8 dataset results for 6D

EuRoC MAV is a visual-inertial datasets collected on-board a Micro Aerial Vehicle (MAV). The dataset contains stereo images, synchronized IMU measurements, and accurate motion and structure ground-truth. The datasets facilitates the design and evaluation of visual-inertial localization algorithms on real flight data

3 PAPERS • NO BENCHMARKS YET

Fraunhofer IPA Bin-Picking

The Fraunhofer IPA Bin-Picking dataset is a large-scale dataset comprising both simulated and real-world scenes for various objects (potentially having symmetries) and is fully annotated with 6D poses. A pyhsics simulation is used to create scenes of many parts in bulk by dropping objects in a random position and orientation above a bin. Additionally, this dataset extends the Siléane dataset by providing more samples. This allows to e.g. train deep neural networks and benchmark the performance on the public Siléane dataset

3 PAPERS • NO BENCHMARKS YET

UAVA (UAV Assistant)

The UAVA,<i>UAV-Assistant</i>, dataset is specifically designed for fostering applications which consider UAVs and humans as cooperative agents. We employ a real-world 3D scanned dataset (<a href="https://niessner.github.io/Matterport/">Matterport3D</a>), physically-based rendering, a gamiﬁed simulator for realistic drone navigation trajectory collection, to generate realistic multimodal data both from the user’s exocentric view of the drone, as well as the drone’s egocentric view.

3 PAPERS • 1 BENCHMARK

A Billion Ways to Grasp

Robot grasping is often formulated as a learning problem. With the increasing speed and quality of physics simulations, generating large-scale grasping data sets that feed learning algorithms is becoming more and more popular. An often overlooked question is how to generate the grasps that make up these data sets. In this paper, we review, classify, and compare different grasp sampling strategies. Our evaluation is based on a fine-grained discretization of SE(3) and uses physics-based simulation to evaluate the quality and robustness of the corresponding parallel-jaw grasps. Specifically, we consider more than 1 billion grasps for each of the 21 objects from the YCB data set. This dense data set lets us evaluate existing sampling schemes w.r.t. their bias and efficiency. Our experiments show that some popular sampling schemes contain significant bias and do not cover all possible ways an object can be grasped.

2 PAPERS • NO BENCHMARKS YET

eSports Sensors Dataset

The eSports Sensors dataset contains sensor data collected from 10 players in 22 matches in League of Legends. The sensor data collected includes:

2 PAPERS • 2 BENCHMARKS

Gait Dataset (Human Gait Dataset)

Details about the creation of the dataset can be seen in https://arxiv.org/abs/2110.06139.

1 PAPER • NO BENCHMARKS YET

KITTI-6DoF (KITTI-Six Degrees Of Freedom)

KITTI-6DoF is a dataset that contains annotations for the 6DoF estimation task for 5 object categories on 7,481 frames.

1 PAPER • NO BENCHMARKS YET

PaintNet

PaintNet is a dataset for learning robotic spray painting of free-form 3D objects. PaintNet includes more than 800 object meshes and the associated painting strokes collected in a real industrial setting.

1 PAPER • NO BENCHMARKS YET

Datasets

8 dataset results for 6D