Point-UQ: An Uncertainty-Quantification Paradigm for Point Cloud Few-Shot Class Incremental Learning
Authors: Xiangqi Li
Libo Huang
Jiarui Zhao
Weilun Feng
Chuanguang Yang
Zhulin An
Yongjun Xu
Venue: ICLR 2026 Poster (2026)
Keywords: 3D point cloud processing
few-shot learning
class-incremental learning
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: 3D few-shot class-incremental learning (3D FSCIL) requires effectively integrating novel classes from limited samples while preserving base-class knowledge, without succumbing to catastrophic forgetting the learned knowledge or overfitting the novel ones. 
Current 3D FSCIL approaches predominantly focus on fine-tuning feature representations yet retain static decision boundaries. 
This leads to a critical trade-off: excessive adaptation to new samples tends to erase previously learned knowledge, while insufficient adaptation hinders novel-class recognition. 
We argue that the key to effective incremental learning lies not only in feature enhancement but also in adaptive decision-making. 
To this end, we introduce **Point-UQ**, an incremental training-free paradigm for 3D **point** clouds based on **u**ncertainty **q**uantification, which shifts the focus from feature tuning to dynamic decision optimization. 
Point-UQ comprises two co-designed modules: *Attention-driven Adaptive Enhancement (AAE)* and *Uncertainty-quantification Decision Decoupling (UDD)*.
The former module fuses multi-scale features into calibrated representations, where prediction entropy serves as a reliable measure of per-sample epistemic uncertainty while preserving original feature semantics. Building on AAE-derived calibrated entropy, the UDD module dynamically arbitrates between semantic classifiers and geometric prototypes—enabling robust base-class knowledge retention and accurate novel-class recognition in 3D FSCIL without retraining. 
Extensive experiments on ModelNet, ShapeNet, ScanObjectNN, and CO3D demonstrate that our approach outperforms state-of-the-art methods by 4% in average accuracy, setting a new standard for robust 3D incremental learning.

Published as a conference paper at ICLR 2026
POINT-UQ: AN UNCERTAINNT--QUANIICCCTTO PARADIGM FOR POINT CLOUD FEW- SHOT CLASS INCREMENTAL LEARNING
Xiangqi Li Libo Huang"", Jiarui Zhao", Weilun Feng!?, Chuanguang Yang',
Zhulin An Yongjun Xu
State Key Laboratory of Al Safety, Institute of Computing Technology,
Chinese Academy of Sciences
2 University of Chinese Academy of Sciences
ABSTRACT
3D few-shot class-incremental learning (3D FSCIL) requires effectively integrating novel classes from limited samples while preserving base-class knowledge, without succumbing to catastrophic forgetting the learned knowledge or overfitting the novel ones. Current 3D FSCIL approaches predominantly focus on fine-tuning feature representations yet retain static decision boundaries. This leads to critical trade-off: excessive adaptation to new samples tends to erase previously learned knowledge, while insufficient adaptation hinders novel-class recognition. We argue that the key to effective incremental learning lies not only in feature enhance- ment but also in adaptive decision-making making. To this end, we introduce Point-UQ. an incremental training-ffee frreinngg-eeeeeeee free paradigm for 3D point clouds based on uncertainty quantification, which shifts the focus from feature tuning dynamic decision optimization. Point-UQ comprises two co-designed modules: Atlention-driven Adaptive Enhancement (AAE) and pntrertiint-quannffiteee (UDD). The former module fuses multi-scale features into calibrated representa- tions, where prediction entropy serves a reliable measure of per-sample epistemic uncertainty while preserving original feature semantics. Building on AAE-derived calibrated entropy, the UDD module dynamically arbitrates between semantic clas- sifiers and geometric probblypes-eeabling robust base-class knowledge retention and accurate novel-class recognition in 3D FSCIL without retraining. Extensive experiments ModelNet, ShapeNet, ScanObjectNN, and CO3D demonstrate that our approach outperforms state-of state-of-the-an the-art methods by 4% in average accuracy, setting new standard for robust 3D incremental learning.
Decision Decoupling
INTRODUCTION
3D point clouds serve as the critical data sources for robotic navigation, augmented reality, and autonomous driving (Resani et al., 2025 Miao et al. 2024 Sun et al. 2024b) An al. (0256). Although the rapid development of 3D point cloud ana analysis techniques, how to use these data in the open-world environment is still stubbed (Xiao et al. 2024). One typical scenario is 3D few-shot class incremental learning (FSCIL), where point cloud models must first train on abundant synthetic base-class data, then incrementally adapt using minimal real-scanned samples per novel class (Li et al. 2025) Ahmadi et al.) 2024 Tan & Xiang 2024). This dual challenge induces catastrophic forgetting of base knowledge, insufficient discriminative power for novel classes, and cross-doman distribution shifts between synthetic base and open- world incremental data (Chowdhury et al. 2022
Cheraghian et al. 2025).
To address these challenges, Microshape (Chowdhury et al. resentation to bridge domain gaps, offering a foundational framework for multi-task compatibility.
2022) pioneered unified feature rep-
"This work was supported by the National Natural Science Foundation of China (No. (No.62472264 and 1o.62406312). Corresponding authors: Libo Huang (huangliho@ict.eec and Zhulin An (anzhulin ..c.aa......................

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Published as a conference paper at ICLR 2026
Building this, subsequent studies enhanced category separation through label replay strategies, demonstrating improved stability in sequential learning scenarios (Tan & Xiang 2024). Recent ad- vancements have explored multimodal alignment techniques, exemplified by C3PR (Cheraghian et al.) 2025), which mapped 3D point clouds to 2D depth images to harness CLIP's cross-modal knowledge. While achieving promising semantic transfer, this projection-basee inherent 3D geometric fidelity (Wang et al. 2022). In a parallel development, the FoundationModel approach directly leverages large-scale 3D vision-language models, aiming strengthen semantic representation through multimodal grounding (Ahmadi et al. 2024
paradigm inevitably sacrifices
Despite their differences, a common thread among these methods is the heavy reliance on meticulous feature fine-tuning strategies to enhance feature discriminability, while retain- ing static decision boundaries throughout incre- mental stages (as illustrated in Fig a)). This paradigm faces a fundamental dilemma: insuf- ficient fine-tuning weakens the model's abil- ity to discriminate novel classes and exacer- bates overfitting to base classes, while aggres- sive fine-tuning causes overfitting to the limited novel samples and accelerates catastrophic for- getting (Lahoud et al.) 2022) Tang et al. (024)) Moreover, repeated tine-tuning incremental phases not only raises training costs but also in- creases the risk of forgetting previously learned knowledge. Notably, current approaches focus predominantly on refining feature representa- tions through fine-tuning, while overlooking the potential of improving the rocision-making pro- cess itself.
Dicriednatic Fecaters Learning
+x
Dudikk
Feature Tuning
Dioriesiaathe Fastaro Leandee
180 Model
Key
Deciston Optimization
Traknin
Fregritigg Rilk
Figure 1: Motivation of Point-UQ: shift the focus from feature tuning to decision optimization. (a) Traditional paradigm relies heavily on discrimi- native feature learning via continuous fine-tuning. leads to high training overhead and escalating risk of model catastrophic forgetting as incremen- tal sessions proceed. Its static decision mechanism limits adaptation to new classes without disrupting previously learned knowledge. (b) Point-UQ fo- cuses on optimizing the decision process based on existing features. By introducing the Uncertainty- quantification Decision Decoupling (UDD) mod- ule, the model eliminates the need for fine-tuning throughout incremental learning- coannin--iinnficanntii re- ducing computational cost and avoiding complex- ity growth. The UDD module dynamically guides the decision-making process based on uncertainty quantification, enabling flexible yet robust adapta- tion to novel classes without retraining.
We argue that effective incremental learning de- pends not only on discriminative features but also on flexible decision pecision-makkin.. This motivates shift in perspective: rather than continually chasing feature refinement through repetitive fine-tuning, a more efficient path lies in dynam- ically allocating existing knowledge via adap- tive decision logic to balance base-class reten- tion and novel-class adaptation, especially when feature expressive power is inherently bounded. Specifically, we demonstrate that uncertainty quantification provides a powerful basis for de-
signing dynamic reasoning paths, which markedly improve adaptability and robustness in incremental scenarios with minimal parameter overhead. As illustrated in Fig. (bb. this decision-centre strategy supports efficient learning without repeated fine-tuning, thereby avoiding the high training costs and forgetting risks inherent in conventional methods.
