PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes

Estimating the 6D pose of known objects is important for robots to interact with the real world. The problem is challenging due to the variety of objects as well as the complexity of a scene caused by clutter and occlusions between objects... (read more)

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Datasets


Introduced in the Paper:

YCB-Video

Mentioned in the Paper:

LINEMOD
TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
6D Pose Estimation using RGB YCB-Video PoseCNN Accuracy (ADD) 21.3% # 2
Mean ADD 53.7 # 2
Mean ADD-S 75.9 # 1
6D Pose Estimation using RGBD YCB-Video PoseCNN (ICP) Mean ADD 79.3 # 3
6D Pose Estimation YCB-Video PoseCNN+ICP ADDS AUC 93.0 # 5
6D Pose Estimation using RGBD YCB-Video ALL PoseCNN+ICP Mean ADD-S 93 # 3

Methods used in the Paper


METHOD TYPE
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