Geometric Feature Embedding for Effective 3D Few-Shot Class Incremental Learning
Authors: Xiangqi Li
Libo Huang
Zhulin An
Weilun Feng
Chuanguang Yang
Boyu Diao
Fei Wang
Yongjun Xu
Venue: ICML 2025 poster (2025)
Keywords: 3D point cloud
few-shot learning
class-incremental learning
Primary area: applications->computer_vision

Abstract: 3D few-shot class incremental learning (FSCIL) aims to learn new point cloud categories from limited samples while preventing the forgetting of previously learned categories. This research area significantly enhances the capabilities of self-driving vehicles and computer vision systems. Existing 3D FSCIL approaches primarily utilize multimodal pre-trained models to extract the semantic features, heavily dependent on meticulously designed high-quality prompts and fine-tuning strategies. To reduce this dependence, this paper proposes a novel method for **3D** **F**SCI**L** with **E**mbedded **G**eometric features (**3D-FLEG**). Specifically, 3D-FLEG develops a point cloud *geometric feature extraction module* to capture category-related geometric characteristics. To address the modality heterogeneity issues that arise from integrating geometric and text features, 3D-FLEG introduces a *geometric feature embedding module*. By augmenting text prompts with spatial geometric features through these modules, 3D-FLEG can learn robust representations of new categories even with limited samples, while mitigating forgetting of the previously learned categories. Experiments conducted on several publicly available 3D point cloud datasets, including ModelNet, ShapeNet, ScanObjectNN, and CO3D, demonstrate 3D-FLEG's superiority over existing state-of-the-art 3D FSCIL methods. Code is available at https://github.com/lixiangqi707/3D-FLEG.

Geometric Feature Embedding for Effective 3D Few-Shot Class Incremental Learning
Xiangqi Li 12 Libo Huang" !
Zhulin An
Weilun Feng' 12 Yongjun Xu
Chuanguang Yang'
Boyu Diao Fei Wang'
Abstract
1. Introduction
Capturing the 3D shapes of objects from point clouds has become increasingly critical across wide range of fields, including robotic automation, healthcare, and autonomous driving (Xiao et al., 2024; Xue et al., 2024; Xu et al., 2023; An et al., 2024b;a). In these applications, there is a grow- ing need for models that can continuously adapt to new object characteristics with limited samples while retaining the ability recognize previously encountered objects (Zhu et al., 2021; Ahmadi et al., 2024; Xing et al., 2025; Huang et al., 2024b; Wu et al., 2025). This growing demand has spurred the development of few-shot class incremental learn- ing (FSCIL) techniques specifically designed for 3D point clouds (Tan & Xiang, 2024; Cheraghian et al., 2025). How- ever, 3D FSCIL faces unique challenges, including catas- trophic forgetting, overfitting to new data, and domain gaps between synthetic training data and real-world scans (Tan & Xiang, 2024; Huang et al., 2024a). These challenges significantly increase the complexity of applying FSCIL to 3D point clouds (Chowdhury et al., 2022; Cheraghian et al., 2025).
3D few-shot class incremental learning (FSCIL) aims to learn new point cloud categories from limited samples while preventing the forgetting of previously learned categories. This research area significantly enhances the capabilities of self-driving vehicles and computer vision sys- tems. Existing 3D FSCIL approaches primar- ily utilize multimodal pre-trained models to ex- tract the semantic features, heavily dependent on meticulously designed high-quality prompts and fine-tuning strategies. To reduce this depen- dence, this paper proposes novel method for 3D FSCIL with Embedded Geometric features (3D-FLEG). Specifically, 3D-FLEG develops a point cloud geometric feature extraction module to capture category-related geometric characteris- tics. To address the modality heterogeneity issues that arise from integrating geometric and text fea- tures, 3D-FLEG introduces geometric feature embedding module. By augmenting text prompts with spatial geometric features through these mod- ules, 3D-FLEG can learn robust representations of new categories even with limited samples, while mitigating forgetting of the previously learned categories. Experiments conducted on several publicly available 3D point cloud datasets, in- cluding ModelNet, ShapeNet, ScanObjectNNN and CO3D, demonstrate 3D-FLEG's superiority over existing state-of-the-art 3D FSCIL methods. Code is available at https: /t/tt/ttttttt httpgtttthuuhh///go/gggg com/ 1 aiggiiiiiiin 000000333DFFF2
To address these challenges, Microshape (Chowdhury et al., 2022) proposed universal description language to miti- gate distribution differences. While this approach reduces knowledge forgetting and somewhat addresses data distri- bution discrepancies, it struggles with learning new cat- egories. Domsinn (Tan & Xiang, 2024) introduced distinct recognition methods for new and old categories by incorporating soft and hard-label replay strategies, further alleviating forgetting and enhancing learning capabilities for new categories. Given the extremely limited training sam- ple available for new categories, researchers have explored leveraging pre-trained foundation models to improve learn- ing while minimizing the forgetting of previously learned information (Zhou et al., 2025). The C3PR method inves- tigated how the knowledge from the CLIP model could be applied to 3D point cloud data by projecting point clouds into depth images from optimal angles and integrating model reprogramming paradigm, offering a new way to handle 3D objects using CLIP (Cheraghian et al., 2025). However, 2D foundation models like CLIP were not de-
'Equal contribution 'Institute of Computing Technology, Chi- nese Academy of Sciences, No. 158 Beiqing Road, Haidian Dis- trict, Beijing, 100095, China "No. 19(A) Yuquan Road, Shijing- shan District, Beijing, China, 100049, Correspondence to: Zhulin An <anzhulin@ ict.ac.en>.
Proceedings of the 42nd International Conference Machine Learning. Vancouver, Canada. PMLR 267, 2025. Copyright 2025 by the author(s).

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Geometric Feature Embedding for Effective 3D Few-Shot Class Incremental Learning
signed for 3D tasks and cannot directly process point clouds. Even when depth images are projected and fed into CLIP's image encoder for classification, CLIP's sensitivity to color and texture information leads to lower classification perfor- mance due to the absence of these details in the projected images (Wang et al., 2022). In contrast, 3D pre-trained foun- dation models, which are trained on large datasets of point -mattitee-textt pairs, are more adept handling point cloud data. Ahmadi et al. (Ahmadi et al., 2024) were the first to apply 3D foundation models to FSCIL, introducing an adaptive module that requires no additional training and a dual-cache system, significantly improving the capability of 3D -sionnlanguage models in incremental learning tasks. However, similar to the 2D counterparts, the performance of 3D pre-trained models heavily relies on high-quality text prompts and elaborate training strategies, especially when working with limited new samples (Sun et al., 2024a).
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Figure I. Comparison of 3D FSCIL results on "ModelNet - ScanObjectNN" datasets using various 'Prompt-Trainee Strat- egy' Combinations: (1) GPT-generated prompts combined with model reprogramming and canonical shape projection (Cheraghian et al., 2025), (2) 64 different prompt templates along with the alignment module and dual cache system (Ahmadi et al., 2024), (3) GPT-generated prompts together with the alignment module and dual cache System. (4) only a prompt template Along with the alignment module and dual cache system, (5) GPT-generated prompts together with embedded geometric features, (6) 64 dif- ferent prompt templates along with embedded geometric features, and (7) only prompt template with embedded geometric features.
As shown in the preliminary 3D FSCIL results in Fig. 1, the choice of prompt and training strategies significantly influences the effectiveness of the foundation model dur- ing the incremental learning phase. Surprisingly, a simpler prompt template outperforms more complex ones, resulting in an improvement of approximately 6% in recognition ac- curacy during the final phase. Manually crafting effective, elaborate prompts can be quite challenging, and intricate training strategies may unnecessarily increase model com- plexity. Based on this observation, we identify two critical challenges that need to be addressed to advance FSCIL on 3D point clouds: minimizing reliance on text prompts and enhancing the model's 's ability to learn robust feature repre- sentations. One promising approach is to embed geometric information into the prompts, an aspect that has not been fully explored in previous studies.
preserve the local geometric structure during high-to-low di- mensional embedding (Belkin & Niyogi. 2003). By captur- ing both global and local geometric characteristics, this ap- proach boosts the model's representation capabilities while minimizing reliance on text prompts. To further address modality heterogeneity, 3D-FLEG incorporates the geomet- ric feature embedding module. By leveraging multi-head attention mechanisms, this module enables the model to better integrate and utilize the extracted geometric features, thereby enhancing both the model's learning ability and the robustness of its feature representations. We validated 3D-FLEG on the ModelNet, ShapeNet, ScanObjectNN, and CO3D point cloud datasets, demonstrating superior perfor- mance in mean accuracy, harmonic mean accuracy, and accuracy drop percentage compared to existing 3D FSCIL methods (Wu et al., 2015; Chang et al., 2015; Uy et al., 2019; Reizenstein et al., 2021; Chowdhury et al., 2022; Tan & Xi- ang, 2024; Cheraghian al., 2025; Ahmadi et al., 2024). In summary, our contributions are threefold:
We propose a novel method called 3D Few-shot class in- cremental Learning method with Embedded Geometric fea- tures (3D-FLEG). This method aims to reduce dependence on text prompts while improving the model's 's capacity to learn robust feature representations by incorporating spatial geometric knowledge from 3D point cloud features.
Specifically, 3D-FLEG integrates geometric feature extrac- tion and geometric embedding modules. The geometric feature extraction module designed to isolate category- specific features and capture object spatial structures. Its core is the construction of dynamic geometric feature projec- tion clusters (DGPC) through clustering and dimensionality reduction. To select cluster centers in real-world point cloud datasets, which often contain noise and complex topolo- gies, we employ spectral clustering (Ng et al., 2001). This technique effectively identifies complex distributions and non-convex clusters in noisy data, ensuring the selection of representative centers for point clouds (Resani et al., 2025). Furthermore, 3D-FLEG utilizes Laplacian eigenmaps to
1.
We propose 3D-FLEG, a prompt semantic enhance- ment strategy based on point cloud geometric features. This approach not only mitigates the forgetting of old knowledge but also enables efficient learning of new category representations with minimal training data, thereby providing a novel solution for 3D few-shot class incremental learning.
2. We designed a framework with two innovative mod- ules: a geometric information extraction module that extracts geometric information from point cloud fea-
2

