Soft-Masked Transformer for Point Cloud Processing With Skip Attention-Based Upsampling

Point cloud processing methods leverage local and global point features to cater to downstream tasks, yet they often overlook the task-level context inherent in point clouds during the encoding stage. We argue that integrating task-level information into the encoding stage significantly enhances performance. To that end, we propose SMTransformer which incorporates task-level information into a vector-based transformer by utilizing a soft mask generated from task-level queries and keys to learn the attention weights. Additionally, to facilitate effective communication between features from the encoding and decoding layers in high-level tasks such as segmentation, we introduce a skip-attention-based up-sampling block. This block dynamically fuses features from various resolution points across the encoding and decoding layers. To mitigate the increase in network parameters and training time resulting from the complexity of the aforementioned blocks, we propose a novel shared point position encoding strategy. This strategy allows various transformer blocks to share the same position information over the same resolution points, thereby reducing network parameters and training time without compromising accuracy. Experimental comparisons with existing methods on multiple datasets demonstrate the efficacy of SMTransformer and skip-attention-based up-sampling for semantic segmentation task. In particular, we achieve state-of-the-art semantic segmentation results of 73.9% mIoU on S3DIS Area 5 and 62.4% mIoU on SWAN dataset. Note to Practitioners—Point cloud processing underpins automation tasks such as robotic perception, navigation, and inspection, where accurate 3D understanding is essential. Existing methods often prioritize vision benchmarks while overlooking automation needs like efficiency on limited hardware and robustness in real-world environments. The proposed SMTransformer embeds task-level guidance into feature learning and employs skip-attention up-sampling to improve segmentation accuracy with practical efficiency. It is well-suited for robotic manipulation, autonomous driving, and inspection applications. Current limitations include reliance on GPUs and sensitivity to extreme density variations. Future work will target edge-device deployment and multi-task extensions.