We instantiate this perspective with Point-UQ, novel point cloud decision optimization frame- work based on Attention-driven Adaptive Enhancement (AAE) module first integrates multi-scale features during base training to produce calibrated representations. Crucially, it leverages prediction entropy as reliable estimate of per-sample epistemic uncertainty, thereby preserving original semantic informa- tion while supplying essential uncertainty cues for subsequent decision stages. Building on these uncertainty signals, the
It consists of two synergistically designed modules: The
-uwaaiittttuaaiiiaationn Decision Decoupling (UDD) module dynamically arbitrates between semantic classifiers and geometric prototypes. For high-uncertainty samples, UDD shifts reliance toward more robust geometric similarities, while -wwuucceraintty predictions retain semantic-based decisions. This mechanism enables the model to retain knowledge of old classes and accurately recognize new class samples without the need for retraining. Extensive experiments on ModelNet, ShapeNet, ScanObjectNN, and CO3D demonstrate Point-UQ's superiority, with con-
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Published as a conference paper at ICLR 2026
sistent improvements in average accuracy, harmonic mean, and forgetting rate state-of of-the-art
baselines (Wu et al.) 2013] Chang et al. Uy et al. Reizenstin et al., 2021: Chowdhury
et al. 2022 Tan & Xiang 2024| Cheraghian et al. 2025 Ahmadi et al. 2024). In summary. our work
makes the following key contributions:
1. We propose the Attention-driven Adaptive Enhanced Module, which integrates multi-scale features for calibrated representations, emphasizes cross-scale discriminative patterns, and leverages prediction entropy as reliable epistemic uncertainty. This supplies essential cues for subsequent decision stages, laying the groundwork for adaptive decision-making,
2. We design the entropy-driver adaptive weighting using AAE-derived entropy. This strategy balances semantic classification and geometric prototype matching: prioritizing geometric cues for uncertain high-entropy samples while maintaining semantic boundaries for confident predictions, resolving old-new class conflicts.
Decision Decoupling Module, which introduces
3. We establish Point-UQ by synergistically integrating AAE and UDD an incremental training-free free 3D FSCIL paradigm. It reframes the problem by shifting focus from feature tuning to decision optimization, effectively balancing base class knowledge retention with rapid novel class adaptation. Extensive experiments on multiple 3D point cloud benchmarks show consistently outperforms existing approaches across various evaluation metrics.
RELATED WORK
2.1 CONTINUAL LEARNING ON POINT CLOUD
Early research in point cloud processing laid the foundation by adapting 2D vision principles
to the 3D domain (Li et al. 2025 Qi et al., 201 Wang et al. 2019, Zhao et al.) 2021: Xu
et al. 2018 Thomas et al.) 2019 Huang et al., 2024a). For instance, PointNet (Qi et al. 201 7a)
introduced permutation-inett invariant feature learning through multi-layer perceptrons and global max- pooling. while PointNet++ (Qi et al. 2017b) improved local geometric modeling via hierarchical point sampling and grouping. With advancements in 3D modeling techniques, increasing attention has shifted to more complex tasks such as few-shot segmentation (An et al.] [0224b (225a)) and continual learning in 3D vision (Resani & Nasihatkon) 2024). Chowdhury et al. (Chowdhury et al. 202T incorporated knowledge distillation with semantic word embeddings to alleviate catastrophic forgetting. The geometric attention mechanism in 13DOL (Dong et al. (2221) and fairness constraints in InOR-Net (Dong et (2023) enhanced structural preservation. CL3D (Resani al., 2025) and RCR (Zamorski et 2023) further improved performance through spectral clustering and memory- efficient compression strategies. MIRACLE3D (Resani & Nasihatkon, 2024) constructs compact shape prototypes for each class, capturing both average structures and key variations, significantly reducing memory overhead without storing raw point clouds. These methods collectively address the core challenges of geometric structure preservation, memory efficiency, and incremental adaptability in continual learning on point clouds.
2.2 3D FEW-SHOT CLASS INCREMENTAL LEARNING
While multimodal learning, particularly involving images and point clouds, has driven significant
progress in 3D vision (Li al. 2025 Yang et al. 2022b 2024 Feng et al. 2025 Huang et al. 2024b
Feng et al. 2024), applying these advances continuous learning scenarios remains challenging.
Specifically. 3D few-shot class incremental learning extends the framework of traditional FSCIL
to the 3D domain, intending to incrementally recognize new object categories from scarce labeled point cloud samples while effectively retaining knowledge of previously learned classes (Tao et al.
2020 Zhang al. 2021 Yang et al. [022a]] Liu al.) 2024 Tian et al.) 2024, Wu et al. 2025). While early studies in 2D vision ex explored strategies such as backbone fine-tuning (Ahmad et al.)
2022, Liu et al. 2022 Kim et al. 2023
Liu et al.) [0033] Lin et al., 2024), meta-learning-basee
prototype optimization (Chi et al.
2022 Hersche et al.] 2022 Zhou et al. (2022b), and dynamic
architecture adaptation (Zhang et al. 2022 Yang et al. 2022a Zhao et al.] (022), recent advances
have shifted toward parameter-efffiient fine-tuning and multi-men modal generalization. These include
freezing pre-trained backbones with lightweight updates (Liu et al. 2024 Wang et al., 2024) and
exploiting textual guidance for better generalization under data scarcity (Park et al. 2024 Sun et al.
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Input
Unceraiit--quaatffcctttnn Decision Decoupling
Feature
Baa Semlen
poitt made
far. Seesien
Iar. Semsin
Enceder
Weight
Text Prompt
Lower Weight
Atteation-driven Adaptive Enhunced
High Entropy
Alle
Low Entropy
Righer Waight
Point Cloud Feature
Lower Weight
Transformer
Point Cloud Encoder
Encoder
Class Prototype
Figure 2: Overall architecture of Point-UQ. In the base-class training phase, the Attention-driven Adaptive Enhancement module integrates multi-scale features into calibrated representations via learnable multi-head self-attention, fusing shallow geometric details with deep semantic features. It simultaneously generates prediction entropy as a reliable metric of per-sample epistemic uncertainty, preserving semantic information while providing critical cues for subsequent decisions. During incremental learning, the Uncerrainty-quanificaatin Decision Decoupling module builds on AAE's outputs: using the precomputed entropy quantify uncertainty, it dynamically balances weights between semantic classification and geometric prototype matching. For high-entropy ambiguous samples, it prioritizes geometric prototype matching leverage base-class geometric priors and compensate for scarce novel samples. For low-entropy clear samples, it emphasizes semantic classifiers reuse stable base-class decision boundaries. Point-UQ synergy enables robust knowledge retention and accurate novel-class recognition without retraining.
2024a) When applied to point cloud analysis, FSCIL introduces two distinct challenges: cross- domain distribution shifts and geometric detail degradation (Chowdhury et al. (022). To address geometric descriptors that effectively bridge synthetic-to-res to-real domain gaps. Building upon this idea, Cross-Domain (Tan & Xiang 2024) further improves generalization by introducing dual-branch architectures that separately model distributions of old and new classes, enabling clearer decision boundaries during incremental updates. addition to domain adaptation, recent works have explored visual-language pre-training to enhance semantic robustness in 3D continual learning. Methods such C3PR (Cheraghian et al.) 2025) and FoundationModel (Ahmadi et al.) (024), inspired by CLIP, transfer high-level semantic priors from 2D vision-language data 3D domains, opening up new possibilities for knowledge transfer. These approaches provide strong semantic grounding, which complements geometric feature learning and supports better adaptation novel categories with limited supervision.
the former, Microshape (Chowdhury et al., 2022) proposes
3
METHOD
3.1 PROBLEM FORMULATION
We formulate the 3D FSCIL problem as sequential learning process where model progressively acquires knowledge disjoint class sets with highly imbalanced sample sizes. The class evolution proceeds through T stages, denoted as (2,, Sr). The first stage S₁ contains base classes Cowe with abundant training data. For subsequent stages t 2 2, each S, introduces a set of novel classes et Crovel: where Ciovel nC = 0 for & i' and Cruse nC = 0 for all t. Importantly, after training on S1, base class data becomes inaccessible, mimicking real-world data privacy and storage constraints.
Each class E Crase U movel is associated with set of 3D point clouds X, = {Iet Ze2,... TeN. 1==1 represents a point cloud with L Euclidean points. For novel classes in S,, the sample size is severely limited, imposing the few-shot learning constraint.
where FNi = [Pei E R.
