意味的に有益な視点選択
Semantically Meaningful View Selection
物体認識の性能を高めるため、カメラ視点を意味的な情報量に基づいて選ぶ問題を定式化し、クラスタリングによる解法と約1万枚の画像データセットを提案した論文。
著者: Joris Guérin, Olivier Gibaru, Eric Nyiri, Stéphane Thiery, Byron Boots
分類: cs.RO, cs.AI, cs.CV
原文アブストラクト
An understanding of the nature of objects could help robots to solve both high-level abstract tasks and improve performance at lower-level concrete tasks. Although deep learning has facilitated progress in image understanding, a robot's performance in problems like object recognition often depends on the angle from which the object is observed. Traditionally, robot sorting tasks rely on a fixed top-down view of an object. By changing its viewing angle, a robot can select a more semantically informative view leading to better performance for object recognition. In this paper, we introduce the problem of semantic view selection, which seeks to find good camera poses to gain semantic knowledge about an observed object. We propose a conceptual formulation of the problem, together with a solvable relaxation based on clustering. We then present a new image dataset consisting of around 10k images representing various views of 144 objects under different poses. Finally we use this dataset to propose a first solution to the problem by training a neural network to predict a "semantic score" from a top view image and camera pose. The views predicted to have higher scores are then shown to provide better clustering results than fixed top-down views.
