Generalized few-shot 3D point cloud segmentation aims to adapt to novel classes from only a few annotations while maintaining strong performance on base classes, but this remains challenging due to the inherent stability-plasticity trade-off: adapting to novel classes can interfere with shared representations and cause base-class forgetting. We present HOP3D, a unified framework that learns hierarchical orthogonal prototypes with an entropy-based few-shot regularizer to enable robust novel-class adaptation without degrading base-class performance. HOP3D introduces hierarchical orthogonalization that decouples base and novel learning at both the gradient and representation levels, effectively mitigating base-novel interference. To further enhance adaptation under sparse supervision, we incorporate an entropy-based regularizer that leverages predictive uncertainty to refine prototype learning and promote balanced predictions. Extensive experiments on ScanNet200 and ScanNet++ demonstrate that HOP3D consistently outperforms state-of-the-art baselines under both 1-shot and 5-shot settings. The code is available at https://fdueblab-hop3d.github.io/.
Figures & tables
Fig. 1: Overview of the proposed HOP3D framework, integrating HOP-Net and HOP-Ent. The training pipeline consists of two phases: Phase 1 trains base classes with HOP-Rep and collects base-task gradients G to construct an orthonormal basis B for HOP-Grad ; Phase 2 introduces novel classes for few-shot adaptation, applies HOP-Grad to project each novel gradient g as g~ (removing B ), continues to use HOP-Rep for prototype subspace orthogonalization, and integrates HOP-Ent for entropy-guided refinement. (a) Gradient orthogonalization in HOP-Grad using B and g~ ; (b) Prototype similarity heatmaps and orthogonal prototype subspaces before/after HOP-Rep ; (c) Confidence distribution and class-frequency distribution before/after HOP-Ent .
Method
ScanNet200
ScanNet++
5-shot
1-shot
5-shot
1-shot
B
N
A
HM
B
N
A
HM
B
N
A
HM
B
N
A
HM
Fully Sup.
68.70
39.32
45.51
50.02
68.70
39.32
45.51
50.02
65.45
37.24
48.53
47.47
65.45
37.24
48.53
47.47
PIFS [ 5 ]
28.78
3.82
9.07
6.71
17.84
2.87
6.02
4.88
39.98
5.74
19.44
10.03
36.66
4.95
17.63
8.71
attMPTI [ 31 ]
37.13
4.99
11.76
8.79
54.84
3.28
14.14
6.17
55.89
4.19
24.87
7.78
53.16
3.55
23.40
6.66
COSeg [ 1 ]
57.67
5.21
16.25
9.54
47.03
4.03
13.09
7.42
59.34
6.96
27.91
12.45
58.49
6.24
27.14
11.26
TABLE I: Performance comparison on ScanNet200 and ScanNet++. Results under 1-shot and 5-shot settings.
Fig. 2: Qualitative comparison between GFS-VL and our HOP3D on ScanNet200. Class color legend is shown at the top. From left to right: query input, ground-truth labels, GFS-VL prediction, and HOP3D prediction.
HOP-Net
HOP-Ent
mIoU-B
mIoU-N
mIoU-A
HM
67.64
28.80
36.98
40.39
✓ †
68.67
29.46
37.71
41.23
✓ ‡
68.79
29.19
37.53
40.98
✓
69.30
30.52
38.69
42.37
✓
✓ §
67.11
30.79
38.43
42.20
✓
✓
68.45
31.80
39.52
43.42
TABLE II: Ablation on ScanNet200 (1-shot) for HOP-Net and HOP-Ent.
Fig. 3: Qualitative results illustrating the effect of HOP-Net and HOP-Ent. From left to right: query, ground truth, prediction without HOP-Net/HOP-Ent, and HOP3D. Circles highlight corrected base–novel misclassifications.
Fig. 4: HOP-Net ablation. (a)–(d) Impact of λorth . (e) Effect of AR. Shaded areas denote 95% confidence intervals.
Fig. 6: HOP-Ent analysis. (a) Confidence distribution, where higher mean confidence indicates better prediction certainty. (b) Class frequency distribution, where a lower coefficient of variation indicates better class balance. HOP-Ent improves both prediction certainty and class balance.
3D point cloud segmentation assigns semantic, instance, or part labels to points for scene understanding, mapping, robotics, and scientific measurement.
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.
Vishal Thengane, Zhaochong An, Tianjin Huang +5
University of Surrey, UK · University of Wollongong, Australia · University of Copenhagen, Denmark +2
In generalized few-shot 3D point cloud segmentation, support annotations are often compromised by sparsity and occlusion. Blindly adapting to such low quality samples risks learning spurious geometric patterns rather than robust novel concepts. Although 3D Vision-Language Models have been introduced to sup plement supervision, the potential of uncertainty to actively govern adaptation dynamics remains underexplored. Existing signal-level methods like loss reweight ing fail to prevent unreliable episodes from steering the optimization trajectory. To address this, we propose Uncertainty-Gated Adaptive (UGA), to the best of our knowledge the first framework that elevates uncertainty to update-level gating for generalized few-shot 3D point cloud segmentation. The core mod ule, Uncertainty-Gated Update (UG-Update), estimates episodic reliability via intra-class dispersion over merged support and pseudo-labeled novel supervision, and selectively gates residual gradient updates. This encourages the model to learn novel semantics from reliable episodes while attenuating updates during unreliable ones. Complementing this, we introduce an Uncertainty-Based Pseudo Label Filter (UB-Filter) to enforce learning from high-confidence dense signals. On ScanNet200 and ScanNet++ (1-/5-shot), UGA sets new state of the art with an average +3.08 HM IoU over the strongest prior baseline.
Yifei Zhao, Fanyu Zhao, Shengtang Wu +3
Fudan University · University of Science and Technology of China
Few-shot 3D semantic segmentation aims to generate accurate semantic masks for query point clouds with only a few annotated support examples. Existing prototype-based methods typically construct compact and deterministic prototypes from the support set to guide query segmentation. However, such rigid representations are unable to capture the intrinsic uncertainty introduced by scarce supervision, which often results in degraded robustness and limited generalization. In this work, we propose UPL (Uncertainty-aware Prototype Learning), a probabilistic approach designed to incorporate uncertainty modeling into prototype learning for few-shot 3D segmentation. Our framework introduces two key components. First, UPL introduces a dual-stream prototype refinement module that enriches prototype representations by jointly leveraging limited information from both support and query samples. Second, we formulate prototype learning as a variational inference problem, regarding class prototypes as latent variables. This probabilistic formulation enables explicit uncertainty modeling, providing robust and interpretable mask predictions. Extensive experiments on the widely used ScanNet and S3DIS benchmarks show that our UPL achieves consistent state-of-the-art performance under different settings while providing reliable uncertainty estimation. The code is available at https://fdueblab-upl.github.io/.