{"url":"/dataset/ru-apc","name":"RU-APC","full_name":"Rutgers APC","description_markdown":"The **RU-APC (Rutgers APC) dataset** is a valuable resource for researchers and developers working on **robotic perception solutions** for **warehouse picking challenges**. Let me provide you with some details about this dataset:\r\n\r\n1. **Dataset Overview**:\r\n   - The **Rutgers APC RGB-D Dataset** is provided by the **PRACSYS lab at Rutgers University**.\r\n   - It is designed to equip the research community with rich data for evaluating and improving robotic perception in the context of warehouse automation.\r\n   - The dataset contains **10,368 depth and RGB registered images**.\r\n\r\n2. **Object Annotations**:\r\n   - For **24 of the Amazon Picking Challenge (APC) objects**, the dataset includes **hand-annotated 6DOF poses**.\r\n   - These annotations are crucial for accurate pose estimation during object manipulation tasks.\r\n\r\n3. **3D Mesh Models**:\r\n   - Alongside the images, the dataset provides **3D mesh models** for **all 25 APC objects** (excluding the *mead_index_cards*).\r\n   - These mesh models can be used for training recognition algorithms.\r\n\r\n4. **Context and Significance**:\r\n   - The **Amazon Picking Challenge (APC)** is a competition that focuses on perception, motion planning, and grasping of different objects placed inside bins of an **Amazon-Kiva Pod**.\r\n   - Warehouse automation, especially picking and placing products on shelves, is a critical area of interest.\r\n   - Unlike simpler tabletop scenarios, warehouse shelves introduce challenges due to narrow, dark, and obscuring bins.\r\n   - Accurate pose estimation is essential for successful object manipulation within these shelves.\r\n\r\n5. **Variety of Objects**:\r\n   - The selected objects in the dataset were used during the **first Amazon Picking Challenge** held in Seattle in May 2015.\r\n   - These objects exhibit diversity in terms of size, shape, texture, transparency, and other characteristics.\r\n   - They represent good candidates for robotic units to transport in warehouse environments.\r\n\r\n(1) Rutgers APC RGB-D Dataset - PRACSYS Group. https://robotics.cs.rutgers.edu/pracsys/rutgers-apc-rgb-d-dataset/.\r\n(2) Datasets - PRACSYS Group. https://robotics.cs.rutgers.edu/pracsys/datasets/.\r\n(3) Datasets - BOP: Benchmark for 6D Object Pose Estimation. https://bop.felk.cvut.cz/datasets/.","description_withheld":null,"homepage":"http://robotics.cs.rutgers.edu/pracsys/rutgers-apc-rgb-d-dataset","introduced_date":"2015-09-03","introduced_date_note":null,"introduced_by":{"paper":"/paper/a-dataset-for-improved-rgbd-based-object","title":"A Dataset for Improved RGBD-based Object Detection and Pose Estimation for Warehouse Pick-and-Place","first_author":"Colin Rennie","url":null},"license":null,"modalities":[],"tasks":[],"languages":[],"variants":["RU-APC"],"data_loaders":[],"num_papers_in_archive":9,"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."}