Complementary spatial transformer network for real-time 3D object recognition

Tiny Deep Learning Models offer many advantages in various applications. From the perspective of statistical machine learning theory the contributions of this paper is to complement the research advances and results obtained so far in real-time 3D object recognition. We propose a Tiny Deep Learning Model named Complementary Spatial Transformer Network (CSTN) for Real-Time 3D object recognition. It turns out that CSTN's working, and analysis are much simplified in a target space setting. We make algorithmic enhancements to perform CSTN computations faster and keep the learning part of CSTN in minimal size. Finally, we provide the experimental verifications of the results obtained in publicly available point cloud data sets ModelNet40 and ShapeNetCore with our model performing 1.65-2 times better in DPS (Detections/s) rate on GPU hardware for 3D object recognition, when compared to state-of-the-art networks. Complementary Spatial Transformer Network architecture requires only 10-35% of trainable parameters, when compared to state-of-the-art networks, making the network easier to deploy in edge AI devices.