  In this study, we present an analysis of model-based ensemble learning for 3D
point-cloud object classification and detection. An ensemble of multiple model
instances is known to outperform a single model instance, but there is little
study of the topic of ensemble learning for 3D point clouds. First, an ensemble
of multiple model instances trained on the same part of the
$\textit{ModelNet40}$ dataset was tested for seven deep learning, point
cloud-based classification algorithms: $\textit{PointNet}$,
$\textit{PointNet++}$, $\textit{SO-Net}$, $\textit{KCNet}$,
$\textit{DeepSets}$, $\textit{DGCNN}$, and $\textit{PointCNN}$. Second, the
ensemble of different architectures was tested. Results of our experiments show
that the tested ensemble learning methods improve over state-of-the-art on the
$\textit{ModelNet40}$ dataset, from $92.65\%$ to $93.64\%$ for the ensemble of
single architecture instances, $94.03\%$ for two different architectures, and
$94.15\%$ for five different architectures. We show that the ensemble of two
models with different architectures can be as effective as the ensemble of 10
models with the same architecture. Third, a study on classic bagging i.e. with
different subsets used for training multiple model instances) was tested and
sources of ensemble accuracy growth were investigated for best-performing
architecture, i.e. $\textit{SO-Net}$. We also investigate the ensemble learning
of $\textit{Frustum PointNet}$ approach in the task of 3D object detection,
increasing the average precision of 3D box detection on the $\textit{KITTI}$
dataset from $63.1\%$ to $66.5\%$ using only three model instances. We measure
the inference time of all 3D classification architectures on a $\textit{Nvidia
Jetson TX2}$, a common embedded computer for mobile robots, to allude to the
use of these models in real-life applications.