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3.2 POINT-UQ- POINT CLOUD UNCHRZAINTY-QUANTHCCCTOO PARADIGM FOR FSCIL
The proposed Point-UQ framework reframes 3D FSCIL by shifting focus from feature tuning to decision optimization, operationalizing uncertainty quantification as the core of its unified pipeline (Fig. 2 Specifically, after a backbone network extracts hicrarchical geometric features from 3D point clouds, AAE first fuses these multi-scale representations into calibrated features while generating prediction entropy reliable uncertainty metric. Building directly these outputs, UDD then dynamically arbitrates between semantic classification and geometric prototype matching based on the entropy signals, enabling robust incremental learning without retraining. Detailed theoretical analysis supporting this framework is provided in Appendix
3.2.1
ATTENTION-DRIVES ADAPTIVE ENHANCED MODULE
In 3D FSCIL, balancing base-class knowledge retention and novel-class adaptation requires represen- tations that preserve both fine-gres Ane-grained geometric details and high-level semantic consistency (Tian et al.) 2024 Zhao et al. 2024). Existing methods either underutilize shallow local features critical for distinguishing similar categories lose detailed geometric during deep semantic aggregation, while fixed-weight fusion fails to adapt to dynamic discriminative demands (Tang et al. (024). To address this, the AAE module employs learnable multi-heas self-attention to dynamically fuse hierar- chical features: it emphasizes task-relevant patterns across scales while suppressing noise. Critically, beyond producing calibrated representations, AAE leverages prediction entropy as a reliable estimate of per-sample epistemic uncertainty. This dual output-ssematiccall preserved features paired with uncertainty cues establishes the foundation UDD's s adaptive malisionmmaiing subsequent incremental stages.
Let the backbone network produce hicrarchical features F = {Fi}f=1 where shallow layers F1, F2 capture local geometric attributes and deep layers FL-1, FL encode global shape semantics. These are stacked into a tensor:
F stack Stack(Fi, ... FL) E BXLLD
where is the batch size and D is the feature dimension. The primary point cloud feature Fpe IR BDD is expanded to a query vector Q = Fp E RBxixD, which is concatenated with F Staakk to form:
F Joint = Concat Q, sttakk) E Bx(++)))
(2)
Using multi-head self-attention, AAE models cross-level dependencies. For the h-th attention head, query, key, and value matrices are generated via linear projections:
Q2 = F joint W K K₁ = F joint W VA = Fjint W
(3)
where 1 Dxde WK Dxda, and WK Dxd, are learnable projection matrices (dy is the key/query dimension, d, is the value dimension). Attention weights are computed as:
QuKKK. Vd
A Softmax
Concatenating head outputs and projecting through W out E H.d,xD yields the fused feature:
F fused =
Concat(Headj..
Head, W out: Head, A Vn.
To retain original local geometry, residual connection combines the primary feature with the fused query-position feature:
F Final F pc
W res
F (0) fused?
(6)
where F (0)
fused is the query-derived feature (first element of F Frused) and W weight matrix. This design enhances fine-grained discriminability by integrating dynamic multi-scale interactions with local geometric preservation.
Rx is learnable
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3.2.2
DECISION DECOUPLING MODULE
In 3D FSCIL, decision conflicts between base and novel classes represent a core performance bottleneck (Tian et al. 2024). Base classes, benefiting from abundant training samples, typically exhibit stable semantic feature distributions and confident classifier outputs. However, directly applying these classifiers to novel-class mediction-esseede
when fine-tuning is involved lead to catastrophic forgetting of base-class knowledge (Ahmadi et al., 2024). Conversely, novel classes- chnesss-hhtattrizzed by scarce sumples-suftie from under-trained semantic classifier weights, whereas geometric prototype matching based on class centers provides more robust discriminative basis through spatial structure analysis. UDD addresses this by decoupling classification strategies via prediction entropy, combining a semantic branch for high-confidence base class predictions and a geometric branch for novel class discrimination, with adaptive fusion guided by uncertainty.
The semantic classifier branch leverages pre-trained base classifiers to compute logits for base classes:
sem f- W hase
IR Cruac
where f E RRP is the AAE-enhanced feature and Whase E R Crese are base classifier weights.
For the geometric prototype branch, robust prototypes are constructed to mitigate outlier effects and enhance semantic relevance. First, K K-means clustering identifies the class centroid He KMeans and the m closest samples (by Euclidean distance d; = ||f; Mell2) are selected as core features Fesl. Using class textual features Pa. semantic similarity 8j f, Pe is computed and normalized via stabilized softmax to yield weights:
exp(s; max(s))
wj-
j= exp($) - max(s))
( Sm
(8)
The final class prototype is obtained as a weighted average of the selected core features:
C. = Σj= wyf;
(9)
2j=1 W
During inference, cosine similarity scores between the test feature f E TR. and each novel class prototype e₂ are computed as:
f.C. Ck
Sgea (k)
Vk (1,..., Cuavel)
(10)
Ilfll ||xl|'
To quantify uncertainty in the semantic branch output, prediction entropy is used:
H(p) - --=--pp log Pe: p Softmax
(11)
where sem E Rrr denotes the logits output from the semantic classifier. tunable scaling factor adjusts the fusion weight via the sigmoid function:
a( H())) [0,
(12)
Finally, the classification score is adaptively fused from both branches:
Finaa
$goo (1 a) Ssom=
(13)
This mechanism prioritizes semantic confidence for clear samples while relying more on geometric matching under high uncertainty, effectively reducing catastrophic forgetting and misclassification.
3.3
TRAINING PIPELINE
The model is trained solely on base classes with frozen point cloud and text encoders, updating only AAE and UDD prototype construction parameters, with training guided by two loss functions:

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Published as a conference paper at ICLR 2026
The cross-entropy loss ensures classification accuracy:
N
Le
Die log()te),
(14)
=1
where Die is the one-hot label and Pie the predicted probability. The cosine similarity loss aligns features with their class prototypes:
fTTyy. GGll (ec.l
c =1
(15)
N
where cy is the prototype of the ground-truth class. The total loss balances these components with tunable weight 3::
Luaal 3- Lee + Leos-
(16)
This setup ensures the model learns discriminative geometric-semantic representations while main- taining base class knowledge through prototype regularization.
EXPERIMENT
Table I: Average Accuracy on Intra-dataset Evaluations. Each column (c.g., "20-40") shows the cumulative number of classes learned, with A quantifying the relative drop in overall average accuracy by the final session.
Method
ModelNet
CO3D
ShapeNet
A
89.8
9.7
4.3
3.3
3.0
96.7
16.7
11.2
3.6
1.8
0.8
99.0
87.0
25.7
1.3
0.9
0.4
99.5
Joint
89.8
88.2
87.0
83.5
80.5
10.4
36.7
69.4
64.8
62.7
60.7
59.8
22.0
87.0
85.2
84.3
83.0
82.5
82.2
81.3
6.6
ILIM
89.8
65.5
58.4
52.3
53.6
40.3
76.7
31.5
27.7
27.1
21.9
71.4
87.0
58.6
45.7
40.7
50.1
49.
49.3
43.3
ScalL,
89.8
66.8
61.5
58.7
56.5
37.1
76.7
39.5
34,1
21.1
50.1
27.5
61.1
87.0
56.6
51.8
44.3
50.3
46.3
45.1
47.8
EEIL
75.4
67.2
55.6
38.1
76.7
61.4
52.4
42.8
39.5
32.8
87.0
77.7
73.2
69.3
66.4
65.9
65.8
22.4
Sem-awware
91.3
82.2
24.3
70.0
61.7
29.1
78.6
66.9
59.2
49.1
42.9
44.
87.2
74.9
68.1
69.0
68.1
66.9
63.8
26,8
Microshape
93.6
83
78.2
75.8
67.1
28.3
78.5
67.3
60.1
56.1
$1
47.2
39.9
87.6
83.2
81.5
79.0
76.8
73.5
72.6
17.1
C3PR
91.6
82.3
75.8
70.9
22.5
69.4
66.5
51.2
53.8
88.0
81.6
77.8
76.7
76.9
76.2
74,7
15.1
Point-UQQuurs)
Table 2: Average Accuracy Cross-dataset Evaluations. Each column (e.g., "39-89") shows the cumulative number of classes learned, with A quantifying the relative drop in overall average accuracy by the final session.
Method
ShapeNer
MedalNNet ScanChjeaNNN
ShapeNet
SaanDbjezrNN
$1.0
20.2
2.3
1.3
0.8
1.0
1.0
1.3
0.9
0.5
1.5
98.0
88.4
6,4
6.0
1.9
97.9
$1.4
38.7
4.0
0.9
98,9
Joins
81.0
79.5
78.3
75.2
75.1
748
72.3
71.3
70.0
58.8
67.3
16.9
88.4
79.7
74.0
71.2
19.5
81.4
82.5
79.8
78.7
3.3
LwF
57.4
19.3
23
1.0
0.9
03
1.3
1.1
0.8
1.9
97.7
35.8
5.8
2.5
97.2
81.4
47.9
14.0
59
92.8
54.0
60.5
57.9
74.5
FACT
76.0
70.3
65.8
63.5
63.0
60.1
58.2
57.5
55.9
31.3
72.5
63.5
28.7
82.3
76.5
69.9
66.8
Sems-wwae
80.6
69.5
66.5
62.9
63.2
63.0
61.2
38.3
58.1
57.2
55.2
31.6
88.5
739
67.7
64.2
27.5
81.3
70.6
65.2
629
22.6
Macroshape
82.6
77.9
73.9
72.7
67.7
66.2
63.4
63.4
00.6
58.1
57.1
30.9
89.3
7322
68.4
63.1
27.1
82.5
74.8
71.2
67.1
18.7
C3PR
83.6
80.0
77.8
75
72.8
72.3
70.3
67.9
64.9
64.1
63.2
24.4
88.
