RT-Less：A RGB Dataset for 6D Pose Estimation of
A Multi-Scene Image Dataset for Pose Estimation
Large public unique dataset:
RTL contains 258K real and synthetic images of reflective texture-less metal parts.
The scene setup contains many variables to simulate real scene and provide varying levels.
Industrial multi-view acquisition:
Camera placement uses the eye-in-hand method to simulate a real industrial view.
Ground truth pose & bounding-boxes:
Accurate annotations for each object are provided.
Various CAD models:
Three types of formats of CAD models were provided to assist training.
38 reflective texture-less machined parts with typical industrial features.
32 scenes simulate real scenes in terms of parts placement, parts types and shape, lighting, background, etc.
Use CAD models only without real images for training.
Use real images for training (CAD models are optional).
Use object detection simulation module to test the pose estimation method only.
Use build-in real object detection module to test the object detection and pose estimation methods as a whole.
Few occlusions and clutters scenes: 1, 3, 7, 11, 13, 15, 16, 20, 24, 28, 29, 31, 32
Slight occlusion and few clutters scenes: 6, 5, 12, 15, 17, 22, 24, 25, 26, 27
Severe occlusion and clutter scenes: 2, 4, 8, 9, 10, 14, 18, 19, 21, 23, 27, 30
Some python scripts to help you understand and use RTL
models BOP format