DPANet: Position‐aware feature encoding and decoding for accurate large‐scale point cloud semantic segmentation

Abstract

Abstract Due to the scattered, unordered, and unstructured nature of point clouds, it is challenging to extract local features. Existing methods tend to design redundant and less‐discriminative spatial feature extraction methods in the encoder, while neglecting the utilisation of uneven distribution in the decoder. In this paper, the authors fully exploit the characteristics of the imbalanced distribution in point clouds and design our Position‐aware Encoder (PAE) module and Position‐aware Decoder (PAD) module. In the PAE module, the authors extract position relationships utilising both Cartesian coordinate system and polar coordinate system to enhance the distinction of features. In the PAD module, the authors recognise the inherent positional disparities between each point and its corresponding upsampled point, utilising these distinctions to enrich features and mitigate information loss. The authors conduct extensive experiments and compare the proposed DPANet with existing methods on two benchmarks S3DIS and Semantic3D. The experimental results demonstrate that the method outperforms the state‐of‐the‐art approaches.