75.7
70.6
67.8
33.2
84.5
77.8
75.5
71.9
14.
FoundorionModel
Poinr-UUQQouss))
4.1 EXPERIMENTAL. SETUP
Dataset partitioning and implementation details. To evaluate the proposed 3D FSCIL method, we conduct experiments four widely-used datasets, following the same setup Microshape (Chowd. hury et al. 2022), including both intra-dataset and cross-datase, settings. For partition details and comprehensive experimental implementation information, refer to Appendix A and Appendix
Evaluation metrics. To comprehensively evaluate the model's ability to retain knowledge of base classes while learning ones, adopt three key metrics: Average Accuracy, Relative Accuracy
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CAPR
Donais
11.24
Microhape
Modd
IL2M
Minsshage
Foundalioniiaad
30
Relative
Session
Session
Session
Session
Figure 3: Experimental results of relative accu- racy drop on ModelNet + ScanObjectNN (Left)
Figure 4: Experimental results of harmonic accu- racy on ModelNet + ScanObjectNN (Left) and ShapeNet + ScanObjectNN (Right).
and ShapeNet + ScanObjectNN (Right).
Drop Rate (A), and Harmonic Accuracy (Peng et al. between minimizing catastrophic forgetting and maximizing novel-class adaptation. For detailed formulations of these evaluation metrics, please refer to Appendix [
2022 These metries jointly reflect the trade-off
Compared methods. To evaluate our approach, we compare it against three categories of baseline methods. Conventional Transfer Learning Methods: Fine-tuning, which updates the model using only new class data, and joint training. where the model accesses all previous and new class data to mitigate forgetting. Adapted 2D FSCIL Methods: We adapt successful 2D FSCIL approaches for 3D point cloud data by replacing their CNNs with PointNet, assessing their applicability in 3D contexts (Belouadah & Popescu 2019: 2020 Castro et al. 2018 Li & Hoiem| 2017: Zhou et al. 2022a Cheraghian et 20.11. Specialized 3D FSCIL Methods: Techniques specifically designed
for 3D FSCIL tasks, including Microshape (Chowdhury et al. 2022), Cross-Dove Domain (Tan & Xiang
2024), C3PR (Cheraghian et al., (2025), and Foundation (Ahmadi et al. 2024), addressing
unique challenges posed by point cloud data.
4.2
EXPERIMENTAL PERFORMANCE ANALYSIS
In Tab. aand Tab. we present the comparison results for within-dataset and cross-dataset settings, respectively.
Intra-dataset and cross-dataset performance evaluation. In intra-dataset settings, 2D-adapted methods fail to capture point cloud geometric structures due reliance on CNN-style global features.
Even dedicated 3D baselines like C3PR (Cheraghian et al. (222)) and Foundation (Ahmadi al. 2024) lack fine-graine fine-grained discrimination they depend solely on deep semantic features and overlook geometry. causing notable accuracy drops on sparse samples. In contrast, Point-UQ achieves a consistent 4% accuracy gain at each incremental stage, rooted our core innovation: uncertainty- guided decision optimization paired with explicit 3D geometry modeling. The AAE module boosts shape discriminability for subtle differences via multi-scale feature integration, while the UDD module curbs misclassification of ambiguous samples.
In cross-datase tasks, 2D-adapted methods suffer up to 20% novel-class accuracy collapse due to lacking 3D invariance. Dedicated 3D baselines also perform poorly under domain shifts, failing to adapt to variations point cloud density and noise. Point-UQ, however, excels by design: its decision logic is inherently adaptive. The AAE module dynamically fuses multi-level geometric features using learnable attention weights, tailoring representations to the current task context. More importantly, the UDD mechanism exploits uncertainty switching dignal--iverttnn high-entropy samples to geometry-based matching, which exhibits superior cross-domain stability. This shift from fixed to uncertainty aware geeision-makin allows Point- UQ to achieve state-of-the-an -f-the-art performance, surpassing the second-bes method by 9%, while maintaining strong base-clas. recognition.
Forgetting and adaptation efficiency. To more accurately reflect the model's 's forgetting behavior during incremental learning, we further calculated the accuracy drop relative to the base classes at each incremental stage 3.. Our method records the smallest average drop, nearly two times lower than baselines such as Microshape [Chowdhury et al. 2022). Furthermore, using the harmonic mean
'The results of the compared methods listed in the tables are sourced from (Ahmadi et al. 2024 and Cher aghian et al. 2025). and all experiments were conducted under consistent configurations. Notably, the Foun- Model was omitted from Tab. ass as its original paper did not report intra-dataset performance, and we encountered significant discrepancies when attempting reproduce its results.
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balances performance between base and novel classes method outperforms existing approaches by 9% on average across cross-datases benchmarks (Fig These results validate that integrating geometric structure modeling with uncertainyy-awar decision decision-making addresses critical limitations in prior work, setting new standards for 3D) few-shot class incremental learning. Meanwhile, we also conducted analysis of the computational complexity of the method, comparing it from two aspects: time efficiency and cache efficiency. Detailed experimental analysis can be found in Appendix D
4.3 ABLATION STUDY
To rigorously assess the contribution of each component in our framework, carried out ablation experiments on the cross-dataset setting from ShapeNet to CO3D. The performance was evaluated using both average accuracy and harmonic accuracy.
Table 3: Effectiveness of the components in Point-UQ.
Average Accuracy (%)
Harmonic Ace Avg (%)
AAE
UDD
3
5
7
10
Avg
X
87.9
86.3
85.2
82.6
80.3
79.3
77.9
76.6
76.1
74.6
73.6
54.5
89.0
88.5
85.1
84.1
81.4
78.2
79.5
77.8
76.1
75.1
X
89.8
88.5
87.1
85.2
83.6
81.9
81.2
80.3
79.7
79.2
78.9
60.0
91.4
89.5
88.3
86,5
84.7
83.4
82.9
81.8
81.1
80.4
80.3
64.8
Evaluating component effectiveness in Point-UQ. To evaluate the effectiivneess of each component in Point-UQ, we conducted a series of experiments. First, we evaluate the effectiveness of our two proposed modules: Attention-driven Adaptive Enhanced Module and Uncertaity-gqunnfficaan -uantification Decision Decoupling Module, with results summarized Tab. 3 Ablation studies are conducted by replacing AAE with standard alignment mechanism and substituting UDD with dual-feature caching strategy (Ahmadi et al. (024). Removing AAE leads to a notable performance drop in the base-class training phase. highlighting its importance in capturing and fusing multi-scale geometric features. The UDD module dynamically balances semantic and geometric prototype similarities based on prediction entropy, enabling uncertainty-awan soft fusion. This allows the model to adaptively leverage reliable knowledge sources during novel class recognition.
In addition to the ablation studies on our main components, we further evaluate the effectiveness of the proposed multi-scale self-attention feature fusion mechanism by comparing it with several representative feature fusion strategies, and validate the design of our semantic-weighted class prototype construction approach through dedicated analysis as shown in Fig. 5 The experimental results the proposed multi-scale self-attention feature fusion mechanism are shown Tab. 4 Specifically,
refers to using only the high-level semantic features from the final layer for prediction. "LayerWise- To-Last" introduces shallow features and fuses each layer with the last one individually, generating separate predictions which are then aggregated for the final output (Tang et al. 2024). "Symmetric-Cross-Fiee
also incorporates shallow features but adopts symmetric layer-wise fusion strategy- the first with the last, the second with the second-to- last, and so on. In contrast, our method, Point-UQ, employs a multi-scale self-attention mechanism to adaptively integrate shallow geometric details with deep semantic features, enabling more effective cross-level information interaction. The results demonstrate that, compared to fixed-rule-basee fusion methods, our self-attention fusion mechanism can dynamically adjust the contribution weights of different feature levels according to task demands. This not only improves the model's 's discriminative capability but also enhances robustness to class variations. These findings further confirm the superiority and necessity of Point-UQ in addressing complex feature distribution challenges in incremental learning scenarios.
Table 4: Effectiveness the proposed multi-scale self-attention feature fusion mechanism.
