SCOPE: Scene-Contextualized Incremental Few-Shot 3D Segmentation

TL;DR

SCOPE (Scene-COntextualised Prototype Enrichment), a plug-and-play background-guided prototype enrichment framework that integrates with any prototype-based 3D segmentation method, is introduced.

Abstract

Incremental Few-Shot (IFS) segmentation aims to learn new categories over time from only a few annotations. Although widely studied in 2D, it remains underexplored for 3D point clouds. Existing methods suffer from catastrophic forgetting or fail to learn discriminative prototypes under sparse supervision, and often overlook a key cue: novel categories frequently appear as unlabelled background in base-training scenes. We introduce SCOPE (Scene-COntextualised Prototype Enrichment), a plug-and-play background-guided prototype enrichment framework that integrates with any prototype-based 3D segmentation method. After base training, a class-agnostic segmentation model extracts high-confidence pseudo-instances from background regions to build a prototype pool. When novel classes arrive with few labelled samples, relevant background prototypes are retrieved and fused with few-shot prototypes to form enriched representations without retraining the backbone or adding parameters. Experiments on ScanNet and S3DIS show that SCOPE achieves SOTA performance, improving novel-class IoU by up to 6.98% and 3.61%, and mean IoU by 2.25% and 1.70%, respectively, while maintaining low forgetting. Code is available https://github.com/Surrey-UP-Lab/SCOPE.

Figures (6)

• Figure 1: Compared with GW xu2023generalized, SCOPE improves performance on novel classes by enriching prototypes with background context, without additional computational overhead.
• Figure 2: Overview of SCOPE. The framework comprises three stages: (1) Base Training, where the encoder $\Phi$ is trained on labelled base data; (2) Scene Contextualisation, which extracts background regions to build an Instance Prototype Bank (IPB); and (3) Incremental Class Registration, where retrieved prototypes are fused with few-shot prototypes via attention to yield refined novel-class representations.
• Figure 3: Incremental performance on S3DIS and ScanNet for $t{=}0$ to $t{=}3$ under $K{=}5$ (left) and $K{=}1$ (right), respectively. Curves show the evolution of mIoU across incremental stages.
• Figure 4: Qualitative comparison with competing methods from $t{=}0$ to $t{=}3$. The colour palette (right) denotes semantic classes, and dotted separators indicate newly introduced classes at each incremental stage (top to bottom).
• Figure 5: Hyperparameter sensitivity on ScanNet in terms of N-IoU and HM, obtained by varying one parameter at a time while keeping the others fixed ($\tau=0.75$, $R=20$, $\lambda=0.5$).