Class-specific Anchoring Proposal for 3D Object Recognition in LIDAR and RGB Images

TL;DR

This paper introduces a class-specific anchoring strategy for 3D object detection in LIDAR and RGB images, significantly improving accuracy by tailoring anchors to object sizes and aspect ratios, and optimizing clustering for different object classes.

Contribution

The paper proposes a novel Class-specific Anchoring Proposal (CAP) method that enhances 3D object detection accuracy by using class-based anchor clustering and optimal cluster numbers.

Findings

Detection accuracy improved by up to 8.8% for pedestrians.

Clustering enhances regional proposal network performance.

Optimal cluster numbers significantly boost detection results.

Abstract

Detecting objects in a two-dimensional setting is often insufficient in the context of real-life applications where the surrounding environment needs to be accurately recognized and oriented in three-dimension (3D), such as in the case of autonomous driving vehicles. Therefore, accurately and efficiently detecting objects in the three-dimensional setting is becoming increasingly relevant to a wide range of industrial applications, and thus is progressively attracting the attention of researchers. Building systems to detect objects in 3D is a challenging task though, because it relies on the multi-modal fusion of data derived from different sources. In this paper, we study the effects of anchoring using the current state-of-the-art 3D object detector and propose Class-specific Anchoring Proposal (CAP) strategy based on object sizes and aspect ratios based clustering of anchors. The proposed anchoring strategy significantly increased detection accuracy's by 7.19%, 8.13% and 8.8% on Easy, Moderate and Hard setting of the pedestrian class, 2.19%, 2.17% and 1.27% on Easy, Moderate and Hard setting of the car class and 12.1% on Easy setting of cyclist class. We also show that the clustering in anchoring process also enhances the performance of the regional proposal network in proposing regions of interests significantly. Finally, we propose the best cluster numbers for each class of objects in KITTI dataset that improves the performance of detection model significantly.