The paper tests four 2D detectors (SAM, YOLOv8, RT-DETR, YOLO-World) as alternatives to SAM for generating bounding-box crops in OpenMask3D's CLIP feature aggregation pipeline, using ground-truth 3D mask proposals on the Replica dataset. YOLO-World achieves mAP 32.5 compared to SAM's 33.0, a gap of only 0.5 points, while reducing per-scene inference time from 675.6 s to 384.29 s. YOLOv8 and RT-DETR show larger accuracy drops (21.1 and 28.4 respectively), indicating that YOLO-World's open-vocabulary zero-shot detection quality is the key factor enabling it to match SAM.
Evidence
correlational
Key metric
mAP 32.5 (YOLO-World) vs 33.0 (SAM), time/scene 384.29 s vs 675.6 s on Replica with ground-truth 3D masks
Caveat
The comparison uses ground-truth 3D mask proposals, so the finding is about crop generation quality in isolation, not about the full end-to-end pipeline. The 0.5 mAP gap, while small, is not zero.