D point-cloud and 2D imagery from a flash LiDAR...
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AcousticRooms is a large-scale synthetic room impulse response (RIR) dataset designed for cross-room RIR prediction tasks. It includes over 300,000 single-channel RIRs simulated across 260 rooms spanning 10 categories, such as apartment, auditorium, office, and cafe. Each room features high-quality 3D spatial geometry and randomized material properties drawn from a diverse library of 332 acoustic materials across 11 categories. For more details, please check https://github.com/facebookresearch/AcousticRooms
The RGB-D Scenes Dataset v2 consists of 14 scenes containing furniture (chair, coffee table, sofa, table) and a subset of the objects in the RGB-D Object Dataset (bowls, caps, cereal boxes, coffee mugs, and soda cans). Each scene is a point cloud created by aligning a set of video frames using Patch Volumes Mapping.
The RGB-D Scenes Dataset contains 8 scenes annotated with objects that belong to the Washington RGB-D Object Dataset. Each scene is a single video sequence consisting of multiple RGB-D frames.
we propose the augmented KITTI dataset with fog for both camera and LiDAR sensors with different visibility ranges from 20 to 80 meters to best match realistic fog environment.
Overview The goal: using simulation data to train neural networks to estimate the pose of a rover's camera with respect to a known target object
Sparse LiDAR extracted from velodyne 64 beams in KITTI dataset. It contains severals LiDAR: LiDAR 2 beams, LiDAR 4 beams, LiDAR 8 beams, LiDAR 16 beams, LiDAR 32 beams
Abstract: We introduce the multi-spectral stereo (MS2) outdoor dataset, including stereo RGB, stereo NIR, stereo thermal, stereo LiDAR data, and GPS/IMU information. Our dataset provides rectified and synchronized 184K data pairs taken from city, residential, road, campus, and suburban areas in the morning, daytime, and nighttime under clear-sky, cloudy, and rainy conditions. We designed the dataset to explore various computer vision algorithms from multi-spectral sensor data to achieve high-level performance, reliability, and robustness against challenging environments.
The volumetric representation of human interactions is one of the fundamental domains in the development of immersive media productions and telecommunication applications. Particularly in the context of the rapid advancement of Extended Reality (XR) applications, this volumetric data has proven to be an essential technology for future XR elaboration. In this work, we present a new multimodal database to help advance the development of immersive technologies. Our proposed database provides ethically compliant and diverse volumetric data, in particular 27 participants displaying posed facial expressions and subtle body movements while speaking, plus 11 participants wearing head-mounted displays (HMDs). The recording system consists of a volumetric capture (VoCap) studio, including 31 synchronized modules with 62 RGB cameras and 31 depth cameras. In addition to textured meshes, point clouds, and multi-view RGB-D data, we use one Lytro Illum camera for providing light field (LF) data simul
This dataset called Indoor Lodz University of Technology Point Cloud Dataset (InLUT3D) is a point cloud set tailored for real object classification and both semantic and instance segmentation tasks. Comprising of 321 scans, some areas in the dataset are covered by multiple scans. All of them are captured using the Leica BLK360 scanner.