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Geometric Feature Embedding for Effective 3D Few-Shot Class Incremental Learning
tures, and a geometric information embedding module that efficiently fuses this information with text features.
2023). This approach has demonstrated significant potential across variety of point cloud processing tasks.
3. Our method achieves significant performance improve- ments in within-dataset and cross cross-dataset experiments, proving its effectivvness and robustness in handling FS- CIL on several publicly used 3D point cloud datasets.
2.2. Few-Shot Class Incremental Learning
Few-shot class incremental learning has garnered significant attention due to its high challenge and practical research significance (Tao et al., 2020; Cheraghian et al., 2021; Zhu et al., 2021; Peng et al., 2022; Ahmad et al., 2022; Liu et al., 2022; Kang et al., 2022; Kim et al., 2023; Liu et al., 2023; Lin et al., 2024; Sun et al., 2024b; Liu et al., 2024a; Wang et al., 2024; Park et al., 2024; Zheng et al., 2025). Mi- croshapes was the first study to explore FSCIL on 3D point clouds (Chowdhury et al., 2022). It highlighted that FSCIL tasks often rely on extensive synthetic data for training but require continuous learning of new real-world categories, introducing cross-domain discrepancy challenge. To ad- dress this, Microshapes introduced a universal description language to reduce data distribution differences. Meanwhile, employed a bual-branch architecture and a label replay strategy mitigate catastrophic forgetting (Tan & Xiang, 2024). Pre-trained foundation models, rich in knowledge, are well-suited to meet FSCIL challenges. The C3PR method explored the application of CLIP models to 3D point cloud processing by generating depth images from optimal projection angles and combining them with model reprogramming paradigms, offering a novel approach (Cheraghian et al., 2025). However, 2D models like CLIP have limitations when directly applied to point clouds, such as sensitivity to color and texture information, which can degrade classification performance. With the emergence of 3D pre-trained models, researchers have shifted towards using models specifically designed for point clouds. Foun- dationModel was the first to apply a 3D foundation model to FSCIL, introducing an adaptive module that requires no additional training and dual-cache system, significantly en- hancing the ability of 3D vision-language models to handle continual learning tasks (Ahmadi et al., 2024).
2. Related Work
2.1. Point Cloud Processing
As one of the primary representations of three-dimensional objects, point clouds have garnered increasing attention due to the growing demand for 3D object representation learning (Xiao et al., 2024). Current approaches to processing point clouds can be primarily categorized into voxel-based meth- ods, projection-basee methods, and point-based methods (Lahoud et al., 2022).
Specifically, voxel-based methods map point cloud data into 3D grid, simplifying computations and supporting convolutional operations. However, they lead to increased computational costs at high resolutions due to higher spatial complexity (Choy et al., 2019; Maturana & Scherer, 2015). Projection- Projection-based based methods, alternatively, project point clouds onto 2D planes or tangent planes to form multi-view rep- resentations, allowing the use of 2D CNNs for feature ex- traction. While this leverages powerful pattern recogni- tion, it can result in loss of depth information and relies on effective multi-view integration (Huang et al., 2023; Su et al., 2015). In contrast, point-based methods have gar- nered more attention due to their ability to learn features directly from unordered, variable-length data without trans- forming them into a regular structure. PointNet pioneered this field by proposing the use of Multi-Layer Perceptrons to independently process each point, combined with global max pooling to generate permutation iermutation-invariant invariant features (Qi et al., 2017a). Its successor, PointNet+++ further developed hicrarchical local feature aggregation strategies, enhancing the capture of local geometric structures (Qi et al., 2017b). To improve feature expressiveness, researchers have drawn inspiration from successful models in the 2D domain, such as CNNs, Graph Neural Networks, and BERT, developing advanced point cloud encoders that significantly boost the capability to understand and process point clouds (Li et al., 2018; Wang et al., 2019; Yu et al., 2022; Liu et al., 2019; Wu et al., 2019). Leveraging these mature point cloud encoders, there has recently been an emergence of foundational 3D models aimed at constructing a unified three-dimensional representation (Zhou et al., 2023; Xue et al., 2023; Liu et al., 2024b; Xue et al., 2024; Zhang et al., 2024; Qi et al., 2024; Zhang et al., 2023). Among these, the Uni3D model uti- lizes pre-trained Vision Transformers and is trained in an end-to-end manner, achieving effective alignment between 3D point cloud features and image-text features (Zhou et al.,
Despite their potential to effectively address FSCIL chal- lenges, the performance of 3D pre-trained models still de- pends on high-quality text prompts and sophisticated train- ing strategies. Therefore, there is need for new training approaches. These new methods should minimize this de- pendence and fully unleash the latent capabilities of these models. To this end, we propose a method based on point cloud geometric feature embedding. This approach effec- tively addresses catastrophic forgetting and cross-domain discrepancies, thereby improving the model's 's ability to learn effective feature representations from limited new category data.
3

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task Geometric Feature Embedding for Effective 3D Few-Shot Class Incremental Learning
Geometric Feature Extraction Module
Point Cloud
fr()
Point Cloud Features
Point Cloud Encoder
featPotnt,
/D()
Dynamic Projection C Clester
Point Cloud
Spectral Clustering
Laplacian Eigenmaps
Features
SeutPoint,
(b,, Bg. D
(P.
Similarity Classification
Geometric Feature Embedding Module
Text Prompt
point cloud model of (
G Gomeetric
f
Transformed
Features
Features
apple ball banana
featGeo,
Transformer Encoder
feaiTrans,
Fusion
Enhanced
Text Features
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Text Features
Expanded Text Features
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Text Encoder
Figure 2. Overall pipeline of 3D-FLEG. The process begins with point cloud data {P.) and category descriptions formatted using the template "a point cloud model of a }", denoted as (TB 1 A pre-trained frozen 3D encoder extracts initial features from the point cloud data, while frozen encoder generates corresponding eigenmaps to construct dynamic geometric feature projection clusters that are specifically designed to continuously adapt to novel data distributions. Leveraging learnable attention weights for basis vectors, these dynamic projection clusters serve purpose of extracting geometric information. Subsequently, the extracted geometric features are then processed by a trainable Transformer encoder to generate semantically rich representations. Finally, the model fuses geometric expanded text features comprehensive embeddings. During inference, it optimizes parameters by minimizing cross-entropy loss between the original point cloud features and enhanced text features.
features. Next, the model employs spectral clustering and Laplacian
3. Method
dataset for 21. For subsequent tasks (t > 1), training uses limited real-world 3D point clouds per new class. Training proceeds sequentially from f = 1 to = T, with the model receiving X,, Vt. and prompt descriptions up to the current P.). After training on Q., the model must classify test samples from all tasks up to 2,, ensuring retains knowledge of previous classes while learning new ones. This formulation evaluates the model's ability to handle incremental learning challenges, especially when new classes have limited data, balancing old knowledge retention and new information acquisition (Chowdhury et al., 2022; Cheraghian et al., 2025).
3.1. Problem Formulation
3D FSCIL faces sequence of T tasks, Q (Q1, Q2.... Qr), where 21 is the base task and 2....., 2r are novel tasks introduced incrementally. Each task Q, has distinct set of classes G,, ensuring C; nC, 0 for any # j. Each class within task includes a prompt description P.. Thus, each task Q, can be represented as a set of tuples {(Nis Vos, (ts, where:
X = [ 1 denotes the i-th 3D point cloud with 4 points in 1
3.2. Model Overview
Yta C, is the class label.
Our proposed method, 3D-FLEG, is illustrated in Fig. 2. It begins with data preparation and initialization and takes as input point cloud data ( and a list of categories with predefined templates {2, { where N is the number of point clouds, M is the number of categories. A pre-
Pu Pr is the class prompt description.
n; represents the number instances per task Q..
In 3D FSCIL., the model initially trains on a large synthetic

