6D姿态估计从0单排——看论文的小鸡篇——Deep Learning of Local RGB-D Patches for 3D Object Detection and 6D Pose Estimation

we employ a convolutional auto-encoder that has been trained on a large collection of random local patches. We demonstrate that neural networks coupled with a local voting-based approach can be used to perform reliable 3D object detection and pose estimation under clutter and occlusion. To this end, we deeply learn descriptive features from local RGB-D patches and use them afterwards to create hypotheses in the 6D pose space.we employ a convolutional auto-encoder that has