383 machine learning datasets
383 dataset results
CPNet dataset has a collection of 25 categories, 2,334 models based on ShapeNetCore, which includes 1,000+ correspondence sets with 104,861 points.
Copyright (C) 2021 Ante Qu antequ@cs.stanford.edu.
The ARPA-E funded TERRA-REF project is generating open-access reference datasets for the study of plant sensing, genomics, and phenomics. Sensor data were generated by a field scanner sensing platform that captures color, thermal, hyperspectral, and active flourescence imagery as well as three dimensional structure and associated environmental measurements. This dataset is provided alongside data collected using traditional field methods in order to support calibration and validation of algorithms used to extract plot level phenotypes from these datasets.
XA Bin-Picking is a point-cloud dataset comprising both simulated and real-world scenes with three industrial parts. Synthesized scenes consists of 1000 training samples. The test samples are real scenes and the ground truth instance labels are made manually. There are 20 to 30 identical types of parts randomly piled up in a scene. Each scene contains about 60,000 boundary points. Each point in the scene has instance annotations. The parts are texture-less and have no discernible color. Both of training samples and test sam- ples only contain the boundary points of parts.
VesselGraph is a dataset of whole-brain vessel graphs based on specific imaging protocols. Specifically, vascular graphs are extracted using a refined graph extraction scheme leveraging the volume rendering engine Voreen and provided in an accessible and adaptable form through the OGB and PyTorch Geometric dataloaders.
The datasets includes curves drawn on 3D surfaces (triangle meshes) in Virtual Reality. A total of 2,880 curves were created using two different techniques by 20 users on 6 meshes. For each curve, a 3D curve executed by the user is provided, the projected curve created on the mesh, and the ground truth target curve on the mesh. For collecting the data, two different task types were employed, which are described in the paper.
EUEN17037 Daylight and View Standard Test Dataset.
Details about the creation of the dataset can be seen in https://arxiv.org/abs/2110.06139.
The dataset, generated from a scientific simulation, consists of a time series (251 steps) of 3D scalar fields on a spherical 180x201x360 grid covering 500 Myr of geological time. Each time step is 2 Myrs, and the fields are:
It comprises synthetic mesh sequences from Deformation Transfer for Triangle Meshes.
This is the supplemental data for our paper on how to benchmark registrations of serial sections with ground truths. There are three main modalities and one further, as a reference.
1、 Competition name:
Dataset consist of both real captures from Photoneo PhoXi structured light scanner devices annotated by hand and synthetic samples produced by custom generator. In comparison with existing datasets for 6D pose estimation, some notable differences include:
ArtImage is a synthetic dataset of articulated object models of 5 categories from PartNet-Mobility for articulated object tasks in category level.
Stack of 2D gray images of glass fiber-reinforced polyamide 66 (GF-PA66) 3D X-ray Computed Tomography (XCT) specimen.
Dataset built from partial reconstructions of real-world indoor scenes using RGB-D sequences from ScanNet, aimed at estimating the unknown position of an object (e.g. where is the bag?) given a partial 3D scan of a scene. The dataset mostly consists of bedrooms, bathrooms, and living rooms. Some room types like closet and gym only have a few instances.
To study the data-scarcity mitigation for learning-based visual localization methods via sim-to-real transfer, we curate and now present the CrossLoc benchmark datasets—a multimodal aerial sim-to-real data available for flights above nature and urban terrains. Unlike the previous computer vision datasets focusing on localization in a single domain (mostly real RGB images), the provided benchmark datasets include various multimodal synthetic cues paired to all real photos. Complementary to the paired real and synthetic data, we offer rich synthetic data that efficiently fills the flight envelope volume in the vicinity of the real data.
This dataset accompanies the linked SerialTrack paper and provides test case data (2D/3D, varying particle density) across a range of synthetic and experimental imaging modalities. Included test cases can be used for further code development, validation of and comparisons for existing particle tracking codes, and/or evaluating and learning to use our SerialTrack code on known data.
PolyU-BPCoMa: A Dataset and Benchmark Towards Mobile Colorized Mapping Using a Backpack Multisensorial System
The ABCD Study is a prospective longitudinal study starting at the ages of 9-10 and following participants for 10 years. The study includes a diverse sample of nearly 12,000 youth enrolled at 21 research sites across the country. It measures brain development (via structural, task functional, and resting state functional imaging), social, emotional, and cognitive development, mental health, substance use and attitudes, gender identity and sexual health, bio-specimens, as well as a variety of physical health, and environmental factors.