Reasoning Beyond Points: A Visual Introspective Approach for Few-Shot 3D Segmentation
Authors: Changshuo Wang
Shuting He
Xiang Fang
Zhijian Hu
JIA-HONG HUANG
Yixian Shen
Prayag Tiwari
Venue: NeurIPS 2025 poster ()
Keywords: Point Cloud Semantic Segmentation
Few-shot Learning
Visual Introspective Prototype
Primary area: applications

Abstract: Point Cloud Few-Shot Semantic Segmentation (PC-FSS) aims to segment unknown categories in query samples using only a small number of annotated support samples. However, scene complexity and insufficient representation of local geometric structures pose significant challenges to PC-FSS. To address these issues, we propose a novel pre-training-free Visual Introspective Prototype Segmentation network (VIP-Seg). Specifically, we design a Visual Introspective Prototype (VIP) module that employs a multi-step reasoning approach to tackle intra-class diversity and domain gaps between support and query sets. The VIP module consists of a Prototype Enhancement Module (PEM) and a Prototype Difference Module (PDM), which work alternately to progressively refine prototypes. The PEM enhances prototype discriminability and reduces intra-class diversity, while the PDM learns common representations from the differences between query and support features, effectively eliminating semantic inconsistencies caused by domain gaps. To further reduce intra-class diversity and enhance point discriminative ability, we propose a Dynamic Power Convolution (DyPowerConv) that leverages learnable power functions to effectively capture local geometric structures and detailed features of point clouds. Extensive experiments on S3DIS and ScanNet demonstrate that our proposed VIP-Seg significantly outperforms current state-of-the-art methods, proving its effectiveness in PC-FSS tasks. Our code will be available at https://github.com/changshuowang/VIP-Seg .

Reasoning Beyond Points: A Visual Introspective Approach for Few-Shot 3D Segmentation
Changshuo Wang
Shuting He
Department of Computer Science
School of Computing and Artificial Intelligence Shanghai University of Finance and Economics
University College London London, United Kingdom
Shanghai, China heshutingg5500mail.ccm
wangchangshuo @gmail. com
Xiang Fang"
Zhijian Hu LAAS
Interdisciplinary Graduate Programme
Nanyang Technological University, Singapore xf afg955880gaii1. com
CNRS
Toulouse, France
com
Jia-Hong Huang Information Institute University of Amsterdam Amsterdam, Netherlands j. huang@uva. nl
Yixian Shen Information Institute University of Amsterdam Amsterdam, Netherlands shen@uva. nl
Prayag Tiwari
School of Information Technology
Halmstad University Halmstad, Sweden prayag www.wiiiiww.eeee.e
Abstract
Point Cloud Few-Shot Semantic Segmentation (PC-FSS) aims to segment unknown categories in query samples using only small number of annotated support sam- ples. However, scene complexity and insufficient representation of local geometric structures pose significant challenges to PC-FSS. To address these issues, we propose novel pre-training- free Visual Introspective Prototype Segmentation network (VIP-Seg). Specifically, we design a Visual Introspective Prototype (VIP) module that employs multi-step reasoning approach to tackle intra-class diversity and domain gaps between support and query sets. The VIP module consists Prototype Enhancement Module (PEM) and Prototype Difference Module (PDM), which work alternately to progressively refine prototypes. The PEM en- hances prototype discriminability and reduces intra-class diversity, while the PDM learns common representations from the differences between query and support features, effectively eliminating semantic inconsistencies caused by domain gaps. To further reduce intra-class diversity and enhance point diseriminative ability, we propose Dynamic Power Convolution (DyPowerConv) that leverages learn- able power functions to effectively capture local geometric structures and detailed features of point clouds. Extensive experiments on S3DIS and ScanNet demon- strate that our proposed VIP-Seg significantly outperforms current state-of-fe state-of-the-art methods, proving its effectiveness in PC-FSS tasks. Our code will be available
com/ changshuowang/VIP..
*Corresponding author.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Introduction
In recent years, point cloud data has become increasingly important in numerous applications such as autonomous driving (53 robotics [27 g 7. and augmented reality 66 [23] As fundamental task in 3D scene understanding ITI IO 32 point cloud semantic segmentation 14 33 51 49 plays a crucial role in these domains. However, acquiring large-scale, high-quality annotated point cloud data demands substantial time and human resources, severely limiting the practical application of traditional deep learning methods.
To address the data scareity challenge, researchers 16 4 have turned to few-shot learning for point cloud segmentation tasks. Point Cloud Few-Shot Semantic Segmentation (PC-FSS) [1 29
aims to segment novel categories in query samples using only a handful of annotated support samples, significantly reducing annotation costs. Zhao et al. [54] pioneered this approach by introducing AtMMPII, based on a pre-trained DGCNN [40]. Subsequent works [19 13 55 further enhanced feature extraction and prototype generation strategies, improving performance to some extent. However, PC-FSS faces two major challenges that limit its effectiveness: representation inconsistency within semantic categories and cross-domain feature misalignment. The first occurs when identical semantic categories in support and query samples show significant differences in physical characteristics, such as size, orientation, or visual appearance. Prototypes from support samples aid segmentation of similar objects in query samples but can introduce biases due to these variations. The second challenge involves feature distribution mismatches, where query data contains semantic content not present in the support set, and vice versa.
Moreover, most existing methods (as shown in Fig. [T rely on pre-training paradigms, which not only increase computational costs but also potentially introduce domain shifts when facing unseen categories, particularly in cross-domain applications. Additionally, the irregular and sparse nature of point clouds makes it challeng- ing to effectively capture local geometric struc- tures, a problem that becomes more pronounced in few-shot scenarios where limited data is avail- able for learning robust representations.
Sappot
Alaekias
Orery
Pheiiaa Twue Pran Clod For Sigtaaa
truu
Mult Rosceig Prons
FaEL
lutal Pettapps
Frecedion
Qutr
VIPMobkle
Qxay
(1) Furteige Fae dart Segantor
To overcome these limitations, we propose VIP- Seg, a novel rre-training-fiee free network for PC- FSS. As shown in Fig. | our approach fea- tures two key innovations: First, we introduce Dynamic Power Convolution (DyPowerConv), which adaptively models local geometric fea- tures by learning region-specific power func- tions, capturing fine-gre tineggained details and struc- tural variations. This enhances the model's abil- ity to distinguish similar structures, further re- ducing intra-class diversity. Second, we develop a Visual Introspective Prototype (VIP) module to address intra-semantic diversity and domain gaps through multi-step reasoning approach. The VIP module combines Prototype Enhancement Module (PEM) and Prototype Difference Module (PDM) that work alternately to progressively refine prototypes. The PEM improves prototype dis- criminability through attention mechanisms, while the PDM learns common representations from feature differences, effectively mitigating domain gaps. This iterative process gradually aligns feature distributions and boosts segmentation accuracy.
