{"url":"/dataset/ic-bin","name":"IC-BIN","full_name":null,"description_markdown":"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 crowd**¹². This dataset is specifically designed to address the challenges posed by **reflective objects in robotic bin-picking scenarios**.\r\n\r\nHere are the key details about the IC-BIN dataset:\r\n\r\n1. **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.\r\n\r\n2. **Contents**:\r\n   - The dataset comprises **multiple objects stacked in a bin**.\r\n   - It includes **three scenes**, each containing **two objects** from the **IC-MI dataset**.\r\n   - These scenes were recorded from **different viewpoints** to evaluate object pose estimation methods.\r\n\r\n3. **Challenges**:\r\n   - Reflective objects are often **texture-less** and cannot be reliably recognized using classic techniques based on local descriptors.\r\n   - The high glossiness of these objects can introduce **fake edges** in RGB images and lead to **inaccurate depth measurements**, especially in cluttered bin scenarios.\r\n\r\n4. **Data Annotation**:\r\n   - For each scene, the dataset provides **monochrome/RGB images** and **depth maps** captured from sampled view spheres around the scene.\r\n   - These images and maps are **annotated** with **accurate 6D poses** of visible objects and an associated **visibility score**.\r\n   - Ground truth depth maps were captured using a high-cost Ensenso camera with objects coated in **anti-reflective scanning spray**.\r\n\r\n5. **Utility and Evaluation**:\r\n   - Researchers can use the IC-BIN dataset to evaluate the performance of **depth fusion** algorithms.\r\n   - Evaluation results highlight the difficulty of handling highly reflective objects, especially in challenging cases with **degraded depth data quality**, **severe occlusions**, and cluttered scenes.\r\n\r\n(1) ROBI: A Multi-View Dataset for Reflective Objects in Robotic Bin-Picking. https://arxiv.org/pdf/2105.04112v1.\r\n(2) Datasets - BOP: Benchmark for 6D Object Pose Estimation. https://bop.felk.cvut.cz/datasets/.\r\n(3) ROBI: A Multi-View Dataset for Reflective Objects in Robotic Bin-Picking. https://ar5iv.labs.arxiv.org/html/2105.04112.\r\n(4) undefined. https://www.trailab.utias.utoronto.ca/robi.","description_withheld":null,"homepage":"https://www.trailab.utias.utoronto.ca/robi","introduced_date":null,"introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[],"tasks":[],"languages":[],"variants":["IC-BIN"],"data_loaders":[],"num_papers_in_archive":0,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}