Average Accuracy (%)
Harmonic Acc Avg (%)
Method
Avg
Dee--Senantic-oll
79.7
79.2
78.9
LayerWise-Too Last
90.5
89.1
87.5
85.7
83.9
82.5
82.1
81.1
80.4
80.0
79.8
61.0
Symmetrie-Cress- Fross-Fusion
90.1
88.9
87.3
82.0
80.5
79.4
79.2
(ouus))
91.4
89.5
88.3
86.5
84.7
83.4
82.9
81.8
81.1
80.4
80.3
64.8

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ClasterProto
RIMT TOOOOTI Accureccy
MATT romomiic Accuraacy Surray
3
Sassion
Sassion
Saasion
Sassion
Figure 5: Experimental results of the Proposed Figure 6: Impact of the Number of Samples Used for Constructing Semantic-weighted Class Proto- types on Final Performance.
Semantic-weighted Class Prototype Construction Approach.
We further validate the effectiveness of our semantic- semantic-weighted class prototype construction method, as illustrated in Fig. 5 uncertainty quantification, well-constructed class prototypes are essential, as our method dynamically assigns higher weight to geometric similarity when prediction entropy is bigh-thereby leveraging structural relationships among prototypes to enhance decision reliability. To evaluate the advantage of our strategy, compare it against two widely used baseline methods: MeanProto: Computes the class prototype as the mean of all sample features within that class. ClusterProto: Selects representative central points via clustering
as class prototypes. Exper- imental results demonstrate that incorporating semantic information into feature weighting yields significantly more discriminative and robust prototypes. Our method achieves superior performance in both novel class recognition and uncertainty estimation, further validating its effectiveness in modeling dynamic class representations during incremental learning.
Hy Hyperparameeerr experiments. In Point-UQ, three key hyperparameterss are involved: the number of samples used to construct semantic- weighted class prototypes m. the weight coefficient that balances the cross-entropy loss and cosine loss B, and the activation function coefficient that maps entropy values weights To evaluate their influence on overall performance, we conduct a comprehensive ablation study in this section. Fig. Biglouillsttttee the impact of the sample count on the final performance, while the remaining experimental results and detailed analysis can found in Appendix [F Besides, we provide extended ablation studies about activation function and alternative uncertainty measures in Appendix
semantic-weighted
CONCLUSION
In this paper, propose Point-UQ, a novel uncertainty-quanificaaoo framework for 3D FSCIL. Departing from fuuing--ocused paradigms, it integrates two synergistic modules: AAE, which fuses multi-scale features and generates epistemic uncertainty via prediction entropy: and UDD, which dynamically arbitrates between semantic classification and geometric matching. This design enables Point-UQ to balance base-class retention and novel-class adaptation without retraining, mitigating catastrophic forgetting and overfitting. Extensive experiments across multiple 3D benchmarks confirm that our approach consistently outperforms state-of-the-ar methods terms of accuracy, forgetting rate, and harmonic mean, establishing new performance standards for 3D FSCIL.
6
SUPPLEMENTARY STATEMENTS
6.1 ETHICS STATEMENT
This research strictly adheres to the ICLR Code of Ethics with no ethics-related risks: it uses public 3D point cloud benchmark datasets (ModelNet, ShapeNet, ScanObjectNN, CO3D) and focuses on algorithmic innovation for 3D few-shot class-incremental learning, without involving scenarios endangering public safety, infringing privacy, or producing discrimination.
6.2 REPRODUCIBILITT STATEMENT
To ensure reproducibility, dataset partitioning, experimental configurations, method details, and evaluation metrics are thoroughly described in the experimental section and corresponding appendices. Experimental results of comparative methods are sourced from public literature, and our experiments strictly follow the same configurations as baseline methods for fair comparison.
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A APPENDIX FOR DATASET PARTITIONING
To better evaluate the practical applicability of Point-UQ under real-world constraints, we design series of cross-datasct incremental learning experiments that simulate scenarios where novel categories come from limited and noisy real-scanned data. These setups emphasize generalization across domains and robustness to data scareity-key challenges in few-shot class incremental learning (FSCIL) for 3D point clouds.
In the most realistic setting, we transfer knowledge from clean synthetic data to real-world scans. Specifically, we first train on base classes from ModelNet or ShapeNet and incrementally introduce new classes from ScanObjectNN-a dataset derived from real-world scans with inherent noise and incompleteness. Following Chowdhury et al. (2022), we define four incremental tasks when transitioning from ModelNet to ScanObjectNN, For the ShapeNet to ScanObjectNNN setup, we use 44 base classes from ShapeNet and introduce 15 real-scanned classes from ScanObjectNN across four stages. The most challenging scenario is established between ShapeNet and CO3D, where we define eleven incremental tasks. In this setting, 44 base classes are taken from ShapeNet, while 50 fine-grained object classes from CO3D are introduced incrementally. This large-scale cross-domanl transition poses significant difficulties due to the domain gap and complex intra-class variations present CO3D. As a complement to these cross-dataset evaluations, we also conduct within-dataser experiments to establish baseline performance. On ModelNet, we split the dataset into 20 base classes and 20 incremental classes, distributed over four stages. For ShapeNet and CO3D, we adopt larger base sets: 25 base classes for each, with the remaining 30 (ShapeNet) or 25 (CO3D) classes divided into 7 or 6 incremental tasks, respectively.
These diverse experimental settings allow us to comprehensively assess our method's ability to generalize across both intra- and inter-dataset boundaries, particularly under data-limited and domain- shifted conditions.
B APPENDIX FOR IMPLEMENTATION DETAILS
Our framework leverages the Uni3D model Zhou al. 2023 for both point cloud and text encoding, maintaining these components frozen throughout the training process. This design choice enables efficient few-shot learning without necessitating any parameter updates to the foundational models. Instead, we focus optimizing the parameters of the AAE module, which consists of 2-layer Trans- former encoder with each layer employing an 8-head multi-head attention mechanism. To balance computational efficiency with performance, our setup mirrors that of the FoundationModel (Ahmadi ct al. 2024). ensuring optimal resource utilization. Specifically, we adopt the "EVA02-E-147" CLIP model alongside the "eva02-base 2ee--bae__pttchh141 448" model as point cloud encoders (Zhou al. 2023). As input, we uniformly sample 1024 points from each 3D point cloud object using farthest point sampling. All experiments were conducted on single NVIDIA A100 GPU. We utilized the AdamW W optimizer with weight decay of 1 x 10-4 to manage overfitting. The training process spanned epochs, employing fixed learning rate of 0. 0.00002 and a batch size of 32. In implementa- tion, we fix the following hyperparameters across all experiments: the number of samples used to construct semantic- weighted class prototypes m is set to 5; the coefficient balancing cross- cross-entropy loss and cosine loss 33 is set to 5; and the activation function coefficient that maps entropy values to weights À is set
C APPENDIX FOR EVALUATION METRICS
To comprehensively evaluate the model's ability to retain knowledge of base classes while adapting to new ones, we adopt three key metrics: harmonic accuracy (AA), relative accuracy drop rate (A), and average accuracy (AA). These metrics jointly reflect the model's stability, adaptability, and overall performance during incremental learning.
Harmonic accuracy (A₂): This metric balances performance on base and novel classes by computing the harmonic mean of their respective accuracies:
2 As An
A
(C1)
As + An

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Published as a conference paper at ICLR 2026
where As and An denote the average accuracy base and novel classes, respectively. A higher A indicates a better trade-off between preserving old knowledge and learning new concepts (Peng al. 2022).
Relative accuracy drop rate (Ai); To quantify performance degradation caused by incre- mental updates, we compute the relative drop in total accuracy from the initial stage to the final one:
accr accp
X 100.
(C2)
acco
Here, accr and acep represent the total accuracy after the last and first incremental tasks, respectively. A smaller A suggests greater robustness against catastrophic forgetting.
Average accuracy (AA): As an overall measure of classification performance, we calculate the average accuracy across all seen classes at each incremental stage:
correctly predicted instances AA
(C3)
total instances the test set
This provides straightforward view of how well the model performs as new classes are introduced over time.
These three metrics together offer comprehensive evaluation model behavior coverrin both class retention and adaptation capabilities, as well as overall classification accuracy.
D APPENDIX FOR COMPUTATIONAL COMPLEXITY
Our method's computational overhead from multi-head attention and clustering mainly occurs during base-class training, yet total training time remains shorter than FoundationMMdel (Ahmadi et al. 2024). In the incremental phase, the few-shot setup (5 samples per novel class) eliminates the need for clustering, and it is training-free freee--ffeeeeeee ideal for real-time scenarios.