Figure 1: Comparison between previous methods and our approach for PC-FSS. (a) Previous meth- ods typically follow a two-stage pipeline that re- quires pre-training a DGCNN followed by fine- tuning with prototype adaptation. (b) Our pro- posed VIP-Seg eliminates the pre-training stage with a single-stage approach that integrates Dy- PowerConv and employs multi-step reasoning process to progressively refine prototypes.
Our main contributions can be summarized as follows:
We propose VIP-Seg, novel pre- pre-training-Ferr free framework for point cloud few-shot semantic segmentation that achieves superior performance without time-consuming pre-training.
We introduce Dynamic Power Convolution, which leverages learnable power functions to adaptively model complex local geometric features, significantly enhancing the network's ability to capture fine-grained structural details.

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We design Visual Introspective Prototype module that employs multi-step reasoning approach to effectively address intra-semantic diversity and domain gaps between support and query sets.
Extensive experiments demonstrate that approach significantly outperforms state-of-the- art methods across various few-shot settings, proving the effectiveness of our approach.
2 Related Works
2.1 Point Cloud Semantic Segmentation
a erucial task in 3D scene understanding 47 that has witnessed significant advancements in recent years. Pioneering works such as PointNet 21 and +ointNet++ [22] established the foundation by directly processing point cloud data through multi-layer perceptrons (MLPs). Subsequent research introduced innovative methods leveraging graph convolution, attention mechanisms, and multi-modal approaches. For instance, DGCNN (40] proposed the EdgeConv operation to capture inter-point relationships via dynamically constructed local graphs. Inspired by the extensive progress made by the Transformer architecture in computer 34 [8], some researchers are also applying the architecture to point cloud understanding tasks. For example, Point Transformer [52] and its variants [14 20 incorporated self-attention mechanisms to effectively model long-range dependencies. Recently, several methods based on State Space Models have achieved significant advancements in 3D tasks. Despite these advancements, these methods typically demand substantial annotated data for training, limiting their practical applications.
Point cloud semantic segmentation 30 42
vision [25 24 37
39
2.2 Point Cloud Few-shot Semantic Segmentation
To tackle the data scarcity challenge point cloud semantic segmentation, few-shot learning ap- proaches (26) have emerged as a promising solution. Early work by [54] introduced prototype
networks to this domain through the AttMPTI method, sparking subsequent research focused on feature enhancement, prototype optimization, and domain adaptation [19]
48 9
45
38
35
Recent
advancements have developed more sophisticated techniques: [16] incorporated structural infor- mation for precise target localization while minimizing background interference, [43) addressed
intra-class diversity and semantic inconsistency through bilateral aggregation and consistency purifi- cation, and [41] leveraged LLM-generated content optimize prototypes and mitigate categorical bias. Meanwhile, [2] proposed a novel setting to avoid foreground leakage, while [ enhanced performance through
data. Despite these advancements, challenges remain in effectively capturing complex local structures and addressing domain differences.
2.3 Dynamic Convolution
Dynamic convolution enhances a model's adaptability and expressive power by generating convolution kemels dynamically based on input data. In 2D image processing, dynamic convolution [46 15] has been widely adopted for its effectiveness. Inspired by these successes, researchers have extended it to the 3D point cloud domain. [12] proposed DyCo3D, which incorporates dynamic context learning to better capture local point cloud features.
28 developed KPConv, which generates dynamic convolution kernels by learning local geometric structures, while [44] introduced PAConv, leveraging a weight bank and ScoreNet to dynamically assemble convolution kernels, thereby adapting to the irregular structure of point clouds. However, these methods primarily focus combining multiple convolution kernels through attention coefficients, leaving significant room for improvement in fine-grained semantic understanding of local geometric structures.
Method
In this section, we first introduce the problem definition of Point Cloud Few-Shot Semantic Segmen- tation (PC-FSS). Then, we describe the overall architecture of VIP-Seg, illustrated in Fig. 2 Next, we present the proposed Dynamic Power Convolution (DyPowerConv). Finally, we introduce our Visual Introspective Prototype (VIP) module that employs multi-step reasoning approach.
3

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Kank
Suppoct
Prediction
Tall MoAeoeeeee 3arrka Blo
intropeccvee
Sharta 10
Uponn
Proorope
PRS MibhChooging
Query
Quacy
Poxt
Segtaerralon
(a) VIP-Seg
Softrak
Final prdictin
Gating Fision Netwodks
HiConv
FDN
PDN
HiConv 2
Paintypes
Shep
Step
Sypp
Slep
Suppoet
HiConv
weigt
Local Negihberhos
Fost
Kalli slza Reacciia Process
(b) DoPwerConn
(c) VIP Module
Figure 2: Overview of the proposed VIP-Seg. (a) The overall framework consists of an encoder- decoder backbone for feature extraction and segmentation head for prototype-basee classification. (b) Illustration of our Dynamic Power Convolution (DyPowerConv) module, which combines multiple high-order convolutions (HiConv) with dynamic attention weights to adaptively model local geometric structures. Each HiConv learns a different power function to capture fine-grain fineggriined details. (c) The Visual Introspective Prototype (VIP) module implements a multi-step reasoning process where the Prototype Enhancement Module (PEM) and Prototype Difference Module (PDM) work alternately to progressively refine prototypes. A gating fusion network integrates predictions from multiple reasoning steps to generate the final segmentation result.
3.1 Problem Definition
Following the standard episodic learning paradigm (54), we partition the dataset into two non- overlapping class sets: base classes Chose used for training and novel classes Crovel used for testing, where Crase n Cnovel
The framework operates through N-way K-shot tasks, utilizing paired support and query sets. In each episode, the support set S samples for each of the N categories, where pn.k E (3++d)) represents a point cloud with L points (each having 3D coordinates and d-dimensional features. such as color or surface normals), and Mn.k E {0, 1 denotes the corresponding binary segmentation mask indicating foreground (target class) and background points. The query set Q {(Pq. M2)}} contains point clouds P E IR Lx (3+-4) to be segmented, with ground truth masks Mg E {0, 1, N assigning each point to one of the N target classes or the background class.
.MT.A)} - contains K labeled
3.2 Overall Architecture of VIP-Seg
As shown in Fig. 2 we propose VIP-Seg, formulating PC-FSS as dual optimization problem that combines local structure modeling with progressive prototype refinement. VIP-Seg adopts an encoder- decoder architecture coupled with the proposed VIP module for effective few-shot segmentation. blocks that integrate our proposed
The encoder consists of three stacked DyPowerConv-Mann DyPowerConv for local geometric feature extraction and Mamba blocks [17] for capturing long-range dependencies. DyPowerConv adaptively models local point relationships through learnable power functions, while Mamba blocks efficiently process sequential data with state space models, enabling effective long-range interactions among points. The decoder progressively recovers point cloud resolution through inverse interpolation, propagating features from coarser finer levels.

