MORE: Simultaneous Multi-View 3D Object Recognition and Pose Estimation

TL;DR

This paper introduces a deep learning approach that simultaneously recognizes 3D objects and estimates their pose from multiple views, improving robotic interaction capabilities in real-world scenarios.

Contribution

It presents a novel method combining view selection and multi-task learning for concurrent object recognition and pose estimation.

Findings

Achieved high accuracy in object recognition and pose estimation.

Demonstrated effectiveness in a real-life robotic scenario.

Developed a view prediction model for optimal multi-view inputs.

Abstract

Simultaneous object recognition and pose estimation are two key functionalities for robots to safely interact with humans as well as environments. Although both object recognition and pose estimation use visual input, most state-of-the-art tackles them as two separate problems since the former needs a view-invariant representation while object pose estimation necessitates a view-dependent description. Nowadays, multi-view Convolutional Neural Network (MVCNN) approaches show state-of-the-art classification performance. Although MVCNN object recognition has been widely explored, there has been very little research on multi-view object pose estimation methods, and even less on addressing these two problems simultaneously. The pose of virtual cameras in MVCNN methods is often predefined in advance, leading to bound the application of such approaches. In this paper, we propose an approach capable of handling object recognition and pose estimation simultaneously. In particular, we develop a deep object-agnostic entropy estimation model, capable of predicting the best viewpoints of a given 3D object. The obtained views of the object are then fed to the network to simultaneously predict the pose and category label of the target object. Experimental results showed that the views obtained from such positions are descriptive enough to achieve a good accuracy score. Furthermore, we designed a real-life serve drink scenario to demonstrate how well the proposed approach worked in real robot tasks. Code is available online at: github.com/SubhadityaMukherjee/more_mvcnn