computer-vision-1
6D Pose Estimation using RGB
3260 papers • 126 benchmarks • 313 datasets
6D Pose Estimation using RGB refers to the task of determining the six degree-of-freedom (6D) pose of an object in 3D space based on RGB images. This involves estimating the position and orientation of an object in a scene, and is a fundamental problem in computer vision and robotics. In this task, the goal is to estimate the 6D pose of an object given an RGB image of the object and the scene, which can be used for tasks such as robotic manipulation, augmented reality, and scene reconstruction. ( Image credit: Segmentation-driven 6D Object Pose Estimation )
(Image credit: Papersgraph)
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LineMOD
LineMOD
Occlusion LineMOD
Occlusion LineMOD
YCB-Video
YCB-Video
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Most implemented papers
PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes
D. Fox, Yu Xiang, Tanner Schmidt, V. Narayanan•Tue Oct 31 2017
This work introduces PoseCNN, a new Convolutional Neural Network for 6D object pose estimation, which is highly robust to occlusions, can handle symmetric objects, and provide accurate pose estimation using only color images as input.
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