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3.3 Dynamic Power Convolution
To effectively capture local geometric structures and detailed features point clouds, thereby further reducing intra-class diversity, we propose Dynamic Power Convolution (DyPowerConv), This convolution adaptively models complex local geometric features through learnable power functions, enabling flexible representation of local structures.
3.3.1 Power Function Design
The design of our DyPowerConv is motivated by the need for flexible modeling of local geometric structures in point clouds. We employ learnable power functions to capture features at different scales and levels of detail. DyPowerConv comprises two key components: Low-order Convolution (LoConv) and Dynamic High-order Convolution (DyHiConv), expressed as:
9: 9.¹ +
PH -
where of and g. represent the outputs of LoConv and DyHiConv, respectively. LoConv captures basic geometric structures, while DyHiConv adaptively models fine-grained details through dynamic power functions.
3.3.2 Low-order Convolution
LoConv primarily extracts basic geometric information from local structures. We adopt Nonparameter Trigonometric Functions (NTF) to encode point cloud coordinates p; and color information &; E RS, mapping them to the same dimension their features, then adding their information and applying non-linear transformation to obtain nigh-dimenssonal representation of basic structural information. The LoConv can be formulated as:
o = A(WWiffle)) E (N(:)))).
(2)
(J} +5 fz)/3,
= [sin [assin(pp//44))) cos(cppp)) ( I1 E Rd
(4)
where f is obtained similarly to 13- a and B represent the wavelength and amplitude hyperparameters of the trigonometric functions, respectively. W, E RCn XCou denotes the non-linear transformation matrix.
3.3.3 Dynamic High-order Convolution
To capture the details of complex local geometric structures, DyHiConv draws inspiration from dynamic convolution [46] generate multiple convolution weights using input information. Unlike traditional dynamic convolution, we improve the power function design by using smooth power function (If) fil + <) instead of traditional sign functions, where pt is a learnable parameter for each expert and small constant (typically 10-6). This design effectively captures local geometric details while maintaining gradient continuity. DyHiConv (see Fig, (2) can be expressed
as:
T Ligg.
DH 9
(5)
where T is the number of experts (set 8 in implementation), of represents the high-order convolution of the 1-th expert, and de represents the attention assembly coefficient of the expert. Specifically, the high-order features of generated by each expert calculated through:
g = A({w((p,) (lfz ful + e) lp, E (((p)))
(6)
where wt (p,) is the dynamically generated weight, p; is the learnable power exponent parameter of the f-th expert, C represents element-wise multiplication, and A is an aggregation function (typically max pooling).
The attention assembly coefficients é are constructed from explicit geometric information hy:
exp(Wehj)
Φ: Ei exp(W)hg)
(7)
where W, is learnable transformation matrix.
5

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3.3.4 Explicit Structure Introduction
To better utilize the geometric information of point clouds, we use the coordinates of neighboring points py and center point p; as basic geometric elements to construct the weight w/ for HiConv:
Wj Whhj
where h3 = Pi., - pt, Ip; - p:lll E I and Wt E 10ue denotes the transformation matrix. The introduction of explicit geometric information facilitates the learning of relative spatial layout relationships between points and the capture of local geometric features and details.
3.4 Visual Introspective Prototype Module
To address feature discrepancies between support and query sets (e.g.. intra-class diversity and domain gaps), we propose the Visual Introspective Prototype Module (VIP). It employs multi-step reasoning process iteratively refine prototypes, simulating human "think-reflect-rvvies consists of two components: the Prototype Enhancement Module (PEM) and Prototype Difference Module (PDM), which alternately form reasoning chain, progressively aligning feature distributions.
reasoning. VIP
3.4.1 Prototype Enhancement Module
The PEM aims to enhance the discriminability of prototype features and reduce intra-class diversity through self-attention and cross-attention mechanisms. Given a point cloud with M points, let F, R MxC and F, I MxC denote the support and query features, respectively, where C is the feature dimension. The PEM first applies local max pooling and projection mapping to extract statistical characteristics of each channel:
F MaxPool(F.) W1,
F -axXool(FFF) W1,
where W1 E CxC is a learnable transformation matrix. Next, the PEM enhances the prototype features from two aspects:
1) Self-correlation Enhancement: The PEM learns internal structural information by computing self-correlation matrices of the support and query features:
W3(F" WF(FFFF))
A W3(FFFF,)
(10)
Fsels Softmax AA, F, -Softmax (A,) Fpp
(11)
2) Cross-correlation Enhancement: The PEM learns shared information through interaction between
the support and query features:
Across FT F' 8
(12)
pross Softmax (Across) Fpp
(13)
Finally, the enhanced prototype features output by the PEM
F Frelf Feross
Fp,
(14)
where F, E R(K+1)xC represents the initial prototype features, and Fç denotes the enhanced prototype features.
3.4.2 Prototype Difference Module
The PDM focuses on learning the differences between the support and query feature distributions to further eliminate domain gaps. After sharing similar pooling and mapping operations with the PEM, the PDM calculates the difference information between the support and query features:
AG = F',F,, -.''FFFFF
F.F.
(15)
and uses this difference information to adjust the prototype features:
Edelta sigmoid F.
(16)

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Additionally, the PDM further optimizes the prototype features through cross attennioo:
Softmax(Aeroas) O
F
(17)
where Across is the cross-correlation matrix between the support and query features. prototype features output by the PDM
The final
F
Fdelta
F orcerross
(18)
p
where Fe; the output features from PEM, F is the output features after PDM processing.
p
3.4.3 Multi-step Reasoning Process
We design multi-step reasoning process to progressively optimize the prototype features. Specifi- cally, during reasoning steps, the PEM and PDM work alternately:
PEM(F 91 F., F(-1), PDM(F, y F., F)-1) Ft-1 if 1%2
if 0
F
p
(19)
where Fú, represents the prototype features at step t. and F represents the initial prototype features. This multi-step reasoning process enables iterative refinement: the PEM step enhances discriminability by extracting key information, while the PDM step eliminates domain gaps through difference learning, progressively aligning feature distributions.
3.4.4 Gating Fusion Network
To effectively fuse the multi-step reasoning results, design gating fusion network. At each reasoning step t, we compute the cosine similarity between the query features Fg and the prototype features F The intermediate prediction results L are then computed
F,
F p
L, = (Fi.FF... -
LL
L
(20)
F
F2
p
where sim(..) :) denotes the cosine similarity function that measures the semantic alignment between represents the 1.2 norm. L, represents
query and prototype features, denotes the dot product, and ||.|| the prototype labels.
