WAPR, wide-angle pose refinement
WAPR

Application cases应用案例

This chapter presents ROBI[2] industrial object detection, tracking, reconstruction and simulated manipulation experiments, with their inputs, recorded results and evaluation conditions. The standard BOP benchmark[3] and LM-O[1] results are documented under 6D pose tasks and interfaces. General detection usage is described under 2D detection and segmentation.

本章介绍 ROBI[2] 工业零件检测、跟踪、重建及机器人仿真实验，说明其输入、记录结果与评估条件。标准 BOP 基准[3]与 LM-O[1] 结果见6D 位姿任务与接口，通用检测用法见2D 检测与分割。

References and licenses参考文献与许可

  1. LM-O data — CC-BY-SA-4.0. Data and model excerpt, not the loader code.数据与模型摘录，不是读取代码。 · Original source原始来源 · GitHub: BOP toolkitGitHub：BOP 工具集
    Brachmann et al. Learning 6D Object Pose Estimation Using 3D Object Coordinates. ECCV 2014. · Paper论文 ↩ ↩
  2. ROBI data — Not separately verified / 未单独核实. The saved public poses and dataset are credited to ROBI; an independent grant to redistribute them has not been verified.保存的公开位姿和数据均注明 ROBI 来源；其再分发授权尚未独立核实。 · GitHubGitHub
    Yang et al. ROBI: A Multi-View Dataset for Reflective Objects in Robotic Bin-Picking. IROS 2021. · Paper论文 ↩ ↩
  3. BOP benchmark · GitHubGitHub
    Hodaň et al. BOP: Benchmark for 6D Object Pose Estimation. ECCV 2018. · Paper论文 ↩ ↩

Model weights and dataset/task assets may have separate terms. WAPR's source license does not replace them.模型权重、数据集与任务资源可能有独立条款；WAPR 源码许可不替代这些许可。