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Geometric Feature Embedding for Effective 3D Few-Shot Class Incremental Learning
trained 3D base model's point cloud encoder (p( extracts initial features {featt Base)
L selects the top l smallest non- zero eigenvalues' corresponding eigenvectors to form matrix U. Each row of U' is normalized to obtain the final feature representation Y = [y1. V2.... ..... ukl". K-means clustering on Y achieves the final clustering result C.
Eigendecomposition
(fr(P.)) while text encoder fr(-) generates corresponding text features {featT e Subsequently, the model constructs dy- namic geometric feature projection clusters for the base- class point clouds. Initial features are clustered using spec- tral clustering combined with K-means, followed by di- mensionality reduction via Laplacian eigenmap to compute basis vectors B (br: b,,..,.. b&)- Upon the arrival of new classes, these clusters adapt to new distributions, with each basis vector b, associated with learnable weight wi- Point cloud features feat are then dot-multiplic with basis vectors and aggregated through a weighted sum to extract geometric information SealGeo,. In the geomet- ric feature embedding module, geometric features are pro- cessed by Transformer encoder with self-attention layers to generate semantically enriched feature representations PeatTrans;; Text features are integrated by concatenat- ing featI'rans; with expanded, dimension-matched text features {feett ext, y resulting in the final fused feature (featFinal, j=1
For Laplacian eigenmap dimensionality reduction, a set of basis vectors B = {b1. be.,..,b,, is calculated from the clustering results. The generalized eigenvalue problem Le = AD2 is solved, where 2 represents eigenvectors and À represents eigenvalues. Eigenvectors corresponding to the top & smallest eigenvalues form a matrix Z, which is standardized to obtain the basis vector matrix B.
When new classes arrive, the geometrie feature projection cluster B is updated based on an update rate parameter to adapt new distributions. For test samples, each basis vector b, E B has learnable weight w; W. Geomet- ric information is aggregated via a weighted sum to get geometric features featGeo;;
featGieo; = Σ wy (featPoint; b2)-
(4)
During inference, 3D-FLEG compares original point cloud features featPoint, with the enhanced text features {featFinnll, JM j= guided by cross-entropy loss C for op- timization. By minimizing this loss, the model refines its parameters to better accommodate evolving data distribu- tions and improve classification accuracy.
Spectral clustering, based on spectral analysis from graph theory, is well-suited for capturing data distributions with complex topological structures and is robust to noise. Lapla- cian eigenmaps, as manifold learning technique, preserve the local geometric properties of the point clouds. By com- puting the weighted similarity between point cloud features and dynamic projection clusters, we extract geometric fea- tures that exhibit good invariance to different viewpoints and scale transformations.
3.3. Geometric Feature Extraction Module
In 3D-FLEG, we introduce geometric feature extraction module designed to extract more representative and stable high-level geometric attributes from raw point cloud fea- tures, serving the task of text enhancement. This module first uses spectral clustering to select cluster centers from the base-class point cloud features and then applies gener- alized eigenmaps for dimensionality reduction, generating dynamic projection clusters.
3.4. Geometric Feature Embedding Module
For 3D FSCIL, we have designed a novel geometric fea- ture embedding module aimed at fully utilizing the rich spatial and geometric information from point cloud data and the semantic information from text descriptions. This module optimizes the processing of geometric features to ensure that data from both modalities interact similar levels of abstraction, thereby achieving more effective in- formation integration. Specifically, 3D-FLEG incorporates both adaptiveAvgPoolld and AdaptiveMaxxPoll to ex- tract features at different granularity levels. In our design, we leverage these pooling methods to effectively capture both fine-grained and coarse-grained characteristics of the input data:
For base-class point cloud data, we extract an initial set of point cloud features [featBase, | (f.) Spectral clustering on feat Base yields cluster assignments C, with K-means applied internally for final clustering. The affinity matrix Win is constructed as follows:
Wi = exp (- ||2; - 12,1?
(1)
202
The degree matrix D has diagonal elements given by:
D& = Σ Wiy
(2) F, =
MaxPool (5)
Here, Fp combines both levels of detail from the geometric features. Next, F, undergoes linear projection and is passed through a Transformer-base multi-head attention mech- anism to capture long-range dependencies and optimize
Normalization of the Laplacian matrix T. is performed as:
= I D- D-!WD-!
5

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Geometric Feature Embedding for Effective 3D Few-Shot Class Incremental Learning
feature relevance. The resulting features are further pro- cessed through fully connected layer to adjust the feature space. These transformations refine the geometric features and prepare them for fusion with text features. We denote the final transformed feature as feat rans;.
4. Experiment
4.1. Dataset Partitioning and Evaluation Metrics
Dataset partitioning. For dataset partitioning, we first conducted within-dataset experiments to establish baseline performance. Using ModelNet, we allocated 20 base classes with the remaining 20 classes divided into four incremen- tal stages. For ShapeNet and CO3D, we utilized 25 base classes, distributing the incremental classes across 7 or 6 tasks respectively, comprising either 30 or 25 incremental classes. To simulate limited real-scanned data for new cate- gories, we also designed cross-dataset experiments. For the transition from ModelNet to ScanObjectNN, we followed (Chowdhury al., 2022) with four tasks. In the case of ShapeNet to ScanObjectNN, we structured four tasks in- volving 44 ShapeNet base classes and 15 ScanObjectNN incremental classes. Lastly, for ShapeNet to CO3D, we set up eleven tasks with 44 ShapeNet base classes and 50 CO3D incremental classes, representing the most challeng- ing setup.
featTrans, (Fp),
(6)
where fTy denotes the processing through a Transformer encoder. The fused feature feat Finaly is obtained by con- catenating processed geometrie features featTrans, with dimension-matched expanded text features feat ExpT ExpTextt followed by an activation function:
featFinnal, tanh ([featTrans; feat Exrp (eoffeerr))) exr,,)) (7)
where feat ExpT ext; refers to text features expanded to align with the geometric feature dimensions, facilitating their effective fusion.
This structured approach ensures effective transformation and integration of geometric features with textual informa- tion, leading to improved model performance and adaptabil- ity.
Evaluation metrics. In our experiment, three key metrics:
3.5. Training Pipeline
We calculate overall accuracy af- ter each incremental step, covering both base and new classes. This measures the proportion of correctly clas- sified samples out of the total number of samples.
Average Accuracy:
During model training, both the point cloud encoder and the text encoder remain frozen. Only the dynamic geometric fea- ture projection clusters the geometric feature extraction module, the weights of each basis vector within them, and the parameters of the geometric feature embedding module trained. 3D-FLEG primarily relies on two key com- ponents: the geometric feature extraction module and the geometric feature embedding module. In the geometric fea- ture extraction module, the core step involves constructing initial dynamic geometric feature projection clusters using base-class data. Notably, during the incremental learning phase, we introduce an update rate to adjust the dynamic projection clusters and train the corresponding weights for each basis vector to adapt to the new data distribution. For each input sample, the dynamic geometric feature projection clusters extract category-relevant geometric features from the raw point cloud features. Subsequently, these geometric features are transmitted to the geometric feature embedding module for semantic enhancement of text features. Finally, 3D-FLEG is trained using a loss function that computes the categorical cross-entropy loss between the original point cloud features and the enhanced text features:
Correct samples
Average Accuracy
(9)
Total samples
Relative Accuracy Drop Rate (A): We introduce A to quantify performance changes during incremental learning:
acCT acCn
A
x 100,
(10)
acco
where accr and acco are the accuracies of the last and first incremental tasks, respectively. Lower A values indicate better stability.
Harmonic Accuracy (A₂): To balance performance old and new classes, especially given the limited data for new classes, adopt harmonic accuracy:
2 x As x An
A
(11)
AB + A,,
N
k
gsk log(Eux)
(8)
N
where As and A, are the accuracies of base and new classes, respectively. Higher A, indicates better bal- ance between old and new class performances. Addi- tionally, we report separate accuracies for base and new classes at each incremental stage for detailed analysis (Peng et al., 2022).
where gi denotes the one-hot encoded ground-truth la- bel vector, and au represents the predicted class prob- ability distribution generated from the fused features {f Final, M M
6