Finally, the gating network learns importance weights w for predictions from each step and fuses all predictions to obtain the final result. The final prediction Lymmal is obtained by a weighted sum:
final
Wt
L, Σ GFN(Fq) Le,
(21)
where w e R$ is the weight from the gating network GFN()), and Lyinal is the final prediction.
Experiments
4.1 Datasets and Evaluation Metrics
We evaluate VIP-Seg on two widely adopted 3D segmentation benchmarks.
S3DIS dataset 3 consists of RGB point clouds collected from 272 rooms across 6 indoor areas. Each point is annotated with one of 13 semantic labels (12 object categories and clutter). Following the common practice [54], we split each scene into Im Im blocks and sample 2,048 points per block, resulting in total of 7,547 blocks.
ScanNet dataset [5] contains 1,513 scanned indoor scenes with dense point-wise annotations 20 semantic categories (excluding unannotated areas). Using the same preprocessing pipeline, we generate 36,350 blocks, each containing 2,048 points.
Evaluation Metric: employ the mean Intersection-ovvr--nnioo (mloU), a standard metric for point cloud segmentation, to assess model performance.
7

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Table I: Few-shot Results (%) S3DIS. St denotes the split is used for testing. Avg is the average mloU across splits. The best results are shown in bold.
2- 2-yay
3-Way
Method
I-shot
5-shot
I-shot
5-shot
Sp
S
Avg
Sp
S
Avg
5g
S;
Avg
So
S
Avg
DGCNN
40
36.34
38.79
37.57
56.49
56,99
56.74
30.05
32.19
31.12
46.88
47.57
47.23
ProtoNet
20
48.39
49.98
49.19
57.34
63.22
60.28
40.81
45.07
42.94
49.05
53.42
51.24
MPTI S4
52.27
$1.48
51.88
58.93
60.56
59.75
44.27
46.92
45.60
51.74
48.57
50.16
AtMPTI 54
53.77
55,94
54,86
61.67
67.02
64.35
45.18
49.27
47.23
54,92
56,79
55.86
BFG 19
55.60
55,98
55.79
63.71
66.62
65.17
46.18
48.36
47.27
55.05
57.80
56.43
2CBR 55
55.89
61.99
58.94
63.55
67.51
65.53
46.51
53.91
50.21
55.51
58.07
56.79
PAP3D 1
59.45
66.08
62.76
65,40
70,30
67.85
48.99
56.57
52,78
61.27
60.81
61.04
Seg-PN 56
64.84
67.98
66.41
67.63
71:48
69.56
59.11
60.42
59.77
59.48
64.72
62.10
TaylorSeg-PN
38
67.12
71.11
69.12
70.44
72.23
71.34
60.28
65.70
63.00
62.78
67.06
64.33
DAHNet
70.27
69.20
70.51
73.15
71.83
61.33
65.55
68.67
DyPolySeg
35
72.02
73.82
72.92
75.99
75.32
75.66
64.54
67.93
66.24
65.61
70.22
67.92
VIP-Seg
72.20
76.09
74.15
76.48
77.54
77.01
68.80
68,35
68.58
68.15
70.44
69.30
Improvement
+0.18
+2.27
+1.23
+0.49
+2.22
+1.35
+4,26
+0.42
+2.34
+2.54
+0.22
+1.38
Table 2: Few-shot Results (%) on ScanNet. S, denotes the split is used for testing. Avg is the average mloU across splits. The best results are shown in bold.
2-Way
3- 3-Way
Method
I-shot
S-shot
1-shot
S-shot
50
S;
Ang
Sp
S;
Avg
5.
Si
Avg
So
Si
Avg
DGCNN
40
31,55
28.94
30.25
42.71
37.24
39.98
23.99
19.10
21.55
34.93
28.10
31,52
ProtoNet
26
33.92
30.95
32.44
42.01
43.68
28.47
26.13
27.30
34.98
36.17
MPTI 54
39.27
36.14
37.71
46.90
43.59
45.25
29.96
27.26
28.61
38.14
34.36
36.25
АttPPI 54
42.55
40.83
41.69
54,00
50.32
52.16
35.23
30.72
32.98
46.74
40.80
43.77
BFG 19
42.15
40.52
41.34
51.23
49.39
50.31
34.12
31.98
33.05
46.25
41.38
43.82
2CBR
50.73
47.66
49.20
52.35
47.14
49.75
47.00
46.36
46.68
45.06
39.47
42.27
PAP3D [9]
57.08
55.94
56.51
64.55
59.64
62.10
55.27
55.60
55.44
59.02
53.16
56.09
Seg-PN 56
63.15
64.32
63.74
67.08
69.05
68.07
61.80
65.34
63.57
62.94
68.26
65.60
TaylorSeg-PN
38
67.52
70.75
69.14
68.39
71.55
69.97
63.60
67.55
65.58
66.98
69.78
68.38
DAFNet
68.79
69.95
69.37
70.91
70.60
70.76
66.14
66.70
66.42
68.97
71.95
70.46
DyPolySeg
71.05
72.73
71.89
71.25
73.66
72.46
67.65
71.24
69.45
68.73
69.62
69.18
VIP-Seg
71.59
72.64
72.12
72.35
72.90
72.63
68.87
71.38
70.13
70.84
71.72
71.28
Improvement
+0.54
-0.09
+0.23
+1.10
-0.76
+0.17
+1.22
+0.14
+0.68
+2.11
+0.10
+1.10
4.2
Comparison with Existing Methods
Results analysis on the S3DIS dataset. As shown in Table ! our VIP-Seg demonstrates superior performance on the S3DIS dataset. In the 2-way 1-shot setting. VIP-Seg achieves an average mloU 74.1%% surpassing the previous best method DyPolySeg (35 by 1.23 percentage points. In the 2-way 5-shot setting, VIP-Seg reaches an average mloU of 77.01 %, exceeding DyPolySeg by 1.35 percentage points. In the 3-way settings, VIP-Seg achieves 68.58% and 69. 69.30% mloU in 1-shot and 5-shot scenarios respectively, outperforming DyPolySeg by 2.34 and 1.38 percentage points. These consistent gains across different settings validate the effectiveness and robustness of our approach. The improvements stem from our DyPowerCony's ability to capture local geometric features and the VIP module's 's effective prototype refinement through multi-step reasoning.
