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Detecting objects in a two-dimensional setting is often insufficient in thecontext of real-life applications where the surrounding environment needs to beaccurately recognized and oriented in three-dimension (3D), such as in the caseof autonomous driving vehicles. Therefore, accurately and efficiently detectingobjects in the three-dimensional setting is becoming increasingly relevant to awide range of industrial applications, and thus is progressively attracting theattention of researchers. Building systems to detect objects in 3D is achallenging task though, because it relies on the multi-modal fusion of dataderived from different sources. In this paper, we study the effects ofanchoring using the current state-of-the-art 3D object detector and proposeClass-specific Anchoring Proposal (CAP) strategy based on object sizes andaspect ratios based clustering of anchors. The proposed anchoring strategysignificantly 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% onEasy, Moderate and Hard setting of the car class and 12.1% on Easy setting ofcyclist class. We also show that the clustering in anchoring process alsoenhances the performance of the regional proposal network in proposing regionsof interests significantly. Finally, we propose the best cluster numbers foreach class of objects in KITTI dataset that improves the performance ofdetection model significantly.

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