Glissando-Net: Deep sinGLe vIew category level poSe eStimation ANd 3D recOnstruction

TL;DR

Glissando-Net is a deep learning model that jointly estimates object pose and reconstructs 3D shape from a single RGB image, integrating 2D-3D features for improved accuracy.

Contribution

The paper introduces Glissando-Net, a novel joint auto-encoder framework that simultaneously predicts 3D shape and pose from RGB images, enhancing prior methods that focused on either task alone.

Findings

Outperforms existing methods in pose and shape estimation accuracy

Effectively integrates 2D-3D features for improved predictions

Eliminates the need for code optimization during inference

Abstract

We present a deep learning model, dubbed Glissando-Net, to simultaneously estimate the pose and reconstruct the 3D shape of objects at the category level from a single RGB image. Previous works predominantly focused on either estimating poses(often at the instance level), or reconstructing shapes, but not both. Glissando-Net is composed of two auto-encoders that are jointly trained, one for RGB images and the other for point clouds. We embrace two key design choices in Glissando-Net to achieve a more accurate prediction of the 3D shape and pose of the object given a single RGB image as input. First, we augment the feature maps of the point cloud encoder and decoder with transformed feature maps from the image decoder, enabling effective 2D-3D interaction in both training and prediction. Second, we predict both the 3D shape and pose of the object in the decoder stage. This way, we better utilize the information in the 3D point clouds presented only in the training stage to train the network for more accurate prediction. We jointly train the two encoder-decoders for RGB and point cloud data to learn how to pass latent features to the point cloud decoder during inference. In testing, the encoder of the 3D point cloud is discarded. The design of Glissando-Net is inspired by codeSLAM. Unlike codeSLAM, which targets 3D reconstruction of scenes, we focus on pose estimation and shape reconstruction of objects, and directly predict the object pose and a pose invariant 3D reconstruction without the need of the code optimization step. Extensive experiments, involving both ablation studies and comparison with competing methods, demonstrate the efficacy of our proposed method, and compare favorably with the state-of-the-art.