Fine-Grained Semantic Information Preservation and Misclassification-Aware Loss for 3D Point Cloud

Encoder-Decoder structure is a popular choice in point cloud processing for dense multi-classification tasks, e.g., 3D semantic segmentation. Though existing techniques that follow this structure achieve high performance, they are known to suffer from fine-grained information loss, especially when the underlying networks are deep. To alleviate this, we propose a bilateral attention fusion module (BAFM) that leverages multi-resolution feature fusion to allow more effective fine-grained information flow in the network. Moreover, we also introduce a misclassification-aware loss (MAL) as a more potent alternative to the widely used cross-entropy (CE) loss for multi-classification tasks. MAL enables an explicit penalization of misclassification. Empirical experiments reveal that our method achieves state-of-the-art performance for several challenging datasets, such as 91.9% mAcc on ModelNet40, 88.4% OA and 87.0% mAcc on ScanObjectNN and 71.3% mIoU on S3DIS Area-5. Code and trained models will be available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/zouyanmei/BAFM-and-MAL.git</uri> .