  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.
