# Depth-wise Selective Scan Aggregation

Inside the unified OneFormer3D query decoder, regional feature aggregation previously relied on fixed statistics such as mean or max; COC replaces that step with a learned positive scalar weight per sample, adds contracted first- and second-order geometric descriptors, and evolves states bidirectionally along decoder depth. On ScanNetV2 instance mAP rises from 59.3 to 59.8, semantic mIoU from 76.6 to 77.4 and panoptic PQ from 71.2 to 72.0; on S3DIS Area-5 mIoU rises from 72.4 to 73.8. Gains vary by dataset and metric, and the authors note cross-paper numbers are reference points only.

Inside the unified OneFormer3D query decoder, regional feature aggregation previously relied on fixed statistics such as mean or max; COC replaces that step with a learned positive scalar weight per sample, adds contracted first- and second-order geometric descriptors, and evolves states bidirectionally along decoder depth. On ScanNetV2 instance mAP rises from 59.3 to 59.8, semantic mIoU from 76.6 to 77.4 and panoptic PQ from 71.2 to 72.0; on S3DIS Area-5 mIoU rises from 72.4 to 73.8. Gains vary by dataset and metric, and the authors note cross-paper numbers are reference points only.