DI presents comparative experiments between Point-UQ and current SOTA on the cross-dataset setting from ShapeNet CO3D, where FoundationModel (Ahmadi al. 2024) is implemented strictly in accordance with its original paper. It is evident that our method achieves significant performance gains across all scenarios while using only one-fifth of the sample cache, with the average inference time per session extending by approximately 30 seconds.
Table DI: Comparison of Time and Memory Efficiency with SOTA
Method
Time Efficiency
Memory Efficiency
Training Time (s) Inference per Session (s)
Exemplars Cache
FoundationModel
2571
130
445
Point-UQ
1622
161
89
E APPENDIX FOR HYPERPARAMETER SENSITIVITY
Table E1: Impact of the entropy-to-weight mapping coefficient (A) experimental results.
Average Accuracy (%)
Harmonic Ace Avg (%)
À
2
10
Avg
0.5
91.2
87.3
84.5
81.9
79.0
77.1
76.0
75.0
74.6
74.1
73.9
10.9
1.0
91.2
88.8
86.9
84.9
82.7
81
80.1
79.2
79.0
78.5
78.4
46.7
1.5
91.2
89.6
88.0
86.2
84.2
83.0
82.3
81.4
80.9
80.3
80.0
60.6
2.0
91.4
89.5
88.3
86.5
84.7
83.4
82,9
81.8
81.1
80.4
80.3
64.8
2.5
91.3
89.0
87.9
86.1
84.4
83.2
82.5
81.5
80.9
80.1
79.8
65.8
3.0
91.3
88.7
87.7
85.9
84.2
83.0
82,3
81.2
80.5
79.8
79.6
66.5
2

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Published as a conference paper at ICLR 2026
valoam0 valuem3 voluem1
valuen0
veluend velucwd
veluem3
voluee?
volcom1
Average Accuracy (%)
Harmonic Accuracy (%)
75
88
70
86
84
65
82
80
5
Session
Session
Figure El: Impact of the loss balancing coefficient (5) on model performance.
In our method, three key hyperparameters are involved: the number of samples used to construct semantic-weighted class prototypes m, the weight coefficient that balances the cross-entropy loss and cosine loss 3, and the activation function coefficient that maps entropy values to weights A. To evaluate their influence on overall performance, we conduct comprehensive ablation study in this section.
Sample count (m)): illustrated in Fig. setting m 5 yields the best performance across multiple evaluation metrics. With only five samples per novel class, excluding any through clustering (m 5) risks discarding critical geometric patterns in sparse data, which are essential for accurate prototype construction. This leads to unstable class representations and degrades generalization to new categories.
Loss balancing coefficient (3): During the base-class training stage, both cross-entropy loss and cosine loss are utilized to encourage discriminative feature learning. The trade-off between these two objectives is controlled by the balancing coefficient B. As shown in Fig. EI evaluate values ranging from to 9. A value of 3 5 achieves the optimal performance because it allows for effective learning of both classification accuracy (via cross-entropy loss) and feature separation (via cosine loss). When ,3 is too small, the model overly emphasizes the cosine loss, which can hinder the alignment between point cloud features and textual embeddings-crittee generalization. Conversely, when is too large, the cosine loss becomes underweighted, leading less structured feature distributions and weakening the geometric prototype's supervisory role in shaping the embedding space.
for cross-modal
Entropy-to-weige mapping coefficient (A): The activation function coefficient À plays a crucial role in the UDD module, as it controls how uncertainty estimates are mapped to adaptive weights for the semantic decision branch and geometric prototype branch. Tab. F: presents the results under different À settings, ranging from 0.5 to 3.0 with intervals of 0.5. A value of X = 2 is found to yield the best performance. This suggests an appropriate sensitivity to uncertainty- too steep in the weighting function. When À is too small, the mapping becomes overly sensitive to minor variations in entropy, causing instability in the final prediction. On the other hand, excessively large À values, while yielding high accuracy novel categories, may cause forgetting of base-class knowledge, suppress low-uncertainty predictions, potentially discarding reliable decisions made by the model. With À 2, the model strikes favorable balance between responsiveness uncertainty and confidence in accurate predictions, which is essential for maintaining high performance without additional parameter updates during the incremental phase.
too flat nor
F EXTENDED EXPERIMENTAL ANALYSIS
F.I INTRA-DATASET BENCIMARKING
To ensure a comprehensive comparison, we evaluated the baseline method FoundationModel (Ahmadi) al. 2024) under intra-dataset setting. As the original work did report results for this setting
3

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Published as a conference paper at ICLR 2026
and the code not available, re-implemented it based the methodological description and conducted extensive hyperparameter tuning to achieve its best attainable performance.
Tab. F1 shows that Point-UQ consistently and significantly outperforms the re-implemented Founda- tionModel across all datasets and incremental stages, even under this favorable configuration for the baseline.
Table Fl: Comparative performance (%) under the intra-dataset setting.
Dataset
Method
0
1
2
3
5
6
A
MouddationModel
81.9
74.8
72.2
70.9
70.3
67.7
64.8
20.9
ShapeNet
Point-UQ
93.3
91.2
90.0
89.3
88.8
88.4
86.5
7.9
FoundationModel
86.1
83.8
83.1
79.7
71.7
17.1
ModelNet
Point-UQ
97.1
91.0
86.8
83.0
79.0
18.6
Mouddation
69.8
55.8
50.5
45.5
41.5
35.1
49.7
CO3D
Point-UQ 82.1
73.6
70.0
68.4
67.4
66.5
19.0
F.2 SCALABILITY TO EXTENDED INCREMENTAL STAGES
To assess the long-term scalability our method, we extended the number of incremental stages from 11 to 25. As shown in Tab. F2 Point-UQ maintains strong stability with forgetting rate (A1) of 20.0%, significantly lower than the compared baseline, demonstrating its practicality for large-scale incremental learning scenarios.
Table F2: Performance (%) over 25 incremental stages on combined benchmark.
Method
2 4
10
12
14
16
18
20
22
24
A
FoundationMMddel
77.7 76.6 74.7
71.5
69.7
68.5
66.7
65.5
64.8
56.7
54.6
53.5
50.2
35.4
Point-UQ
91.4 89.2 87.3 85.0
83.6
82.6
81.3
80.4
79.8
77.9
75.4
74.9
73.1
20.0
F.3 PERFORMANCE UNDER EXTREME FEW-SHOT SETTINGS
We evaluated Point-UQ under the challenging 1-shot and 2-shot settings. As shown in Tab. F3
Point-UQ demonstrates remarkable robustness, with an average accuracy drop only 4% in the 1-shot and 3% in the 2-shot setting compared to the standard 5-shot setting.
Table F3: Performance (%) under 1-shot, 2-shot, and 5-shot settings.
Setting
2
3
4
7
8
9
10
A
1-shot
91.4
88.6
87.5
85.1
83.7
82.4
81.2
79.9
78.8
77.8
76.0
16.8
2-shot
91.4
89.4
88.2
86.3
85.1
83.7
82.5
80.9
79.8
79.1
77.3
15.4
5-shot
91.4
89.5
88.3
86.5
84.7
83.4
82.9
81.8
81.1
80.4
80.3
12.1
F.4 ADDITIONAL BENCHMARKING ON ESCIL3D-XL
We further evaluated Point-UO on the FSCIL3D-X benchmark, comparing it against FACT (Zhou al. 2022a). BiDist (Zhao al. 2023 and FILP-3D (Xu et al. 2025). Under the 5-shot incremental learning setting, Point-UQ demonstrates exceptional generalization capability.
Tab. shosswws that Point-UQ achieves comprehensive superiority this new benchmark, exhibiting higher average accuracy and significantly reduced forgetting rates, particularly in the challenging S2R task.
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Published as a conference paper at ICLR 2026
Table F4: Performance comparison on the FSCIL3D-XL benchmark (%).
(a) Synthetic- to Synthetic (S2S) Task
Method
0
3
4
FACT
82.6
77.0
72.4
69.8
68.4
67.7
67.3
18.5
BiDist
89.6
87.7
86.2
84.7
83.8
83.6
82.3
8.1
FILP-3D
90.6
89.0
86.7
84.2
83.2
81.8
82.2
9.3
Point-UQ
90.7
89.4
88.3
86.6
85.8
85.1
83.5
7.9
(b) Synthetic-to Real (S2R) Task
Method
0
1
3
4
5
8
9
10
11
A
FACT
82.4
77.2
74.5
73.1
71.3
70.4
67.2
65.2
63.8
61.8
59.9
59.8
27.4
BiDist
89.4
54.0
54.7
56.4
57.0
55.9
56.3
52.9
52.3
51.7
50.8
50.1
43.9
FILP-3D
90.0
87.0
86.4
85.0
83.7
82.7
81.4
79.4
78.2
76.8
74.8
74.6
17.1
Point-UQ
90.7
88.9
87.6
85.8
84.3
82.7
82.0
82.1
82.0
82.0
82.1
82.0
9.6
G QUALITATIVE ANALYSIS OF UDD DECISION SWITCHING
To qualitatively verify the link between prediction entropy and the UDD module's decision logic, we performed a comprehensive analysis that examines the module's s behavior in relation to sample ambiguity. Our visualization provides compelling evidence for the dynamic decision mechanism of the UDD module.