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Geometric Feature Embedding for Effective 3D Few-Shot Class Incremental Learning
Table 1. Average accuracy within single dataset.
ModelNet
CO3D
ShapeNet
Method
20
30
40
25
30
50
25
35
45
55
FT
89.8
9.7
4.3
3.0
96.7
76.7
11.2
3.6
3.2
1.8
0.8
99.0
87.0
25.7
6.8
1.3
0.9
0.4
99.5
Joint
89.8
88.2
87.0
83.5
80.5
10.4
76.7
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
LwF
89.8
36.0
9.1
3.1
96.0
76.7
14.7
4.7
3.5
2.3
1.0
98.7
87.0
60.8
33.5
15.9
3.8
1.8
97.9
11.2M
89.8
65.5
58.4
52.3
53.6
40.3
76.7
31.5
27.7
18.1
27.1
21.9
71.4
87.0
58.6
45.7
40.7
50.1
49.4
49.3
43.3
ScalL
89.8
66.8
64.5
58.7
56.5
37.1
76.7
39.5
34.1
24.1
30.1
27.5
64.1
87.0
56.6
51.8
44.3
50,3
46.3
45.4
47.8
FEIL
89.8
75.4
67.2
60.1
55.6
38.1
76.7
61.4
52.4
42.8
39.5
32.8
57.2
87.0
71.7
73.2
69.3
66.4
65.9
65.8
22.4
FACT
90.4
77.1
65.0
77.9
59.7
54.8
46.7
87.5
71.4
67.5
62.5
28.6
Sem-aware
91.3
82.2
74.3
70.0
64.7
29.1
78.6
66.9
59.2
53.6
49.1
42.9
44.1
87.2
74.9
68.1
69.0
68.1
66.9
63.8
26.8
Microshape
93.6
83.1
78.2
75.8
67.1
28.3
78.5
67.3
60.1
56.1
51.4
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
72.2
70,9
22.5
81.5
69.4
66.5
63.0
54.2
53.8
34.0
88.0
81.6
77.8
76.7
76,9
76.2
74.7
15.1
3D-FLEG((urs)
98.3
95.5
93.5
87.3
82.1
75.4
69.7
66.7
57.7
57.3
30.2
94.3
89.7
88.4
86.2
83.0
12.0
Table 2. Average accuracy across datascts.
ShapeNet COLD
ModelNet - SeanOhjectNN
ShapeNet - SeanOhject
Method
44
49
59
64
69
74
84
89
30
34
37
4
44
49
54
A 4
FT
81.0
20.2
2.3
1.0
1.0
1.3
0.5
1.6
98.0
88.4
6.4
6.0
97.9
81.4
38.7
98.9
Joint
81.0
79.5
78.3
75.2
75.1
74.8
72.3
71.3
70.0
68.8
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
81.0
57.4
19.3
2.3
1.0
0.9
0.8
1.3
1.1
0.8
1.9
97.7
88.4
35.8
5.8
2.5
97.2
81.4
47.9
14.0
5.9
92.8
112MM
81.0
45.6
36.8
35.1
31.8
33.3
34.0
31.5
30.6
32.3
30.0
63.0
88.4
58.2
52.9
52.0
41.2
81.4
53.2
43.9
45.8
43.7
ScalL
81.0
50.1
45.7
39.0
37,9
38.0
36.0
33.0
35.2
56.5
56.5
55.9
52.9
81.4
49.0
46.7
40.0
50,9
BEIL
81.0
75.2
69.3
63.2
60.5
57.9
53.0
51.9
51.3
47.8
47.6
41.2
88.4
70.2
61.0
56.8
35.7
81.4
74.5
69.8
63.4
22.1
FACT
81.4
76.0
70.3
68.1
65.8
63.5
63.0
60.1
58.2
57.5
55.9
31.3
89.1
72.5
68.3
63.5
28.7
82.3
74.6
69.9
66.8
18.8
Sem-aware
80.6
69.5
66.5
62.9
63.2
63.0
61.2
58.3
58.1
57.2
55.2
31.6
88.5
73.9
67.7
64.2
27.5
81.3
70.6
65.2
62.9
22.6
Microshape
82.6
77.9
73.9
72.7
67.7
66.2
65.4
63.4
60.6
58.1
57.1
30.9
89.3
73.2
68.4
65.1
27.1
82.5
74.8
71.2
67.1
18.7
C3PR
83.6
80.0
77.8
75.4
72.8
72.3
70.3
67.9
64.9
64.1
63.2
24.4
88.3
75.7
70.6
67.8
23.2
84.5
77.8
75.5
71.9
14.9
FoundationModel
87.3
86.2
84.4
82.2
80.7
79.6
78.2
76.8
76.1
74.5
72.6
16.8
87.7
84.7
81.5
79.2
9.7
90.8
86.5
86.4
85.6
5.7
3D-FLEGGours)
91.7
90.5
89.0
86.8
84.9
83.3
82.3
81.1
79.8
77.8
76.8
16.2
93.8
91.9
87.5
86.8
7.5
90.9
89.1
87.1
86.3
5.1
4.2. Implementation Details
4.3. Overall Experiment
In the implementation of the incremental stages, we ran- domly selected five samples per category and retained one sample from previously learned categories, simulating prac- tical data scarcity. The dynamic geometric feature projec- tion cluster contains 1024 base vectors and has an update rate of 0.1 during the incremental phase. For all samples, we select 1024 points from the 3D point cloud objects using the farthest point sampling method as the input. Specifi- cally, considering the computational overhead and model performance, our experiment is configured the same as the (Ahmadi et al., 2024). We employed the ---02---11+* CLIP model and the "eva02-base 2at002bbseepptcch4.... 44.488 model our point cloud encoder. The Transformer encoder com- prises 2 standard layers, each with 8-head self-attention. As indicated by Uni3D, while more training parameters can lead to better training results, it also incurs greater computa- tional overhead (Zhou al., 2023). Therefore, we opted for the basic version of the point cloud encoder. Additionally, conducted
Compared methods. In this section, we evaluate a series of methods for FSCIL on 3D point clouds. These methods en- compass fine-tuning and joint training, where fine-tuning up- dates the model using only data from new categories sim- plify the incremental learning process, while joint training allows the model to simultaneously access data from all cat- egories to mitigate forgetting issues. Additionally, we adapt common 2D FSCIL methods such as 11.2M, Scall., EEIL,, LwF, and FACT by replacing their CNNs with PointNet to accommodate point cloud data, exploring the applicabil- ity of established 2D techniques in 3D contexts(Belouadah & Popescu, 2019; 2020: Castro et al., 2018; Li & Hoiem, 2017; Zhou et al., 2022; Cheraghian et al., 2021). Finally, we examine specialized strategies specifically designed for 3D FSCIL tasks, addressing unique challenges associated with point cloud data (Chowdhury et al., 2022; Tan Xiang, 2024; Cheraghian et al., 2025; Ahmadi et al., 2024).
Experimental results and analysis. In Tab. 1 and Tab. 2, present the experimental comparison results for within-
the entire experimental process NVIDIA A 100 GPU. The optimizer utilized is the AdamW optimizer, with the weight decay set to 1 x 4 During the basic category training, we trained for 10 epochs with a learning rate of 0.0005. For the new categories, we in- creased the training to 50 epochs and set the learning rate to 0.001, maintaining fixed batch size of 32.
single
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Geometric Feature Embedding for Effective 3D Few-Shot Class Incremental Learning
Table 3. Effectiveness of the dynamic geometric feature projection
Harmonic Accuracy (%)
Harmonic Accuracy (%)
80
10
clusters and attention weight in 3D-FLEG.
Average Accuracy (%)
Harmonic Accuracy (%)
80
DGPC
Attention Weight
3
1
2
3
93.8
84.1
75,4
76.2
20
93.8
91.6
86.7
84.9
86.4
76.5
75.4
86.8
87.0
77.4
77.5
Session
Session
Figure 3. Experimental results of harmonic accuracy on ModelNet ScanObjeetNN (Left) and ShapeNet + ScanObjectNN (Right).
mean metric evaluation index and conducted experiments on two cross-datasets. As shown in Fig. 3, 3D-FLEG out- performs current state-of-the fif-the-rtt methods in all scenarios, which fully demonstrates that it can not only better reduce the forgetting of old knowledge but also efficiently learn the feature representations of new classes using limited samples.
dataset and cross-dataset scenarios. These results indicate that while many existing incremental learning methods per- form admirably with 2D images, they tend to exhibit subopti- mal performance when applied to 3D point cloud data. This shortfall primarily stems from their inability effectively address catastrophic forgetting and the distributional shifts between different data sources. In contrast, methods specifi- cally designed for few-shot incremental learning on point clouds, such as Microshape, demonstrate superior perfor- mance. By leveraging the powerful pre-training knowledge of the base model, C3PR and FoundationModel have also shown enhanced capabilities in mitigating forgetting and learning new categories. Our proposed method, 3D-FL.EG, advances this further by seamlessly integrating geometric features of point clouds with text prompts, thereby augment- ing the capabilities of 3D foundation models. In experi- ments conducted within a single dataset, 3D-FLEG not only demonstrates significantly smaller drop in accuracy but also achieves approximate 4% increase in accuracy over existing state-of-the- art methods at each incremental stage. In the more challenging cross-datases experiments, our ap- proach also significantly outperforms the existing optimal solutions and achieves the best performance.
4.4. Ablation Study
To evaluate the effectiveness of our proposed method and its individual modules, we conducted a series of ablation studies on the cross-dataset task from ModelNet to ScanOb- jectNN. During the evaluation, we utilized average accuracy and harmonic accuracy as our metrics.
Evaluating component effectiveness in 3D-FLEG. To rig- orously evaluate the effectiveness of each component in 3D- FLEG, we conducted a series of detailed experiments from three perspectives: assessing the efficacy of the geomet- ric feature extraction module based on dynamic projection clusters, validating the approach for constructing dynamic projection clusters, and evaluating the performance of the geometric feature embedding module.
As illustrated in Tab. 3, incorporating the dynamic geomet- ric feature projection clusters significantly enhances the model's ability to integrate point cloud and textual features, markedly outperforming direct fusion of raw features, which is shown as the first row in the table. By introducing an atten- tion mechanism that assigns differential attention weights to each basis vector within the projection cluster, 3D-FLEG further refines feature representation. Learnable weights prioritize cluster centers relevant to incremental tasks, en- suring that the most pertinent geometric features are empha- sized, improving robustness and adaptability. This approach enhances stability in the presence of noise, outliers, and viewpoint changes. Furthermore, we substantiated the effec- tiveness of the implementation approach for the geometric feature extraction module, and the results are illustrated in Fig. 4. In 3D-FLEG, we employed spectral clustering and Laplacian eigenmaps to process point cloud features. Exper- iments demonstrated that the dynamic projection clusters constructed by combining these two methods exhibit supe- rior performance in 3D few-shot class incremental learning, To verify the effectiveness and efficiency of the proposed geometric feature embedding module, we conducted abla- tion studies by replacing with long short-term memory
is worth noting that since the absolute values of the aver- age accuracy recorded in the tables may be affected by the training effect of the base model, it cannot comprehensively measure the specific performance of the model during the incremental stage. To more accurately reflect the forgetting situation of the model during the incremental learning stage, we further calculated the accuracy drop of each incremental stage compared to the base model. For detailed information, please refer Tab. 5 and Tab. 6 in the Appendix A.
In addition, to further highlight the accuracy of our method in classifying new categories, we adopted the harmonic
'The experimental results for the comparison methods pre- sented in the table are derived from the studies in (Ahmadi et al., 2024) and (Cheraghian et al., 2025), with all experiments con- ducted under consistent settings. Note that the FoundationModel was excluded from Tab. because its original publication did not report results within the dataset, and notable performance discrep- ancies were encountered during our reproduction attempts.
8

