Learning Categorylevel Implicit 3D Rotation Representations for 6D Pose Estimation from RGB Images We exploit the embedding ability of a denoising autoencoder for an implicit 3D rotation representation learning at the category level. Contrast to the exact 3D reconstruction model of each instancelevel physical object, we leverage the inexact CADReconstruction models of an object as the representative model for some category. Under our assumptions that objects within the same category share resemble geometry, we train a denoising autoencoder on synthetic 3D views of categorylevel objects to extract the homogenous features at the bottleneck layer. The latent representation is agnostic not only to heterogeneous textures, colors, and illuminations, but also ambiguous pose caused by object symmetry. To extend the instancelevel 3D translation estimation to the category level, we considered the 3D diagonal length ratio between the source and target object. We achieved a frame rate of 17Hz.

Asymmetric Supervised Deep Autoencoder for Depth Image based 3D Model Retrieval In this paper, we propose a new asymmetric supervised deep autoencoder approach to retrieve 3D shapes based on depth images. The asymmetric supervised autoencoder is trained with real and synthetic depth images together. The novelty of this research lies in the asymmetric structure of a supervised deep autoencoder. The proposed asymmetric deep supervised autoencoder deals with the incompleteness and ambiguity present in the depth images by balancing reconstruction and classification capabilities in a unified way with mixed depth images. We investigate the relationship between the encoder layers and decoder layers, and claim that an asymmetric structure of a supervised deep autoencoder reduces the chance of overfitting by 8% and is capable of extracting more robust features with respect to the variance of input than that of a symmetric structure. The experimental results on the NYUD2 and ModelNet10 datasets demonstrate that the proposed supervised method outperforms the recent approaches for cross modal 3D model retrieval.