  Point clouds are a basic data type that is increasingly of interest as 3D
content becomes more ubiquitous. Applications using point clouds include
virtual, augmented, and mixed reality and autonomous driving. We propose a more
efficient deep learning-based encoder architecture for point clouds compression
that incorporates principles from established 3D object detection and image
compression architectures. Through an ablation study, we show that
incorporating the learned activation function from Computational Efficient
Neural Image Compression (CENIC) and designing more parameter-efficient
convolutional blocks yields dramatic gains in efficiency and performance. Our
proposed architecture incorporates Generalized Divisive Normalization
activations and propose a spatially separable InceptionV4-inspired block. We
then evaluate rate-distortion curves on the standard JPEG Pleno 8i Voxelized
Full Bodies dataset to evaluate our model's performance. Our proposed
modifications outperform the baseline approaches by a small margin in terms of
Bjontegard delta rate and PSNR values, yet reduces necessary encoder
convolution operations by 8 percent and reduces total encoder parameters by 20
percent. Our proposed architecture, when considered on its own, has a small
penalty of 0.02 percent in Chamfer's Distance and 0.32 percent increased bit
rate in Point to Plane Distance for the same peak signal-to-noise ratio.