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Geometric Feature Embedding for Effective 3D Few-Shot Class Incremental Learning
Gramel Chrering 1AB
Zoar Modile
Harmantc BCC Accuuricty 1NC
Hormselc Accurniy (h4
Accuory
Session
Session
Session
Session
Figure 4. Evaluating spectral clustering and Laplacian eigenmaps Figure 5. Validation of the geometric feature embedding module. for geometric feature extraction module.
at 1024. In addition, we further conducted experiments to in- vestigate the impact of dynamically adjusting the update rate of projection clusters during the incremental learning phase on model performance. Detailed analyses are provided in the Appendix B.
Table 4. Impact of Basis Vector Count in Dynamic Projection Clus- ters on Experimental Results.
Average Accuracy (%)
Harmonic Accurucy (%)
Basis Vectors Count
0
1
2
3
2
3
256
93.4
91.5
86.4
85.0
86.8
76.2
76.8
512
91.2
91.5
86.3
85.9
76.2
77.1
1024
93.8
91.9
87.5
86.8
87.0
77.4
77.5
5. Conclusion
2048
93.6
91.6
87.5
86.7
87.3
77.7
77.7
4096
93.8
91.9
86.4
77.7
78.3
This paper proposes 3D-FLEG, a novel solution to the chal- lenging and practical problem of few-shot class incremental learning on 3D point clouds. Previous studies leverage well-designed prompts and complex strategies to extract the pre-training knowledge, neglecting the inherent geo- metric features embedded in the point clouds. To address these issues, 3D-FLEG introduces the geometric feature extraction module and the geometric feature embedding module. These two modules work synergistically to build text semantic enhancement strategy based on point cloud geometric features, enabling 3D-FLEG reduce reliance text prompts and enhance the model's capacity to learn robust feature representations while mitigating the forget- ting problem. Experiments on four widely used 3D datasets demonstrate that 3D-FFLEG outperforms existing methods, validating its effectiveness and robustness. Despite the ex- cellent performance of 3D-FLEG in 3D FSCIL, further advanced geometric feature extraction and embedding meth- ods are worth exploring. Extending 3D-FLEG broader 3D vision applications, i.e., 3D object detection and scene segmentation. is also attractive to the community.
networks (LSTM) or full connect neural network (FCNN). As shown in Fig. 5, our approach achieved better geometric feature embedding results. By introducing local and global pooling along with Transformer-based modules, our method optimizes feature relevance and reduces redundancy, min- imizing unnecessary noise interference. This enables the model to learn more meaningful representations.
Hy Hyperparameter sensitivity. In our method, the number of basis vectors contained in the dynamic projection clus- ter and the update rate of the dynamic projection cluster in the incremental phase are two key parameters. We further explored the impact of different parameters on the experi- mental results through experiments.
In Tab. 4, we investigate the impact of the number of basis vectors in dynamic projection clusters on the experimen- tal results. The experimental results show that when the number increases to 1024, the performance of the model in the basic stage and the incremental stage tends to be sta- ble. This phenomenon can be attributed to the fact that the base model we use has a feature space of 1024 dimensions. When the number of basis vectors does not align with this feature dimension, additional modules must be introduced to compress or expand the original features to match the required dimensionality. However, this process not only increases model complexity but can also lead to the loss of critical information within the point cloud features or the introduction of redundant information. Therefore, in our experimental setup, to maintain consistency and integrity in feature representation, the number of basis vectors is fixed
A Acknowledgments
This work is partially supported by the Beijing Natural Science Foundation under Grant Number 4244098, the Na- tional Natural Science Foundation of China under Grant Number 62476264 and 62406312 12, the Postdoctoral Fellow- ship Program and China Postdoctoral Science Foundation under Grant Number BX20240385 (China National Post- doctoral Program for Innovative Talents), and the Science Foundation of the Chinese Academy of Sciences.

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Geometric Feature Embedding for Effective 3D Few-Shot Class Incremental Learning
Impact Statement
Su, H., al. Shapenet: An information-ricc 3d model repository. arXiv preprint arxiv:/5/2.0330122 2015.
3D-FLEG provides a powerful new approach for 3D few- shot class incremental learning. not only advancing relevant technologies in the field of machine learning but also signif- icantly boosting applications that rely on 3D data analysis, such medical imaging, autonomous driving technologies, and virtual reality. In the long term, 3D-FLEG is expected to foster continuous innovation in these fields, enhancing the quality and efficiency of products and services based on 3D data.
Cheraghian, A., Rahman, S., Fang, P., Roy, S. K., Peters- son, L., and Harandi, M. Semantic-aware knowledge distillation for few-shot innsssincementall learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2534-2543, 2021.
Cheraghian, A., Hayder, Z., Ramasinghe, S., Rahman, S., Jafaryahya, J.. Petersson, L., and Harandi, M. Canonical shape projection is all you need for 3d few-shot class incremental learning. In European Conference on Com- puter Vision, pp. 36-53. Springer, 2025.
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Xiao, A., Zhang, X., Shao, L., and Lu, S. A survey of label-efficient deep learning for 3d point clouds. IEEE Transactions on Pattern Analysis and Machine Intelli- gence, 2024.
Zhou, J., Wang, J., Ma, B., Liu, Y.S.,, Huang, T., and Wang, X. Uni3d: Exploring unified 3d representation at scale. arXiv preprint 10X0022310.0677,, 2023.
Xing, M., Cheng, L., Tang. S., Wang, Y.. Zhong, Z., and Wang, M. Knowledge swapping via learning and unlearn- ing. arXiv preprint arXiv:2502.0800 08075, 2025.
Zhu, K., Cao, Y., Zhai, W., Cheng, J., and Zha, Z.-.. Self- relf-promoted prototype refinement for few-shot class- incremental learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6801-6810, 2021.
Xu, Y., Hu, C., Zhao, N., and Lee, G. H. Generalized few- shot point cloud segmentation via geometric words. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 21506-21515 2023.
Xue, L., Gao, M., Xing, C., Martin-Martin R., Wu, J., Xiong, C., Xu, R., Niebles, J. C., and Savarese, S. Ulip: Learning a unified representation of language, images, and point clouds for 3d understanding. In Proceedings of the JEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1179-1188 1889, 2023.
12

