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….
어서 정리를 마무리 해주세요. (댓글 테스트)
네 알겠습니다. (응답 테스트)