Unsupervised Classification of 3D Objects from 2D Views

Part of Advances in Neural Information Processing Systems 7 (NIPS 1994)

Bibtex Metadata Paper

Authors

Satoshi Suzuki, Hiroshi Ando

Abstract

This paper presents an unsupervised learning scheme for categorizing 3D objects from their 2D projected images. The scheme exploits an auto-associative network's ability to encode each view of a single object into a representation that indicates its view direction. We propose two models that employ different classification mechanisms; the first model selects an auto-associative network whose recovered view best matches the input view, and the second model is based on a modular architecture whose additional network classifies the views by splitting the input space nonlinearly. We demonstrate the effectiveness of the proposed classification models through simulations using 3D wire-frame objects.