---

Geometric Feature Embedding for Effective 3D Few-Shot Class Incremental Learning
94
updateRate
updateRate
updateekate - 0.05
updateRate 0.05
updateilttte = 0.1
updateRate 0.1
Average Accuracy (%)
updateRate = 0.15
updateRate 0.15
updatoRate 0.2
updatofatte 0.2
updateRate 0.25
Harmonic Accuracy
84
updateRate 0.25
updateRate
updateRate
90
84
Session
Session
Figure Impact dynamic projection cluster update rate during incremental learning phase on model performance.
Table 5. Relative accuracy drop within single dataset
ModelNet
CO3D
ShapeNet
Method
20
35
Mean
30
Mean
25
50
Meun
FT
89.2
95.2
96.3
96.7
94.4
85.4
95.3
95.8
97.7
99.0
94.6
70.5
92.2
98.5
99.0
99.3
99.5
93.2
Joint
7.0
10.4
5.6
9.5
18.3
5.5
LwF
59.9
89.9
96.0
96.5
85.6
80.8
93.9
95.4
97.0
98.7
93.2
30.1
61.5
81.7
95.6
96.4
97.9
77.2
HL2M
41.8
36.0
58.9
76.4
64.7
53.2
43.2
43.7
ScalL
25.6
28.2
34.6
37.1
31,4
48.5
55.5
68.6
60.8
64.1
59.5
34.9
40.5
49.1
42.2
46.8
47.8
43.6
EEIL
16.0
25.2
33.1
38.1
28.1
19.9
31.7
44.2
48.5
57.2
40.3
10.7
15.9
20.3
23.7
24.3
24.4
19.9
FACT
10.1
14.7
18.7
28.1
17.9
13.9
23.4
29.7
35.6
40.1
28.5
13.9
18.4
20.1
22.9
24.9
28.6
21.5
Sem-aware
10.0
23.3
20.2
14.9
31.8
37.5
21.9
20.9
23.3
21.5
Microshape
11.2
16.5
19.0
28.3
18.8
14.3
23.4
28.5
34.5
28.1
5.0
7.0
9.8
12.3
16.1
17.1
11.2
C3PR
10.2
17.2
21.2
22.6
17.8
14.8
18.4
22.7
33.5
34,0
24.7
7.3
11.6
12.8
12.6
13.4
15.1
12.1
3D-FLEG(ours)
2.8
4.9
7.0
11.2
6.5
8,2
15.1
18.8
29.7
30.2
20.4
3.1
4.9
5.4
6.3
8.6
12.0
6.7
Table 6. Relative accuracy drop rate across datasets.
ShapeNet CO3D
ModelNet + ScanObjecINN
ShapeNet + ScanObjectNN
Method
39
54
59
69
74
89
Mean
26
34
37
Mean
54
59
Mean
FT
75.1
97.2
97.9
98.8
98.8
98.4
99.4
98.0
96.2
92.8
93.2
97.9
94.6
52.5
95.1
98.9
82.2
Joint
1.9
3.3
7.2
7.3
7.7
10.7
12.0
13.6
15.1
16.9
9.6
9.8
16.3
19.5
15.2
-1.4
2.0
3.3
1.3
LwF
29.1
76.2
97.2
98.8
98.9
99.0
98.4
98.6
99.0
97.7
89.3
59.5
93.4
97.2
83.4
41.2
82.8
92.8
72.3
IL2M
43.7
54.6
56.7
60.7
58.0
61.1
62.2
60.1
63.0
57.9
34.2
40.2
41.2
38.5
34.6
46.1
43.7
41.5
ScaIL
38.1
43.6
51.7
51.9
53.1
55.6
59.3
56.5
52.1
36.8
40.2
37.7
39.8
42.6
50.9
44.4
BEIL
7.2
14.4
22.0
25.3
28.5
34.6
35.9
36.7
41.0
41.2
28.7
20.6
31.0
35.7
29.1
8.5
14.3
22.1
15.0
FACT
13.6
16.3
19.2
22.0
22.6
26.2
29.4
31.3
21.6
23.3
28.7
23.5
15.1
18.8
Sem-aware
13.8
17.5
22.0
21.6
21.8
24.1
27.7
27.9
29.0
31.5
23.7
16.5
23.5
27.5
22.5
13.2
19.8
22.6
18.5
Microshape
5.7
10.5
12.0
18.0
19.9
20.8
23.2
26.6
29.7
30,9
19.7
18.0
23.4
27.1
22.8
9.3
13.7
18.7
13.9
C3PR
9.8
12.9
13.5
15.9
18.8
22.4
23.3
24.4
15.2
20.0
23.2
19.2
10.7
14.9
11.2
FoundationModel
1,3
3.3
5.8
7.6
8,8
10.4
12.0
12.8
14.7
16.8
9.4
3.4
7.1
9.7
6.7
4.7
4.8
5.7
5.1
(D-FLEG(ours))
1.3
2.9
5.3
7,4
9.2
10.3
11.6
13.0
15.2
16.2
9.2
2.0
6.7
7.5
54
2.0
4.2
5.1
3.8
A. Appendix for Experimental Results and Analysis.
To more accurately reflect the model's forgetting behavior during incremental learning, further calculated the accuracy drop relative to the base classes at each incremental stage. The experimental results are presented in Tab. 5 and Tab. 6. These tables demonstrate that 3D-FLEG consistently exhibits competitive performance across every incremental stage. By embedding geometric information, our method effectively mitigates the forgetting previously learned knowledge while efficiently learning new class representations with only a minimal number new samples. This approach offers a novel solution for few-shot class-incs class-incremental learning on point clouds.

---

Geometric Feature Embedding for Effective 3D Few-Shot Class Incremental Learning
B. Appendix for Hyperparameter Sensitivity
In our method, the update rate of the dynamic projection cluster in the incremental phase and the number of basis vectors contained in the dynamic projection cluster are two key parameters. In this session, we conducted experiments to investigate the impact of dynamically adjusting the update rate of projection clusters during the incremental learning phase on model performance. As illustrated in Fig. 6, setting appropriate update rate during the incremental phase can further enhance model performance. In particular, when there are discrepancies between the data distributions of base classes and new classes, static base vectors, lacking an updating mechanism, gradually lose their representativeness and struggle to adapt to data changes, thereby affecting long-term performance stability. However, our experiments also revealed that choosing an appropriate update rate is critical. An excessively high update rate can lead to overfitting to the latest data, which may compromise overall model performance.
2


