  LiDAR-generated point clouds are crucial for perceiving outdoor environments.
The segmentation of point clouds is also essential for many applications.
Previous research has focused on using self-attention and convolution (local
attention) mechanisms individually in semantic segmentation architectures.
However, there is limited work on combining the learned representations of
these attention mechanisms to improve performance. Additionally, existing
research that combines convolution with self-attention relies on global
attention, which is not practical for processing large point clouds. To address
these challenges, this study proposes a new architecture, pCTFusion, which
combines kernel-based convolutions and self-attention mechanisms for better
feature learning and capturing local and global dependencies in segmentation.
The proposed architecture employs two types of self-attention mechanisms, local
and global, based on the hierarchical positions of the encoder blocks.
Furthermore, the existing loss functions do not consider the semantic and
position-wise importance of the points, resulting in reduced accuracy,
particularly at sharp class boundaries. To overcome this, the study models a
novel attention-based loss function called Pointwise Geometric Anisotropy
(PGA), which assigns weights based on the semantic distribution of points in a
neighborhood. The proposed architecture is evaluated on SemanticKITTI outdoor
dataset and showed a 5-7% improvement in performance compared to the
state-of-the-art architectures. The results are particularly encouraging for
minor classes, often misclassified due to class imbalance, lack of space, and
neighbor-aware feature encoding. These developed methods can be leveraged for
the segmentation of complex datasets and can drive real-world applications of
LiDAR point cloud.
