HINT-3D: Human-in-the-Loop Interactive Test-Time Adaptation for 3D Segmentation

We present HINT-3D, a human-in-the-loop test-time
adaptation framework for 3D semantic segmentation. A few
corrective clicks are converted into region masks by a promptable
3D interface (PointSAM). These masks supervise stability-aware
updates to a pretrained backbone at inference. We persist the
updates so later scenes start from improved weights, enabling
cumulative learning. The wrapper is backbone-agnostic: it requires
only logits, a mask-to-index bridge, plus access to a small trainable
parameter set; we instantiate it on KPConv, RandLA-Net, and
Point Transformer v1. On S3DIS Area-5, HINT-3D delivers
strong effort-accuracy gains within a scene, consistent zero-click
improvements across scenes, and reduced Expected Calibration
Error (ECE), while maintaining responsiveness with head-only
updates and uncertainty-gated training. We report mIoU versus
saved masks, cross-scene transfer, ECE, latency, and class-specific
corrections on common indoor failure modes.