Reviews:
[{"review_id": "JTjxTtuizf", "reviewer": "Reviewer_8rPH", "summary": "The paper proposes a model called 3D-FLEG for the 3D few-shot class incremental learning task. The model has a geometric feature extraction module that obtains geometric features through clustering and Laplacian eigenmaps, and it includes a geometric feature embedding module to fuse these geometric features with text features, considering modality heterogeneity.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your thoughtful and constructive feedback. We deeply appreciate your insights, which have guided us in refining our manuscript. Below, we outline the specific revisions made in response to your concerns:\n\n**Q1: Ablation Studies on the Number of Clusters**  \n**A1**: We deeply appreciate your guidance on this critical aspect. Honestly speaking, we have added a detailed ablation analysis of the number of clusters in Table 6 of Appendix D (also given below), demonstrating how it affects model performance. Note that the cluster numbers are the same as the **Basis Vectors Count** in our experiments, as each cluster center is adaptively mapped to a corresponding basis vector within the projection space during dynamic projection cluster construction.\nAs shown in Table 6, aligning cluster numbers with the base model's feature dimension (1024 in our case) balances information retention for accuracy and the redundancy of basis vectors. We have also included additional sensitivity analyses on cluster update rates as given in Fig. 6 of Appendix D. \nWe have revised our manuscript to emphasize these findings more clearly in the main paper.\n\n|  |**Average**|      ||**Accuracy(%)**|**Harmonic**|**Accuracy**|**(%)**| \n|:---------------------:|------|------|------|------|-----|------|------|\n| **Basis Vectors Count**| 0    | 1    | 2    | 3   | 1    | 2    | 3    |\n| 256                 | 93.4 | 91.5 | 86.4 | 85.0 | 86.8 | 76.2 | 76.8 |\n| 512                 | 93.2 | 91.5 | 86.3 | 85.9 | 87.0 | 76.2 | 77.1 |\n| 1024                | 93.8 | 91.9 | 87.5 | 86.8 |87.0 | 77.4 | 77.5 |\n| 2048                | 93.6 | 91.6 | 87.5 | 86.7 |87.3 | 77.7 | 77.7 |\n| 4096                | 93.8 | 91.9 | 87.8 | 86.4 |87.0 | 77.7 | 78.3 |\n\n\n\n\n\n**Q2：Impact of Prompt Style on Model Performance**  \n**A2**: Thank you for highlighting the importance of prompt-style analysis. \nWe implemented a more comprehensive comparison of the 7 experimental configurations.\nThey include:\n1. **SOTA Baselines**: Methods (1)-(2) adopt strategies from C3PR [1] and FoundationModel [2].  \n2. **Cross-Combination Strategies**: Methods (3)-(7) integrate various prompt styles with distinct training strategies.\n\nFrom that, we recorded and provided a new Figure 1 (now visible in Figure 1 of [the anonymous link](https://anonymous.4open.science/r/6061-FB07/6061.pdf)) in our revised version. \n\nAs shown in Figure 1, the performance of our geometric embedding (Method 7) remains stable across prompt variations, proving reduced dependency on prompt quality. \nThis demonstrates that geometric feature embedding alleviates reliance on prompt quality by encoding structural priors, ensuring stable performance even under variations in prompt style.  Compared with \"Alignment Module + Dual Cache System\" (Method 2), which caches 5 samples per class, our strategy replays only a single sample and achieves 7% higher accuracy with lower memory overhead.\n\n**Q3：Cited related papers**  \n**A3**: We sincerely appreciate the suggestion. The following works have been incorporated to strengthen our related works review:  \n1. **Geometric Word-Based Segmentation [3]**: Validates cluster-driven feature learning, aligning with our dynamic projection cluster design. (We have added it to Section 3.3 in our revised paper.)\n2. **3D Few-Shot Generalization [4]**: Highlights domain adaptation challenges, motivating our geometry-centric approach for cross-dataset robustness. (We have added it to Section 3.4 in our revised paper.)\n3. **Multimodal Fusion [5]**: Supports our cross-modal alignment strategy via joint geometric-textual optimization. (We have added it to Section 3.4 in our revised paper.)\n\nThese references are now cited in relevant sections as indicated in the corresponding bracket.  \n\nWe hope our revised works have addressed your concerns.\nShould further clarifications or adjustments be needed, we are fully committed to incorporating your guidance.\nThanks for your feedback, as it is invaluable in refining our work.\n\n\n**References**\n\n[1] Canonical shape projection is all you need for 3d few-shot class incremental learning, ECCV 2024.\n\n[2] Foundation Model-Powered 3D Few-Shot Class Incremental Learning via Training-free Adaptor, ACCV 2024.\n\n[3] Generalized Few-Shot Point Cloud Segmentation Via Geometric Words, ICCV 2023.\n\n[4] Rethinking Few-shot 3D Point Cloud Semantic Segmentation, CVPR 2024.\n\n[5] Multimodality Helps Few-shot 3D Point Cloud Semantic Segmentation, ICLR 2025.", "claims_and_evidence": "The claim that the reliance on text prompts and training strategies limits the robustness and performance of few-shot class incremental learning is reasonable and well supported.", "methods_and_evaluation_criteria": "The proposed method is well explained, and the evaluation criteria and datasets used are appropriate for the task.", "theoretical_claims": "There are no theoretical claims.", "experimental_designs_or_analyses": "The main experiments are comprehensive and demonstrate the effectiveness of the proposed method. Nonetheless, some ablation studies are missing. For instance, the Geometric Feature Extraction Module uses spectral clustering as its first step, but the paper does not specify the number of clusters used in this step and analyze how varying this parameter affects performance. Additionally, it would be useful to know how the model's performance varies if the prompt style is changed, for example, to GPT-generated prompts.", "relation_to_broader_scientific_literature": "The proposed designs complement the broader literature and introduce new designs.", "essential_references_not_discussed": "The following papers are related to the paper's context and should be cited—including computing geometric features via clustering, improving generalization on novel classes, and fusing multimodal knowledge for novel class learning:\n\n+ ICCV 2023, Generalized Few-Shot Point Cloud Segmentation Via Geometric Words\n+ CVPR 2024, Rethinking Few-shot 3D Point Cloud Semantic Segmentation\n+ ICLR 2025, Multimodality Helps Few-shot 3D Point Cloud Semantic Segmentation", "other_strengths_and_weaknesses": "The paper is clearly written and easy to follow. The motivations for the design choices are clear and reasonable.", "other_comments_or_suggestions": "N/A", "questions_for_authors": "Please see the Experimental Designs part about the missing ablations. Providing the analysis on the mentioned ablation studies will further enhance the quality of the paper.", "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": "REw5TqfaIc", "reviewer": "Reviewer_g1xS", "summary": "The paper proposes 3D-GLEG, a method to improve 3D few-shot class incremental learning by incorporating geometric features into the learning process. The authors propose two modules: a geometric feature extraction module and a geometric feature embedding module. By leveraging geometric information, 3D-FLEG achieves superior performance on four datasets, ModelNet, ShapeNet, ScanObjectNN and CO3D.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your insightful feedback, which has helped us significantly improve the clarity and rigor of our manuscript. Below are our detailed responses:\n\n**Q1: Claims (Laplacian Eigenmaps, AdaptiveAvgPool1d, geometric feature) are not well supported**\n\n**A1: (1) Laplacian Eigenmaps for Geometric Structure Extraction**  \nTheoretically, Laplacian Eigenmaps minimizes $\\sum_{i,j} (y_i - y_j)^2 W_{ij}$, where $y_i, y_j$ are low-dimensional representations of data points $x_i, x_j$, and $W_{ij}$ reflects their proximity ($W_{ij}=e^{-\\frac{\\|x_i-x_j\\|^2}{t}}$ if neighbors; otherwise, $W_{ij}=0$). This ensures nearby points in the original space remain close in the reduced space.\nUsing the graph Laplacian $L=D-W$, the problem reduces to minimizing $y^T L y$. The eigenvectors of $L$ capture the manifold's structure, preserving local geometry effectively. A similar theoretical analysis appears in [1].\n\nBesides, we have additionally conducted an ablation study to validate the role of Laplacian Eigenmaps as given below, demonstrating a 1.8% improvement in accuracy when utilized. We have incorporated the comprehensive theoretical proof and analysis in our revised paper.\n\n|Laplacian eigenmaps||Average|Accuracy|(%)|\n|:-:|-|-|-|-|\n||Session0|Session1|Session2|Session3|\n|&cross;|**93.8**|91.2|86.4|85.0|\n|&check;|**93.8**|**91.9**|**87.5**|**86.8**|\n\n**A1: (2) AdaptiveAvgPool1d for Fine-Grained Features**  \nWe apologize for the confusion caused by the description of \"fine-grained\" features. You are right that traditional average pooling (AvgPool) typically extracts globally smoothed features. AdaptiveAvgPool1d in 3D-FLEG differs by dynamically adjusting pooling windows to capture local geometric statistics rather than fixed-window averaging. Specifically, it quantifies geometric attribute distributions across localized regions along the channel dimension, enabling fine-grained pattern extraction [2], where \"fine-grained\" refers to the statistical representation of local geometric details. We have revised our manuscript to clarify this distinction and articulate our design rationale.\n\n**A1: (3) Modality Abstraction Alignment**  \nWe have strengthened Section 4.3 to clarify the modality abstraction alignment:\nA dual-pooling strategy (Eq. 5) extracts multi-scale geometric features, bridging the gap between text and point cloud details. The Transformer encoder (Eq. 6) then refines these features, emphasizing geometry relevant to text prompts and reducing noise.\nCross-entropy loss (Eq. 8) ensures consistency in the shared semantic space, aligning geometric and textual abstractions.\n\n**Q2: Cross-Dataset Superiority**\n\n**A2:** Thank you for prompting this critical analysis. 3D-FLEG’s cross-dataset superiority stems from its geometry-centric design:  \n- **Dynamic Projection Clusters** capture task-invariant geometric patterns, which generalize across synthetic-to-real domains.\n- **Geometric Feature Embedding** directly fuses these features with text semantics, bypassing domain-specific text variations. \n\nThis synergy enables 3D-FLEG to achieve 7% higher accuracy in cross-dataset tasks. We have revised Section 4.3 to clarify this mechanism.\n\n**Q3: Symbol Confusion and Misspellings**\n\n**A3:** Thank you for highlighting these inconsistencies. We have reviewed the manuscript and made corrections to the symbols and typographical errors to improve clarity and rigor.\n\n**Q4: Detailed Descriptions of Prompt and Training Strategies in Figure 1**\n\n**A4:** We appreciate your suggestion to improve Figure 1. We have detailed the experimental setup principles with relevant citations. For further details, please see our response to Reviewer 8rPH's Q2.\nThe \"Alignment Module and Dual Cache System\" requires caching five samples per class, causing significant computational and memory overhead. In contrast, our geometric embedding module integrates geometric features directly with text prompts, eliminating the need for complex alignment training and caching. \n\n**Q5：Clarification on Initial Features ${featBase}_i$ and ${featPoint}_i$**\n\n**A5：** To address this ambiguity, we have revised Section 3 to explicitly define:\n\n${featBase}_i$ : Features extracted from base-class data using the frozen Uni3D encoder. These features are used to construct dynamic geometric projection clusters.  \n${featPoint}_i$ : Features processed during incremental training from the same Uni3D encoder. These are dynamically reprojected through DGPC using learnable attention weights to extract geometric features.\n\nYour feedback has guided us in refining both the theoretical foundations and presentation of our work. All revisions have been incorporated into the manuscript.\n\n**References**\n\n[1] Laplacian eigenmaps for dimensionality reduction and data representation, Neural computation 2003.