RISAS: A Novel Rotation, Illumination, Scale Invariant Appearance and Shape Feature

TL;DR

RISAS introduces a new RGB-D feature that is robust to viewpoint, illumination, scale, and rotation changes, combining texture and geometric information for improved keypoint detection and description.

Contribution

The paper presents RISAS, a novel appearance and shape feature with a combined keypoint detector and descriptor leveraging surface normals and depth for invariance.

Findings

RISAS outperforms CSHOT and LOIND in robustness and accuracy.

Incorporating texture and shape improves feature performance.

RISAS enhances existing descriptors when combined with its detector.

Abstract

This paper presents a novel appearance and shape feature, RISAS, which is robust to viewpoint, illumination, scale and rotation variations. RISAS consists of a keypoint detector and a feature descriptor both of which utilise texture and geometric information present in the appearance and shape channels. A novel response function based on the surface normals is used in combination with the Harris corner detector for selecting keypoints in the scene. A strategy that uses the depth information for scale estimation and background elimination is proposed to select the neighbourhood around the keypoints in order to build precise invariant descriptors. Proposed descriptor relies on the ordering of both grayscale intensity and shape information in the neighbourhood. Comprehensive experiments which confirm the effectiveness of the proposed RGB-D feature when compared with CSHOT and LOIND are presented. Furthermore, we highlight the utility of incorporating texture and shape information in the design of both the detector and the descriptor by demonstrating the enhanced performance of CSHOT and LOIND when combined with RISAS detector.