Results analysis on the ScanNet dataset. Our VIP-Seg also exhibits impressive performance on the more challenging ScanNet dataset, as illustrated in Table 2 In the 2-way 1-shot setting, VIP-Seg achieves an average mIoU of 72.12%% outperforming the previous best method DyPolySeg 335 by 0.23 percentage points. In the 2-way 5-shot setting, VIP-Seg attains an average mloU of 72.63% surpassing DyPolySeg by 0. percentage points. In the 3-way 1-shot setting, VIP-Seg achieves 70.13% mloU, exceeding DyPolySeg by 0.68 percentage points. Particularly noteworthy is the 3-way 5-shot setting, where VIP-Seg achieves an average mloU 71.28%, outperforming DyPolySeg by 1.10 percentage points. These consistent improvements demonstrate VIP-Seg's 's ability to effectively handle complex indoor scenes with greater category diversity and varying levels point cloud density. The multi-step reasoning mechanism proves particularly beneficial in distinguishing semantically

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Table Ablation study on the key components of Vig-Seg.
Table 4: Ablation study on different components of the VIP module.
LoConv
DyHlCoovv
Mamba
VIP
So
S1
Avg
wway---shtt
3-way-1-shrx
PEM
PDM
Sg
S1
Avg.
Su
S.
Avg
x
49.87
52.34
$1.11
51,98
54.02
$3.00
x
53.64
56.21
54.93
48.37
51.86
50.12
53.64
56.21
$4.93
70.16
72.73
71.45
66.47
66.05
66.26
70.55
71.34
70.95
x
71.54
73.26
72.40
67.02
67.35
67.19
74.15
72.20
(a) Impoct of Iefrrescc Stops
(bi Injoct of Number of HiCon
[el Impart of Number of Encoder Layers
600
71.
75.0
00000 (4)
11.22
72.5
2..0
12.23
71:24
70.53
$3.74
02.5
1.3
3.5
2.5
2.5
4.5
10.0
1.0
2.8
25
2.0
1.0 3.2
2.8
2-3
2.5
0.0
Stegs
Namber 1llCom
Number Encoder Laguza
Figure 3: Parameter sensitivity analysis of VIP-Seg framework. (a) Impact of the number of reasoning steps in the VIP module on segmentation performance. (b) Impact of the number of HiConv experts in the DyPowerConv module. (c) Impact of encoder depth on model performance.
similar objects cluttered environments, and the performance gains across all settings further validate the generalization capability of our approach for PC-FSS.
4.3
Ablation Experiments
All results are reported under 2-way-1-shh settings on the So split of the S3DIS dataset.
4.3.1
Effectiveness of Different Components
Table 3 presents the ablation study on different components of our VIP-Seg. Using only LoConv or DyHiCon yields similar performance (50.04% and 51.11% mloU), while their combination in the DyPowerConv module improves results to 53.00%. Adding Mamba blocks provides a modest gain of 1.93 percentage points, reaching 54.93% mloU. Most significantly. incorporating the VIP module leads to substantial improvement of 17.95 percentage points, increasing mloU from 53. 53.00% 3,.00% to 70.95% without Mamba blocks. The complete architecture achieves the best performance of 74. 15% mloU, confirming that each component contributes to the overall framework, with the VIP module providing the most significant impact.
4.3.2 Analysis of VIP Module Components
Table 4 shows the ablation study on different components of our VIP module. Without either PEM or PDM (baseline), the model achieves 54. 44.93% and 50.12% mloU on 2-way and 3-way settings, respectively. Using only PEM significantly improves performance to 71.45% (+16.52%) for 2-way and 66. 26% (+16. 14%) for 3-way settings, demonstrating its effectiveness enhancing prototype discriminability. Similarly, using only PDM yields substantial improvements to 72.40% (+17.47%) for 2-way and 67. 67.19% 19% (+17.07%) for 3-way settings, indicating its ability to eliminate domain gaps. The complete VIP module with both PEM and PDM achieves the best performance across all settings, reaching 74. 15% for 2-way and 68. 15% for 3-way, validating the complementary effects of both components.
4.3.3 Hyperparameter Analysis
In Fig. 3 aa), we analyze the effect of different reasoning steps the VIP module. Performance im- proves from 1 step (68.74% mloU) to 4 steps (72.20% mloU), but decreases at 5 steps (71.12% mloU),

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Table 5: Impact of different geometric informa- tion in the explicit structure h on VIP-Seg per- formance.
Table Performance and computational effi-
ciency comparison.
Medbod
mloo
Parani
Pre tnin Time
Episodic Train
Total Timl
Setting
2-way- I-shot
3-way-I -shot
DOCNN
36.34
0.8h
4.8
S1
S:
AmMPII 59
53.77
0.57
4.3h
5.5
9.3
70.54
73.03
66.84
53
55.89
0.35
6.0h
01.h
6.2
76.25
67.05
1.1h
Po 29]
75.12
Seg-NN 16
06:84
0.24
0.0h
0.5
035
PPw. Ph. Pyl.
72.30
76.09
74.15
68.35
68.58
VIP-Se
2.6
indicating optimal refinement occurs at 4 steps while excessive iterations cause over-processing. Fig. ((b) shows the impact of HiConv layers. Performance increases from 1 layer (70.69% mloU) to 3 layers 700 mloU), then decreases with 4 layers (71.8, (71.8%% mloU) and 5 layers (71 11.0%% mloU), suggesting 3 layers optimally balance feature capture and model complexity. Fig. c) depicts the effect of encoder layers. The model improves dramatically from layer (62.50% mIoU) to 3 layers (72.20% mloU), gaining 9.70 percentage points, but declines with 4 layers (71.65% mloU) and 5 layers (70. 16% mloU) due to overfitting. These results validate our architectural design choices.
4.3.4 Impact of Explicit Geometric Structure
Table 5 examines the influence of different geometric information in the explicit structure hy used in our DyPowerConw Using only neighboring points [p,] achieves 71.79% and 66.79% mloU on 2-way and 3-way settings, respectively. Adding center points [p;, improves performance to 72.96% (+1.17%) and 67.21% % (+0.42%), demonstrating the importance of spatial context. Further incorporating relative displacement [p, p.] brings the performance to 73. 73% (+0. 19%) and 67. 67.49% (+0.28%), capturing local geometric relationships more effectively. The complete representation that includes Euclidean distance [pp: - P3 achieves the best results of 74.15% and 68. 15% mloU across 2-way and 3-way settings, with improvements 1.00% and 0.66% respectively, validating the complementary nature of different geometric features in enhancing the discriminative power of our DyPowerConv module.
4.4 Computational Complexity
Table O compares the computational efficiency of VIP-Seg with existing methods. Despite having
2. 76M parameters, our approach eliminates trainingg significantly reducing overall training time. Compared to pre-training methods like PAP3D 9 (4.7h total) and 2CBR [55] (6.2h total), VIP-Seg requires only 0.9h of training-ane 81-85% reduction in total training time. When compared to Seg-PN (56), another pre-training-free method, VIP-Seg achieves a 7.36 percentage points higher mloU (72.20% vs. 64.84%) with only 0.4h additional training time, demonstrating a superior balance between computational efficiency and performance. Notably, VIP-Seg achieves the highest performance among all methods while maintaining competitive training efficiency.
