3D Object Pose Estimation

On-going article

Recent works

  • SSD-6D: Making RGB-Based 3D Detection and 6D Pose Estimation Great Again, ICCV2017
  • PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes, RSS2018
  • (RGB) Deep Object Pose Estimation for Semantic Robotic Grasping of Household Objects (DOPE), CoRL 2018
  • (RGB) Real-time seamless Single Shot 6D Object Pose Prediction, CVPR2018
  • (RGB+D) DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusion, CVPR2019
  • (RGB) DeepIM: Deep Iterative Matching for 6D Pose Estimation, ECCV2018
  • (RGB+D) GRIP: Generative Robust Inference and Perception for Semantic Robot Manipulation in Adversarial Environments, IROS 2019

Metric

  • Average distance (ADD) metric : average 3D distance of model vertices
    – 3D 공간상의 mean distance가 10% 안쪽이면 맞다고 함
    – ADD-S 라는 Closest Object에 대한 Metric도 있음
  • 2D reproduction error (CVPR2018) 5Pixel 이하면 Correct 라고 하고 있음
  • IoU Score (Projection 한 BB 0.5 이상이면)

Dataset

  • YCB-Video
  • LINEMOD Dataset

The goal of Our Project

  • LineMOD Dataset
  • DeepIM: Deep Iterative Matching for 6D Pose Estimation, ECCV2018
  • ECCV2018 SOTA ADD 기준 88.6 
  • 우리 목표 90, 92, 94 으로 잡으면 될 듯 싶음….

Author: rcvlab

RCV연구실 홈페이지 관리자 입니다.

2 thoughts on “3D Object Pose Estimation

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