Heal
Lnd anC:
subove Hig Aoliuy
(a) Relationship between predicted entropy and &
(b) Distribution of a across uncertainty levels
Figure GI: Visual analysis UDD module decision patterns. (a) Scatter plot showing the positive correlation between semantic entropy and fusion weight with sigmoid fitting curve (k=2), (b) Box plot demonstrating systematic increase in a values across entropy buckets, confirming the uncertainty adaptiv behavior of UDD.
Key Observations: The visualization reveals strong positive correlation between entropy and the fusion weight a. As shown in Fig. Gla low-entropy samples (clear cases) consistently result in lower values, indicating semantic-dominated decisions. In contrast, high-entropy samples (ambiguous cases) trigger significantly higher a values, demonstrating systematic switch to geometry-dominat decisions. This pattern further validated by the box plot in Fig. GIb which shows the progressive increase in a distribution across uncertainty levels.
H EXTENDED ABLATION STUDIES
H. ABLATION STUDY ON ACTIVATION FUNCTIONS
We conducted ablation study on the activation function used for uncertainty weighting in Eq. (12), comparing tanh (scaled to [0,1]), normalized softplus, a ReLU-sigmoid combination, and piecewise linear function against the default sigmoid.
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As summarized in Tab, H1 the sigmoid function achieves the best balance between accuracy and stability throughout all incremental stages, yielding the lowest final forgetting rate.
Table HI: Performance comparison of different activation functions for uncertainty weighting.
Activation
1
2
3
4
6
7
10
tanh_scaled
91.4
88.0
87.0
85.5
84.2
83.0
82.2
80.8
80.0
79.3
78.2 2
14.4
softplus _ norm
91.4
88.9
87.0
84.2
83.0
81.8
80.8
79.4
77.8
76.5
74.8
18.1
relu sigmoid
91.4
90.0
88.6
87.1
86.0
84.8
84.0
82.5
81.6
80.7
79.7
12.8
piecewise linear
91.4
87.6
86.4
84.8
83.7
82.6
81.5
80.2
79.3
78.4
77.3
15.4
sigmoid
91.4
89.5
88.3
86.5
84.7
83.4
82.9
81.8
81.1
80.4
80.3
12.1
H.2 ANALYSIS OF ALTERNATIVE UNCERTAINTY MEASURES
We investigated alternative uncertainty estimation strategies: the semantic confidence gap (difference between top-2 predicted probabilities) and semantic-geometre consistency (agreement between semantic predictions and geometric similarity patterns).
Results in Tab. H2 show that while both alternatives achieve reasonable performance, the semantic classifier entropy maintains consistent advantage, achieving the lowest forgetting rate. Its superiority stems from its computational efficiency, direct alignment with our training-free paradigm, and robust sensitivity ambiguity under domain shifts.
Table H2: Performance comparison of different uncertainty measurement methods.
Method
5
7
8
9
10
A
Semantic Confidence Gap
99..4 90.3 89.0 87.1 85.8
84.7
84.0
82.5
81.4
80.3
78.9
13.7
Geoentrii Consistency
91.4 87.9 86.8
85.6
84.5
83.3
82.5
81.2
80.5
79.9
78.6
13.1
Semantic Classifier Entropy
91.4 89.5 88.3
86.5
84.7
83.4
82.9
81.8
81.1
80.4
80.3
12.1
H.3 BASE MODEL COMPATIBILITY
Comprehensive cross-dataset evaluations were conducted employing DGCNN and PointNet pre- trained backbones. Point-UQ is shown in Tab. H3 sustain robust performance and consistently surpass baseline methods with either backbone. These results substantiate that the effectiveness of our approach independent of the underlying feature extractor, functioning robustly across disparate geometric representations.
Table H3: Performance with different base models on the cross-dataset setting.
Base Model
1
2
3
8
9
10
A
DGCNN
89.9
84.1
80.9
77.5
75.0
71.9
70.3
69.3
68.0
67.5
66.7
25.8
PointNet
89.1
86.1
83.2
80.1
77.2
75.0
73.5
72.4
71.5
70.8
70.1
20.5
Point-UQ
91.4
89.5
88.3
86.5
84.7
83.4
82.9
81.8
81.1
80.4
80.3
12.1
THEORETICAL ANALYSIS OF POINT-UQ
This appendix presents theoretical comparison between conventional fine-tuning approaches and the proposed Point-UQ framework, analyzing their fundamental mechanisms through mathematical formulations.

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Published as a conference paper at ICLR 2026
Table II: Summary of key notations used in the theoretical proofs.
Notation
Description
Input space of 3D point clouds
o())
Feature mapping function
(AAE()
Enhanced feature from AAE module
fer (2)
Output distribution of fixed base model Predictive entropy (Shannon entropy)
(())
Ssmm
Semantic classification logits
Geometric similarity scores
gen
Adaptive fusion weight from UDD Class prototype for class
Set of all seen classes
Generalization error of UDD novel classes Calibration error coefficient Calibration Error
c
CE
(x)
True (oracle) uncertainty Lipschitz constant
L
I.1 PART I: THEORETICAL LIMITATIONS OF CONVENTIONAL FINE-TUNING APPROACHES
Fundamental Bound on Feature Representation 3D FSCIL, the pursuit of perfect feature representation faces inherent theoretical constraints. The sample complexity requirement:
(VC(H) + log(1/0)
m 2
(II)
contradicts the few-shot setting where Moovv
<
(VC(H)
This leads to the unavoidable trade-off:
Fixed VC(H) + Insufficient representation power
Increased VC(H) = Rovertit
[@) and O(D²) complexity
(12)
Feature Drift in Parameter Tuning Conventional fine-tuning Oo + O, Oo + AO induces inevitable feature drift:
(1(2) (z) + Ad(x), [ 2 n I/V Wo)ll-
(I3)
This drift fundamentally limits the generalization performance:
pline-tune
Reew + Aarin.
Aunn > 0
(14)
Theoretical Conclusion for Conventional Methods
The conventional paradigm is bounded by:
min
pfiie tune new
2 Rie
+ Adrin + Erepresentation:
(15)
where Grepresentation represents the irreducible error from imperfect feature representation under few-shot constraints.
I.2 PART II: THEORETICAL ADVANTAGES OF THE POINT-UQ FRAMEWORK
Decision- Theoretic Formulation of UDD decision optimization:
Decision-Theoretic
The UDD module replaces parameter tuning with
Sfinal a- Sgoo a) Ssems
a a= (a.A-((fe,(a (z))).
(16)
This formulation provides two key advantages:
FEATURE STABILITY PRESERVATION By fixing Ou UDD eliminates feature drift: Aδ( ensuring:
RUDD
R
(17)
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GENERALIZATION ERROR BOUND
The generalization enror satisfies:
Reew
-
Re +e E
Adrint 0 0<E<l
(18)
Since Adnn = 0, we obtain the strict bound:
RUDD
Riew-
(19)
Entropy Calibration Enhancement via AAE The AAE module ensures reliable uncertainty quantification through feature enhancement:
[t ddh
( Σ (z()))((z)) (1 = Softmax 1=1
(110)
This optimization reduces calibration error:
CEAAE Ex (feo - -1))) <
CEbase
(I11)
Theoretical Conelusion for Point-UQ The Point-UQ framework achieves the combined bound:
R
<
Rease + R +
Ccalibration-
(I12)
where Ecalibration > 0 represents the reducible error from entropy calibration, which can minimized through AAE optimization.
1.3 COMPARATIVE ANALYSIS
The theoretical comparison reveals fundamental advantages of Point-UQ over conventional ap- proaches:
Error Bound Comparison:
Poinnt-UQ UQ
< RRuse + Riew + Callibration
< R + A drift +
Crepresentation
S
pline-tum Lune
total
(113)
Complexity Advantage:
OPoin-Uul 10ol + AAAE + OuDDl
O tine-tuml
+
A|0
(114)
where AAΛA + 1OuDl << A | represents the parameter overhead of conventional capacity expansion.
Stability Guarantee:
A Aprinn- (Fola) =
0 vs.
Adiin-tunee (Foia) 0.