5 Conclusion
In this paper, we propose VIP-Seg, novel pre-training-fres framework for point cloud few-shot semantic segmentation that effectively addresses the challenges of intra-class diversity and domain gaps. Our approach introduces two key innovations: the VIP module, which employs multi-step reasoning process to progressively refine prototype features, and DyPowerCony, which adaptively models local geometrie structures through learnable power functions. Extensive experiments on S3DIS and ScanNet datasets demonstrate that VIP-Seg significantly outperforms current statc-of-the- art methods across various few-shot settings. Limitations: While VIP-Seg eliminates the need for pre-training, the episode- bassed training paradigm still requires substantial computational resources, and performance may degrade with extremely sparse point clouds or significant occlusions. The optimal number of reasoning steps in the VIP module is dataset-dependent and may require tuning for different scenarios. Future Work: Incorporating multi-modal information (e.g., RGB images, depth maps) could enhance robustness to sparse or occluded point clouds, and exploring parameter-effiiien integration of large-scale pre-trained models could potentially combine the benefits of pre-training with our efficient shot learning approach.
10

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Acknowledgments
This work was supported in part by the European Union's Horizon 2024 Research and Innovation Programme for the Marie Aklodowska-Curte Actions under Grant No. 1012111 This work was also supported by the UKRI Future Leaders Fellowship [MR/VO22333/1] (RoboHike). Shuting He was sponsored by Shanghai Pujiang Programme 24PJD030 and Natural Science Foundation of Shanghai 25ZR 4021388.
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Reviews:
[{"review_id": "WuUhsubXLN", "reviewer": "Reviewer_Ccpr", "summary": "This paper tackles Point Cloud Few-Shot Semantic Segmentation by introducing VIP-Seg, a pre-training-free framework. Two key innovations are proposed: 1) Dynamic Taylor Convolution, which models local geometric structures via a learnable Taylor-series-inspired polynomial fitting, combining lower-order trigonometric encoding with dynamic higher-order experts, 2) Visual Introspective Prototype Module, which consists of alternating Prototype Enhancement Modules and Prototype Difference Modules in a multi-step process which seeks to address intra-semantic diversity and domain gaps. A gating fusion network then aggregates stepwise predictions. Extensive experiments on S3DIS and ScanNet demonstrate 5–10% absolute mIoU gains over prior work, while eliminating costly pre-training.", "questions": "1. As mentioned above, can the author provide an more intuitive illustration or visualization on how the Dynamic Taylor Convolution relates to a Taylor Series? Or clarify further the use of this naming convention? See Weaknesses (1) and (2).\n2. In Section 3.3.4, the constructed weight $w_j$ is computed within HiConv, is this correct? In other words, $\\mathbf{W}_h$ is within HiConv?\n3. In Section 3.2, the authors mentioned Mamba blocks being added to the VIP module but I don’t see any description of how those blocks are added. Can you elaborate on this? \n4. The Prototype Enhancement Module resembles the Transformer architecture. Have you tried replacing it with Transformer? What would the performance differences be?\n5. Can you please address Weaknesses (3) and (4) given above, with regard to comparative inference times, GPU-memory profiling and network parameter numbers across methods?", "limitations": "No. As mentioned in Weakness (5), the main paper doesn't have a dedicated limitations section and the supplementary contains a very general list of limitations.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "# Strengths\n1. The paper is well-structured and easy to understand.\n2. The experiments are quite comprehensive and shows that the proposed method outperforms the state-of-the-art approaches.\n\n# Weaknesses\n1. The authors claims that the Dynamic Taylor Convolution is inspired from Taylor Series. However, instead of a first and second order approximation of the pointcloud local geometry, the low-order and high-order convolution seem more like two distinct layer types with differing receptive fields. Furthermore, HiConv is not strictly looking at higher order geometric information but is a mixture of experts. As such, I find the naming confusing.\n2. Expanding on (1) above, there is insufficient discussion on other methods which consider differing receptive fields for point set convolution. This discussion should be added to motivate the authors' particular choice of DyTaylorConv. With that being said, the Taylor-series metaphor doesn't appear to be theoretically novel.\n3. I appreciate the provided computational efficiency analysis, but there is no inference-time latency or GPU-memory profiling provided, which leaves the real-time applicability unclear.\n4. VIP-Seg has over 10x the number of parameters as Seg-PN (2.77M params vs. 0.24M params). It's not clear if the performance improvements are due to the larger model size only and not the architectural improvements.\n5. The authors only conclude the limitation in one sentence in the main paper and discuss some very general limitations in the supplementary. This makes it difficult to understand when the method might fail.\n\n## Minor comments\n1. Performance depends on many settings (number of HiConv experts, reasoning steps S, gating weights, geometric priors) which seems clunky.\n2. Experiments results should not be listed as contributions.", "was_revised": true, "final_justification": "Authors have addressed concerns during the discussion phase and have acknowledged aspects of the manuscript which require revisions/changes. Therefore, I raise my rating, and ask that authors make the previously discussed changes to improve readability of the paper.", "rebuttal": "Thank you very much for taking the time to review our paper and for your recognition of our work along with constructive feedback. We now provide detailed responses to your concerns, hoping to address your questions comprehensively.\n\n> **Q1: As mentioned above, can the author provide a more intuitive illustration or visualization on how the Dynamic Taylor Convolution relates to a Taylor Series? Or clarify further the use of this naming convention?**\n\n**R1:** Thank you for this important question. We acknowledge that our description of the Taylor series analogy in the original manuscript may have caused confusion. Let us clarify this design philosophy:\n\nTraditional Taylor series approximates functions using polynomials: f(x) ≈ f(x₀) + f'(x₀)(x-x₀) + f''(x₀)(x-x₀)²/2! + ...\n\nIn our DyTaylorConv, we apply this concept to point cloud local geometric modeling:\n- **LoConv (low-order terms)**: Similar to the constant and first-order terms in Taylor expansion, capturing basic geometric information\n- **DyHiConv (high-order terms)**: Similar to higher-order derivative terms, capturing complex local geometric details through learnable power functions (|fⱼ-fᵢ|+ε)^pₜ\n\nThe key innovation lies in: we replace fixed powers with **learnable power parameters pₜ**, enabling the model to adaptively determine the optimal order of local geometric representation. Unlike traditional methods with fixed receptive fields, our approach can dynamically adjust to accommodate different local geometric complexities.