Improving Viewpoint-Independent Object-Centric Representations through Active Viewpoint Selection

TL;DR

This paper introduces an active viewpoint selection method that predicts and chooses the most informative viewpoints to improve object-centric scene understanding, outperforming random strategies in segmentation and reconstruction tasks.

Contribution

The paper presents a novel active viewpoint selection strategy that predicts and selects viewpoints with the greatest information gain, enhancing multi-view object-centric learning.

Findings

Significantly improved segmentation accuracy

Enhanced 3D reconstruction quality

Accurate prediction of unseen viewpoints

Abstract

Given the complexities inherent in visual scenes, such as object occlusion, a comprehensive understanding often requires observation from multiple viewpoints. Existing multi-viewpoint object-centric learning methods typically employ random or sequential viewpoint selection strategies. While applicable across various scenes, these strategies may not always be ideal, as certain scenes could benefit more from specific viewpoints. To address this limitation, we propose a novel active viewpoint selection strategy. This strategy predicts images from unknown viewpoints based on information from observation images for each scene. It then compares the object-centric representations extracted from both viewpoints and selects the unknown viewpoint with the largest disparity, indicating the greatest gain in information, as the next observation viewpoint. Through experiments on various datasets, we demonstrate the effectiveness of our active viewpoint selection strategy, significantly enhancing segmentation and reconstruction performance compared to random viewpoint selection. Moreover, our method can accurately predict images from unknown viewpoints.