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: - The dataset comprises multiple objects stacked in a bin. - It includes three scenes, each containing two objects from the IC-MI dataset. - These scenes were recorded from different viewpoints to evaluate object pose estimation methods. 3. Challenges: - Reflective objects are often texture-less and cannot be reliably recognized using classic techniques based on local descriptors. - The high glossiness of these objects can introduce fake edges in RGB images and lead to inaccurate depth measurements, especially in cluttered bin scenarios. 4. Data Annotation: - For each scene, the dataset provides monochrome/RGB images and depth maps captured from sampled view spheres around the scene. - These images and maps are annotated with accurate 6D poses of visible objects and an associated visibility score. - Ground truth depth maps were captured using a high-cost Ensenso camera with objects coated in anti-reflective scanning spray. 5. Utility and Evaluation: - Researchers can use the IC-BIN dataset to evaluate the performance of depth fusion algorithms. - 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.