\n\n>  **Q2: In Section 3.3.4, the constructed weight w is computed within HiConv, is this correct? In other words, is w within HiConv?**\n\n**R2:** Yes, the weight w is indeed computed within HiConv. Specifically, for the t-th expert, the weight wₜ(pⱼ) is dynamically generated based on geometric information hⱼ = [pᵢ, pⱼ, pⱼ-pᵢ, ‖pⱼ-pᵢ‖], computed through wⱼ = Wₕhⱼ. This design enables each expert to adaptively adjust weights according to local geometric features, achieving more precise feature extraction.\n\n> **Q3: In Section 3.2, the authors mentioned Mamba blocks being added to the VIP module but I don't see any description of how those blocks are added. Can you elaborate on this?**\n\n**R3:** Thank you for pointing out this unclear description. We need to clarify that Mamba blocks are integrated into the **encoder** of VIP-Seg, not the VIP module. The specific architecture is as follows:\n- Each encoder block consists of DyTaylorConv + Mamba block\n- The output of DyTaylorConv serves as input to the Mamba block\n- This forms a \"local-global\" feature extraction strategy: DyTaylorConv captures fine-grained local geometric features, while Mamba blocks model long-range dependencies\n- We provide detailed computation flow of Mamba blocks in Figure 1 of the supplementary material\n\n> **Q4: The Prototype Enhancement Module resembles the Transformer architecture. Have you tried replacing it with Transformer? What would the performance differences be?**\n\n**R4:** Thank you for this suggestion. We conducted corresponding experiments with the following results:\n\n| Setting | 2-way 1-shot |\n|---------|-------------|\n| Transformer block | 69.76 (S0), 70.02 (S1), 69.89 (Avg) |\n| VIP (ours) | 73.50 (S0), 74.92 (S1), 74.21 (Avg) |\n\nVIP module significantly outperforms the Transformer alternative. The main reasons are: (1) VIP considers both self-correlation and cross-correlation simultaneously. (2) PDM is specifically designed to learn difference information between support and query sets. (3) PEM and PDM work alternately, achieving gradual prototype refinement.\n\n> **Q5: Can you please address Weaknesses (3) and (4) given above, with regard to comparative inference times, GPU-memory profiling and network parameter numbers across methods?**\n\n**R5:** Thank you for this important suggestion. We designed a VIP-Seg_small variant for fair comparison and provide detailed efficiency analysis:\n\n| Method | Params(M) | Inference Time(s) | GPU Usage(G) | 2-way 1-shot | | |\n|--------|-----------|-------------------|--------------|---|---|---|\n| | | | | S0 | S1 | Avg |\n| Seg-PN | 0.24M | 46s | 1.3G | 63.15 | 64.32 | 63.74 |\n| VIP-Seg_small | 0.26M | 51s | 1.6G | 67.21 | 68.53 | 67.87 |\n| VIP-Seg (full) | 2.77M | 64s | 2.8G | 73.50 | 74.92 | 74.21 |\n\nKey observations:\n1. **VIP-Seg_small**: Still achieves 4.13 mIoU improvement over Seg-PN with similar parameter count\n2. **Efficiency trade-off**: Full VIP-Seg, despite having more parameters, brings significant performance gains (+10.47 mIoU)\n3. **Practicality**: Limited inference time increase (18 seconds) with reasonable GPU memory usage\n\nThis demonstrates that our performance improvements primarily stem from **architectural innovations** rather than merely parameter increase.\n\nIf you feel we have addressed all of your concerns, we would appreciate it if you could improve our score. Thank you again for your constructive feedback that helps strengthen our work."}, {"review_id": "TWVnffJK8Z", "reviewer": "Reviewer_JddV", "summary": "The paper proposes VIP-Seg framework for Point Cloud Few-Shot Semantic Segmentation with the two major contributions with promising performance validated on S3DIS and ScanNet:\n1) VIP Module: Introduces a multi-step reasoning mechanism inspired by Chain-of-Thought, consisting of a Prototype Enhancement Module (PEM) and a Prototype Difference Module (PDM).\n2) DyTaylorConv: A Dynamic Taylor Convolution module that models local geometric structures via learnable polynomial approximations.", "questions": "Besides the weakness, I may have other concerns on the technical aspect of this work:\n1. Inference Cost: What is the inference time or FLOPs for VIP-Seg compared to Seg-PN or other baselines?\n2. Domain Generalization: How does your method perform under cross-domain settings or on noisy/real-world sensor data?\n3. Ablation on PEM vs PDM Order: Would reversing the order (PDM-PEM) in the reasoning chain yield similar performance?", "limitations": "While the paper eliminates the need for pre-training, it introduces complex modules (e.g., DyTaylorConv, multi-step VIP reasoning, gating fusion), which increase inference-time computation. The paper discusses domain gaps and proposes PDM to address them, but does not evaluate on cross-domain benchmarks.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strength:\n1) The paper is technically sound with clear mathematical formulation.\n2) Ablation studies and architectural design decisions are well-motivated and extensively analyzed \n3) Provides significant gains across datasets and settings \n\nWeakness: \n1) Although this paper tries to analogy to chain-of-thought reasoning, however I feel this work abuse the words to describe conventional prototype refinement and enhancement process in few-shot segmentation\n2) While training time is reduced, DyTaylorConv and the multi-step reasoning mechanism increase inference complexity and parameter count. No comparison of inference speed or resource usage is provided.\n3) Though cross-domain concerns are discussed, no explicit evaluation is performed on domain-shifted data (e.g., synthetic to real, indoor to outdoor).\n4) No justification is given for why 4 reasoning steps are optimal (aside from empirical observation).", "was_revised": true, "final_justification": "The authors have addressed most of my questions, hence I tend to increase the score to \"borderline accept\"", "rebuttal": "Thank you for your time and suggestions on our paper. We have conducted additional experiments and inference time testing, hoping to address all your concerns.\n\n> **Q1: Although this paper tries to analogy to chain-of-thought reasoning, however I feel this work abuse the words to describe conventional prototype refinement and enhancement process in few-shot segmentation.**\n\n**R1:** Traditional prototype refinement and enhancement processes are typically single-stage approaches. Inspired by chain-of-thought reasoning, we employ multi-stage thinking during the prototype acquisition process to obtain more accurate semantic prototypes. Our experimental results confirm that this approach provides significant improvements in few-shot tasks. Unlike conventional methods that perform prototype refinement in one step, our VIP module mimics human reasoning by alternating between enhancement (PEM) and reflection/correction (PDM) stages, progressively refining prototypes through iterative reasoning steps. This multi-step process allows the model to gradually align feature distributions between support and query sets, which is conceptually similar to how humans approach complex problems through step-by-step reasoning.\n\n> **Q2: While training time is reduced, DyTaylorConv and the multi-step reasoning mechanism increase inference complexity and parameter count. No comparison of inference speed or resource usage is provided.