6 dataset results for 6D Pose Estimation using RGB

The YCB-Video dataset is a large-scale video dataset for 6D object pose estimation. provides accurate 6D poses of 21 objects from the YCB dataset observed in 92 videos with 133,827 frames.

146 PAPERS • 6 BENCHMARKS

T-LESS

T-LESS is a dataset for estimating the 6D pose, i.e. translation and rotation, of texture-less rigid objects. The dataset features thirty industry-relevant objects with no significant texture and no discriminative color or reflectance properties. The objects exhibit symmetries and mutual similarities in shape and/or size. Compared to other datasets, a unique property is that some of the objects are parts of others. The dataset includes training and test images that were captured with three synchronized sensors, specifically a structured-light and a time-of-flight RGB-D sensor and a high-resolution RGB camera. There are approximately 39K training and 10K test images from each sensor. Additionally, two types of 3D models are provided for each object, i.e. a manually created CAD model and a semi-automatically reconstructed one. Training images depict individual objects against a black background. Test images originate from twenty test scenes having varying complexity, which increases from

78 PAPERS • 2 BENCHMARKS

LM (LINEMOD)

The LM (Linemod) dataset is a valuable resource introduced by Stefan Hinterstoisser and colleagues in their research on model-based training, detection, and pose estimation of texture-less 3D objects in heavily cluttered scenes¹. Let's delve into the details:

33 PAPERS • 5 BENCHMARKS

ApolloCar3D

ApolloCar3DT is a dataset that contains 5,277 driving images and over 60K car instances, where each car is fitted with an industry-grade 3D CAD model with absolute model size and semantically labelled keypoints. This dataset is above 20 times larger than PASCAL3D+ and KITTI, the current state-of-the-art.

17 PAPERS • 14 BENCHMARKS

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

4 PAPERS • NO BENCHMARKS YET

Drunkard's Dataset

Estimating camera motion in deformable scenes poses a complex and open research challenge. Most existing non-rigid structure from motion techniques assume to observe also static scene parts besides deforming scene parts in order to establish an anchoring reference. However, this assumption does not hold true in certain relevant application cases such as endoscopies. To tackle this issue with a common benchmark, we introduce the Drunkard’s Dataset, a challenging collection of synthetic data targeting visual navigation and reconstruction in deformable environments. This dataset is the first large set of exploratory camera trajectories with ground truth inside 3D scenes where every surface exhibits non-rigid deformations over time. Simulations in realistic 3D buildings lets us obtain a vast amount of data and ground truth labels, including camera poses, RGB images and depth, optical flow and normal maps at high resolution and quality.

1 PAPER • 1 BENCHMARK

Datasets

6 dataset results for 6D Pose Estimation using RGB