IC-BIN

The IC-BIN dataset was introduced by Doumanoglou et al. as part of their research on recovering 6D object pose and predicting next-best-view in the crowd¹². This dataset is specifically designed to address the challenges posed by reflective objects in robotic bin-picking scenarios.

Here are the key details about the IC-BIN dataset:

1. Purpose: The IC-BIN dataset aims to facilitate research in 6D object pose estimation and active vision techniques for reflective objects commonly encountered in bin-picking applications.

2. Contents:

3. The dataset comprises multiple objects stacked in a bin.
4. It includes three scenes, each containing two objects from the IC-MI dataset.

5. These scenes were recorded from different viewpoints to evaluate object pose estimation methods.

6. Challenges:

7. Reflective objects are often texture-less and cannot be reliably recognized using classic techniques based on local descriptors.

8. The high glossiness of these objects can introduce fake edges in RGB images and lead to inaccurate depth measurements, especially in cluttered bin scenarios.

9. Data Annotation:

10. For each scene, the dataset provides monochrome/RGB images and depth maps captured from sampled view spheres around the scene.
11. These images and maps are annotated with accurate 6D poses of visible objects and an associated visibility score.

12. Ground truth depth maps were captured using a high-cost Ensenso camera with objects coated in anti-reflective scanning spray.

13. Utility and Evaluation:

14. Researchers can use the IC-BIN dataset to evaluate the performance of depth fusion algorithms.
15. Evaluation results highlight the difficulty of handling highly reflective objects, especially in challenging cases with degraded depth data quality, severe occlusions, and cluttered scenes.

(1) ROBI: A Multi-View Dataset for Reflective Objects in Robotic Bin-Picking. https://arxiv.org/pdf/2105.04112v1. (2) Datasets - BOP: Benchmark for 6D Object Pose Estimation. https://bop.felk.cvut.cz/datasets/. (3) ROBI: A Multi-View Dataset for Reflective Objects in Robotic Bin-Picking. https://ar5iv.labs.arxiv.org/html/2105.04112. (4) undefined. https://www.trailab.utias.utoronto.ca/robi.