DPCAE: denoising point cloud auto-encoder for 6D object detection

6D pose estimation for robotic gripping is greatly affected by cluttering, rendering and occlusion. Unlike the mainstream method with RGB images which is troubled by rendering, our approach for 3D orientation estimation is based on a Denoising Point Cloud Auto-encoder (DPCAE) which can avoid the rendering problem and eliminate cluttering and occlusion. Independent of the real pose-annotated training data, the Auto-encoder uses the point cloud data generated by the random object coverage of each object surface in the simulated environment, with the ability to obtain an implicit representation of object orientation and remove outliers to restore the surface of the objects. Experiments on the LineMod dataset show that our proposed approach is superior to those that require similar model-based approaches and competes with state-of-the-art approaches with real pose-annotated images.