\n\n[2] Point cloud segmentation of overhead contact systems with deep learning in high-speed rails, Journal of Network and Computer Applications 2023.", "claims_and_evidence": "1. The claim that Laplacian Eigenmaps can extract geometric structure from point cloud data is not well supported.\n\n2. The claim that AdaptiveAvgPool1d for fine-grained feature extraction sounds not true for me. Based on my understanding, average pooling is not typically used for fine-grained feature extraction. Instead, it tends to extract global, smoothed features. \n\n3. The claim that the geometric feature embedding module can ensure that data from both modalities interact on similar levels of abstraction is not fully supported according to section 3.4. How do equations 5 and 6 ensure that the point cloud features and text features have similar levels of abstraction? They are only in the same dimensional space. The authors only use a simple text prompt template that includes class names, which I believe contains abstract features. However, point cloud features should contain more detailed features.", "methods_and_evaluation_criteria": "The proposed method and evaluation criteria are appropriate for the FSCIL problem. The evaluation on ModelNet, ShapeNet, ScanObjectNN and CO3D provides a solid benchmark, covering both real-world and synthetic 3D datasets. The metrics, including accuracy, harmonic mean accuracy, and relative accuracy drop, effectively measure both new class adaptation and forgetting mitigation.", "theoretical_claims": "The paper does not provide formal theoretical proofs but makes claims about the effectiveness of Laplacian eigenmaps in preserving geometric structures and the dynamic geometric feature projection clusters in improving feature representation. While the method is conceptually plausible, the paper does not rigorously prove that the transformed features are explicitly geometry-aware.", "experimental_designs_or_analyses": "The paper evaluates the method on four datasets and uses multiple metrics. The results indicate improved performance over baseline methods. However, there are some potential issues:\n1. No direct ablation study that analyzes the contribution of Laplacian eigenmaps or whether they truly enhance geometry-awareness and how.\n2. More detailed discussion of why the method performs better across datasets.", "relation_to_broader_scientific_literature": "For 3D FSCIL problem, previous works like Microshape proposed a universal description language to reduce domain discrepancies, and C3PR adapted CLIP to handle FSCIL task. This paper solves the problem by integrating geometric features, reducing reliance on foundation models and complicated training strategies.", "essential_references_not_discussed": "N/A", "other_strengths_and_weaknesses": "N/A", "other_comments_or_suggestions": "1. It is confusing that both the ground-truth label and final feature representations computed from U are represented with the notation y.\n2. Some of the Microshapes is spelled as \"Micrpshapes\"", "questions_for_authors": "1. For Figure 1, can the authors elaborate more on each prompt-training and training strategies and also add the citations if necessary? Since there are no detailed descriptions for the alignment module, how can the authors conclude that their method, embedding geometric features, is simpler than the alignment module?\n\n2. Can the authors explain how they get the initial features, $featBase_i$, as they also mentioned the point cloud features $featPoint_i$?", "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": "Ckn0zvEWPi", "reviewer": "Reviewer_YEPj", "summary": "This paper investigates few-shot class incremental learning for 3D object classification using foundation models. Building on the work of FoundationModel (Ahmadi et al.), the authors employ a frozen, pre-trained large-scale 3D encoder (Uni3D) to extract generalizable features for each point. They then construct enhanced text embeddings based on prompts generated from category names, which are combined with geometric features to calculate similarity and assign the final label. A key contribution of the paper is the method for constructing abstract geometric features using spectral clustering and Laplacian eigenmaps, as well as the way to fuse the text embeddings from the prompts with the geometric features through transformer. Experimental evaluation across multiple datasets and settings demonstrates that the proposed method achieves clear improvements in performance.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely appreciate your insightful feedback, which has guided us in refining the manuscript and addressing key concerns. Below, we provide detailed responses to each of your questions, supported by additional analyses and clarifications.\n\n**Q1: Clarification of Ablation Studies in Table 3**  \n**A1**: Thank you for prompting us to clarify this section. Table 3 systematically evaluates the contributions of two critical components in the geometric feature extraction module:  \n- **Dynamic Geometric Projection Clusters**: These clusters are constructed via spectral clustering and Laplacian eigenmaps to encode shared geometric structures.  \n- **Attention Weights for Basis Vectors**: Learnable weights prioritize cluster centers relevant to incremental tasks.  \n\nThe ablation settings are:  \n- Row 1: Raw point cloud features without DGPC.  \n- Row 2: DGPC with equal weights for all basis vectors.  \n- Row 3: DGPC + learnable attention weights.  \nKey findings are now presented more succinctly: \n- DGPC Necessity: Removing DGPC (Row 1) degrades average accuracy by 2.7%, as raw features fail to integrate geometric-textual semantics.\n\n- Attention Mechanism: Adding learnable weights (Row 3) improves harmonic accuracy by 2.1% over Row 2, demonstrating adaptive weighting’s role in suppressing noise and outliers.\n\nThe results validate that DGPC and attention weights synergistically enhance stability and discriminability. These results are now contextualized in Section 4.4, and additional visualization examples are available in Figure 2 of [the anonymous link](https://anonymous.4open.science/r/6061-FB07/6061.pdf). We previously did not express this clearly, but based on your feedback, we will restructure the table and discussion for better interpretability.\n\n**Q2: Visualization of Dynamic Projection Clusters**  \n**A2**: To validate the effectiveness of dynamic geometric feature projection clusters during incremental learning, we visualize the geometric features extracted by DGPC at each incremental stage, as shown in Figure 2 of [the anonymous link](https://anonymous.4open.science/r/6061-FB07/6061.pdf). Visualizations reveal that DGPC-enhanced features exhibit tighter intra-class clustering and clearer inter-class separation. These results demonstrate that DGPC effectively encodes task-invariant geometric priors, enabling robust feature extraction across incremental phases. We have integrated key examples into the main text in our revised version.\n\n**Q3: Implementation Details of the Transformer Encoder**  \n**A3**: The Transformer encoder comprises 2 standard layers, each with 8-head self-attention. We have enhanced the implementation details section in the revised version to include more comprehensive information.\n\n**Q4: Design Principles of Figure 1**  \n**A4**: We sincerely appreciate your guidance in improving Figure 1's clarity. In light of your thoughtful suggestions, we have reorganized and optimized Figure 1 (detailed in Figure 1 of [the anonymous link](https://anonymous.4open.science/r/6061-FB07/6061.pdf)) to improve its clarity. It now contains 7 comparisons: \n1. **SOTA Baselines**: Methods (1)-(2) adopt strategies from C3PR [1] and FoundationModel [2].\n2. **Cross-Combination Strategies**: Methods (3)-(7) integrate various prompt styles with distinct training strategies.\n\nFor detailed explanations of Figure 1, we sincerely invite you to consult our response to Reviewer 8rPH's Question 2.\n\n**Q5: Linking Observations to Robust Feature Learning**  \n**A5**: We appreciate your guidance in strengthening this connection. The experiments (lines 87-93) demonstrate that complex prompt designs yield inferior performance compared to simple prompts. This observation highlights a critical limitation: existing methods overly rely on manually crafted text semantics while failing to autonomously extract geometry-aware robust features from 3D point clouds. Consequently, models exhibit excessive sensitivity to textual variations and struggle to adapt to distribution shifts in incremental phases with limited samples.  \n\n3D-FLEG addresses this by:  \n1. **Geometric Feature Embedding**: Explicitly encoding spatial structures into prompts via dynamic projection clusters, bypassing dependency on complex text engineering.  \n2. **Unified Optimization**: Forcing joint alignment between geometric features and text semantics during incremental training, enabling the model to prioritize discriminative cross-modal patterns from sparse data.\n\nBy incorporating supplementary experiments, enhanced visualizations, and expanded explanations, we sincerely hope to have addressed all raised concerns. Please let us know if you feel any additional adjustments would better address your concerns.\n\n**References**\n\n[1] Canonical shape projection is all you need for 3d few-shot class incremental learning, ECCV 2024.\n\n[2] Foundation Model-Powered 3D Few-Shot Class Incremental Learning via Training-Free Adaptor, ACCV 2024.", "claims_and_evidence": "The evidence provided to support the claims in the paper is insufficient in certain areas, as some details are lacking. For instance, the specific configuration used when removing a module in Table 3 is not clearly explained, leaving this aspect of the experiment unclear.", "methods_and_evaluation_criteria": "Yes, the proposed method is evaluated both within-dataset incremental learning and cross-dataset incremental learning, in line with the protocols established by existing methods.", "theoretical_claims": "The paper does not present any theoretical claims.", "experimental_designs_or_analyses": "Yes, the overall experimental settings are valid, as they follow established protocols from existing methods. However, the ablation study in Table 3 lacks sufficient support, as the detailed configuration for removing a module is not clearly provided.", "relation_to_broader_scientific_literature": "The key idea of this paper is closely related to the concept of cross-modal feature fusion, specifically integrating text and visual cues, which has been explored in prior research.", "essential_references_not_discussed": "N.A.", "other_strengths_and_weaknesses": "**Strengths**:\n\n1. The proposed method is sound and well-constructed.\n2. The paper demonstrates clear improvements across multiple datasets and FSCIL settings.\n3. The ablation studies are comprehensive.\n\n\n**Weaknesses**:\n\n1. It would be beneficial to include visualizations of the basis vectors (and their evolution as new classes are introduced) or the distribution of geometric features.\n2. Implementation details of the transformer encoder are not provided.\n3. In Table 3, the detailed configuration for removing one module is not clearly explained.", "other_comments_or_suggestions": "N.A.", "questions_for_authors": "1. What principles guided the design of the five settings presented in Figure 1?\n\n2. How does the observation in lines 87-93 lead to the conclusion that \"one challenge to address to advance FSCIL on 3D point clouds\" is \"enhancing the model’s ability to learn robust feature representations\"?", "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:

Meta-review: There are shared favorable opinions that the authors present solid and comprehensive evaluations and well-executed. The authors were enthusiastic about providing the requested ablation studies, and a couple of rounds of author-reviewer discussions led to a unanimous consensus that was positive. Note that multiple reviewers suggested that a theoretical analysis of the proposed method or module would be more beneficial in grasping the gist of the work. 
Since no significant weaknesses were found, AC is glad to recommend acceptance of this hardworking paper.
Decision: Accept (poster)