Matching objects across the textured-smooth continuum

TL;DR

This paper presents a novel method for 3D object recognition that combines texture and shape features, using a learned weighted fusion to improve matching accuracy across viewpoints.

Contribution

It introduces a holistic approach that jointly uses textural and shape features with learned fusion, outperforming previous methods on diverse object datasets.

Findings

Significant improvement over purely textural methods.

Outperforms shape-only approaches in diverse conditions.

Effective fusion of modalities enhances recognition accuracy.

Abstract

The problem of 3D object recognition is of immense practical importance, with the last decade witnessing a number of breakthroughs in the state of the art. Most of the previous work has focused on the matching of textured objects using local appearance descriptors extracted around salient image points. The recently proposed bag of boundaries method was the first to address directly the problem of matching smooth objects using boundary features. However, no previous work has attempted to achieve a holistic treatment of the problem by jointly using textural and shape features which is what we describe herein. Due to the complementarity of the two modalities, we fuse the corresponding matching scores and learn their relative weighting in a data specific manner by optimizing discriminative performance on synthetically distorted data. For the textural description of an object we adopt a representation in the form of a histogram of SIFT based visual words. Similarly the apparent shape of an object is represented by a histogram of discretized features capturing local shape. On a large public database of a diverse set of objects, the proposed method is shown to outperform significantly both purely textural and purely shape based approaches for matching across viewpoint variation.