(115)
Final Theoretical Conclusion fine-tuning approaches by simultaneously achieving:
The Point-UQ framework theoretically dominates conventional
(1) R Point-tIQ
Rotal
< plline-tune
(2) 1Peinn-tuQl < 1 (ineettune)
(3) A AProint-UQ 0 Pline-tume
This triple advantage demonstrates the fundamental superiority of the decision optimization paradigm over parameter tuning in 3D FSCIL scenarios.
J LLM USAGE STATEMENT
In this paper, Large Language Models are only used as general-purpont auxiliary tools, primarily for document-level auxiliary tasks such as grammar checking and expression refinement. LLMs did participate in the core conceptualization, method derivation, or experimental design of this research, nor did they contribute to any core writing content.
8


Reviews:
[{"review_id": "ESLWjJxr0V", "reviewer": "Reviewer_SBK3", "summary": "This paper introduces Point-UQ, a training-free uncertainty-guided paradigm for 3D few-shot class-incremental learning that replaces feature fine-tuning with dynamic decision optimization. Experiments on four benchmarks report roughly 4% average accuracy gains over existing methods while reducing catastrophic forgetting.", "questions": "1. Can intra-dataset results for FoundationModel be supplied, or at least the numerical discrepancies encountered during reproduction, so that readers can judge whether performance gaps are methodological or implementational?\n\n2. Can high- and low-entropy examples be visualized together with the corresponding UDD decisions to confirm that uncertainty estimates capture genuine ambiguity?\n\n3. How does accuracy degrade under 1-shot or 2-shot conditions, and is the K-means prototype still reliable?\n\n4. Does the approach maintain low forgetting and modest resource usage when the number of incremental stages grows well beyond ten?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Point-UQ introduces a paradigm shift from feature fine-tuning to decision optimization, resolving the core trade-off between retaining base-class knowledge and adapting to novel classes in 3D FSCIL while avoiding the high training costs and catastrophic forgetting of conventional methods.\n\n2. Point-UQ’s AAE and UDD modules are tightly integrated, as AAE generates calibrated features and reliable uncertainty estimates, and UDD employs these signals for adaptive decision-making, effectively combining geometric structure modeling and semantic consistency that are critical for 3D point cloud analysis.\n\n3. Point-UQ shows improvements across multiple benchmarks and metrics (average accuracy, harmonic mean, forgetting rate), and its theoretical analysis formally proves advantages over fine-tuning in error bounds, complexity, and feature stability.", "weaknesses": "1. The intra-dataset evaluation omits comparison with the strongest baseline, FoundationModel. The authors explain that reproducible intra-dataset numbers could not be obtained, but no detailed comparison between their reproduced values and the original paper is provided. The claimed improvements may therefore partly reflect implementation differences.\n\n2. No qualitative analysis links prediction entropy to actual sample ambiguity, making it hard to verify that the UDD module switches between semantic and geometric cues as intended.\n\n3. All experiments adopt a 5-shot protocol and performance in more extreme 1- or 2-shot regimes, where prototype construction is fragile, is not examined.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "9qsnI1mPIm", "reviewer": "Reviewer_nY6g", "summary": "This paper proposes a unified method to estimate and interpret uncertainty in 3D point cloud tasks such as classification and segmentation. It introduces a lightweight probabilistic module that models both epistemic and aleatoric uncertainties directly within point-based neural networks without requiring multiple forward passes. The framework leverages a dual-branch design—one for feature learning and another for uncertainty estimation—to identify unreliable regions in 3D space, enabling more robust predictions and better calibration under noisy or sparse data conditions. Experimental results on benchmark datasets demonstrate that Point-UQ improves model reliability and interpretability while maintaining competitive accuracy.", "questions": "1. Can the authors experiment with different functions instead of the sigmoid function in Equation (12)?\n\n2. Can the authors display the intermediate output of Equation (12) during model tuning to verify whether it correctly weights according to uncertainty?\n\n3. The paper does not mention the model’s performance with respect to seed sensitivity. Methods of this kind often vary significantly with random seeds—can the authors comment on this?\n\n4. Pl comment on the calibration point.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Adoption of decision optimization for incremental learning instead of conventional fine-tuning approaches.\n\n2. Uncertainty-based quantification for decision-making, providing higher weight to geometric prototypes or semantic representations accordingly.\n\n3. Training-free and computationally efficient method.", "weaknesses": "1. The authors did not discuss the effect of replacing the sigmoid function in Equation (12) with other activation functions.\n\n2. The paper mentions handling overfitting and catastrophic forgetting, but comparative results showing model success versus baseline failures are missing.\n\n3. Tables could be clearer and more reader-friendly. For example, in table headings, the numbers listed below the dataset names are not explained and should be clarified.\n\n4. While the method claims to improve reliability without multiple forward passes, the paper provides limited quantitative analysis of calibration metrics (e.g., ECE, NLL) and does not fully benchmark computational trade-offs compared to ensemble-based or Bayesian baselines.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "MojrRCkmu0", "reviewer": "Reviewer_92Tg", "summary": "This paper introduces Point-UQ, a novel framework designed for 3D few-shot class-incremental learning (FSCIL). The key innovation of Point-UQ lies in its approach to dynamic decision optimization through uncertainty quantification. The framework integrates two co-designed modules: the Attention-driven Adaptive Enhancement (AAE) for fusing multi-scale features and generating epistemic uncertainty, and the Uncertainty-quantification Decision Decoupling (UDD) module, which helps retain base-class knowledge while facilitating novel-class recognition.", "questions": "My questions are mainly based on the above weaknesses:\n1. If a different base model is used to extract point features, would the performance of the proposed method still be strong?\n2. How does the model perform on the FSCIL3D-XL benchmark?\n3. Could the authors consider testing the model on newer datasets, such as Objaverse [1,2], to further evaluate its performance?\n\n[1] Objaverse: A universe of annotated 3d objects. CVPR, 2023: 13142-13153.\n[2] Objaverse-xl: A universe of 10m+ 3d objects. NIPS, 2023, 36: 35799-35813.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. This paper presents a well-structured pipeline from multi-scale feature fusion to decision-making, addressing core challenges in 3D FSCIL, such as catastrophic forgetting and overfitting on novel classes.\n2. The use of uncertainty quantification in decision-making (AAE and UDD modules) is a novel and effective strategy to tackle the trade-offs in incremental learning.\n3. The method outperforms existing approaches in various metrics, including average accuracy, forgetting rate, and harmonic accuracy, demonstrating its robustness across different datasets and evaluation setups.", "weaknesses": "1. Fairness of Experiments: This paper is based on a strong pre-trained model, Uni3D, for initialization. As shown in Table 3, even without the addition of AAE and UDD, the model's performance is already better than other methods. This raises concerns about the primary sources of performance improvement in the proposed approach.\n2. Outdated Benchmark: The benchmark datasets used in this paper are relatively old. I suggest the authors additionally evaluate the model’s performance on the FSCIL3D-XL benchmark [1] to provide a more up-to-date comparison.\n\n[1] FILP-3D: Enhancing 3D few-shot class-incremental learning with pre-trained vision-language models. Pattern Recognition, 2025, 165: 111558.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "EEqULBcmIO", "reviewer": "Reviewer_tD98", "summary": "This paper proposes Point-UQ, a training-free uncertainty-quantification framework for 3D few-shot class-incremental learning. Unlike existing approaches that rely on fine-tuning feature representations with static decision boundaries, Point-UQ focuses on decision optimization. The framework integrates two key modules: Attention-driven Adaptive Enhancement and Uncertainty-Quantification Decision Decoupling. Extensive experiments show that Point-UQ outperforms state-of-the-art baselines across multiple benchmarks.", "questions": "Please refer to the weakness part and address the concerns there.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper is well-motivated, clearly contrasting conventional feature-tuning paradigms with the proposed decision-centric approach and providing novelty to this task.\n2. The design of AAE and UDD is technically sound: AAE enhances representation quality while providing uncertainty cues, and UDD leverages entropy to arbitrate between semantic and geometric reasoning.\n3. The experimental evaluation is extensive, covering both intra-dataset and cross-dataset benchmarks, and demonstrates the effectiveness of the proposed framework.", "weaknesses": "1. Some ablations on hyperparameters are missing. For example, how sensitive is the method to the choice of the entropy scaling factor (Eq 12) in UDD?\n2. The uncertainty estimation relies solely on entropy from the semantic classifier. It would be useful to investigate alternative uncertainty measures and analyze their impact on performance.\n3. Missing related work. Several recent works on 3D few-shot learning are relevant and should be cited:\n   - Rethinking few-shot 3d point cloud semantic segmentation (CVPR 2024)\n   - Multimodality Helps Few-shot 3D Point Cloud Semantic Segmentation (ICLR 2025)\n   - Generalized Few-shot 3D Point Cloud Segmentation with Vision-Language Model (CVPR 2025)", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)