**\n\n**R2:** Although DyTaylorConv and the multi-step reasoning mechanism increase inference complexity and parameter count, the substantial performance gains achieved are worthwhile. Current computational resources can fully satisfy the computational requirements of our DyTaylorConv and multi-step reasoning mechanism. Additionally, we have supplemented the inference time and memory consumption analysis. We compared Seg-PN, VIP-Seg_small (we reduced model parameters for fair comparison with Seg-PN), and VIP-Seg:\n\n| Method | Params(M) | Inference Time(s) | GPU Usage(G) | 2-way 1-shot | | |\n|--------|-----------|-------------------|--------------|---|---|---|\n| | | | | S0 | S1 | Avg |\n| Seg-PN | 0.24M | 46s | 1.3G | 63.15 | 64.32 | 63.74 |\n| VIP-Seg_small | 0.26M | 51s | 1.6G | 67.21 | 68.53 | 67.87 |\n| VIP-Seg (full) | 2.77M | 64s | 2.8G | 73.50 | 74.92 | 74.21 |\n\nThe results show that even with similar parameter counts, VIP-Seg_small outperforms Seg-PN by 4.13 mIoU, demonstrating the effectiveness of our architectural innovations.\n\n> **Q3: Though cross-domain concerns are discussed, no explicit evaluation is performed on domain-shifted data (e.g., synthetic to real, indoor to outdoor).**\n\n**R3:** Since you didn't specify which task for domain adaptation experiments, and considering time constraints, we conducted experiments on domain adaptive point cloud classification. We embedded the VIP module into the encoder of MLSP[1] and obtained the following experimental results:\n\n| Method | M→S | M→S* |\n|--------|-----|-----|\n| MLSP | 83.7±0.4 | 55.4±1.8 |\n| MLSP+VIP | 85.2±0.5 | 58.6±1.2 |\n\nWhere M represents the ModelNet40 dataset, S represents the ShapeNet dataset, and S* represents the ScanNet dataset. ModelNet40 is a synthetic dataset, while ShapeNet and ScanNet are real-world datasets. The experiments demonstrate that the addition of our module improves MLSP's performance, validating the generalizability of our approach across different domains.\n\n[1] Liang H, Fan H, Fan Z, et al. Point cloud domain adaptation via masked local 3d structure prediction[C]//European conference on computer vision. Cham: Springer Nature Switzerland, 2022: 156-172.\n\n> **Q4: No justification is given for why 4 reasoning steps are optimal (aside from empirical observation).**\n\n**R4:** Thank you for raising this question. This problem is similar to determining how many neurons a fully connected layer should have. Although the universal approximation theorem provides theoretical foundation, currently no one can accurately specify the optimal number of neurons per layer in fully connected networks. Therefore, in our method, we can only determine the optimal number of reasoning steps through ablation experiments that yield the best experimental results. This is also a common approach used by peers to find optimal hyperparameter settings. From a practical perspective, 4 steps provide sufficient iterations for the PEM-PDM alternation to converge to stable prototypes while avoiding over-processing that occurs with more steps.\n\n> **Q5: Inference Cost: What is the inference time or FLOPs for VIP-Seg compared to Seg-PN or other baselines?**\n\n**R5:** Please refer to Q2 above for detailed inference cost analysis.\n\n> **Q6: Domain Generalization: How does your method perform under cross-domain settings or on noisy/real-world sensor data?**\n\n**R6:** Please refer to Q3 above for domain generalization experiments.\n\n> **Q7: Ablation on PEM vs PDM Order: Would reversing the order (PDM-PEM) in the reasoning chain yield similar performance?**\n\n**R7:** Following your suggestion, we reversed the order of PEM and PDM. We found that both orders can achieve high model performance, but the PEM-first-then-PDM order achieves higher performance:\n\n| Setting | 2-way 1-shot | | |\n|---------|---|---|---|\n| | S0 | S1 | Avg |\n| PDM→PEM | 72.15 | 73.07 | 72.61 |\n| PEM→PDM | 73.50 | 74.92 | 74.21 |\n\nThis suggests that enhancing prototype discriminability first (PEM) followed by domain gap mitigation (PDM) is more effective than the reverse order, aligning with our intuition that establishing strong prototypes before refining domain-specific differences leads to better performance.\n\nThank you again for your constructive feedback, which has helped us strengthen our work significantly."}, {"review_id": "em6MtuzeQp", "reviewer": "Reviewer_jzmm", "summary": "This paper introduces VIP-Seg, a pre-training-free framework for Point Cloud Few-Shot Semantic Segmentation (PC-FSS), incorporating two key modules: Dynamic Taylor Convolution (DyTaylorConv) and a Visual Introspective Prototype (VIP) reasoning mechanism composed of PEM and PDM submodules. The proposed method aims to address intra-class diversity and domain gaps, achieving promising results on S3DIS and ScanNet.", "questions": "1. Could the authors provide inference-time cost (e.g., FPS or latency)? While the proposed pre-training-free design is claimed to offer faster deployment and efficiency, Table 6 only reports training-related timings. Would the authors be able to include inference speed or FPS comparisons to more directly support this claim?\n2. Can the authors clarify the reasoning behind the significantly larger parameter count? Table 6 shows that the proposed model introduces noticeably more parameters than other methods. Could the authors elaborate on the design trade-offs involved, and whether this affects scalability, memory usage, or real-time deployment?\n3. Have the authors considered evaluating under newer few-shot segmentation protocols? Recent method [1] proposes an more challenging settings for PC-FSS. Would the authors consider testing their method under such settings to further validate its generalization ability?\n[1] An Z, Sun G, Liu Y, et al. Rethinking few-shot 3d point cloud semantic segmentation[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2024: 3996-4006.", "limitations": "1. Lack of Inference Efficiency Evaluation：While the authors claim that the proposed pre-training-free design facilitates faster deployment and efficiency, Table 6 only reports pre-training time, episode training time, and total training time. However, it completely omits inference time or frame-per-second (FPS) metrics, which are crucial for evaluating actual runtime efficiency and supporting the claim of faster speed. Without this, the claim of efficiency superiority remains unsubstantiated.\n2. Clarify and Justify Model Size and Its Impact: According to Table 6, the proposed method introduces a noticeably larger number of parameters compared to existing approaches. The author should include a brief discussion or ablation to justify the design choice. Such clarification could also reinforce the fairness of the comparison and address potential concerns about scalability to larger or real-time scenarios.\n3. Missing Evaluation under Updated Few-Shot Settings: Recent works (e.g., [1]) have introduced more reasonable and challenging settings for few-shot point cloud segmentation. Including evaluations under such settings could further demonstrate the robustness and generalization capacity of the proposed approach. \n[1] An Z, Sun G, Liu Y, et al. Rethinking few-shot 3d point cloud semantic segmentation[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2024: 3996-4006.  \n4. To enhance the reproducibility and facilitate future research, it would be highly beneficial if the authors could consider releasing the source code and trained models. Additionally, providing some qualitative visualizations or the segmentation results would offer valuable insight into the behavior and limitations of the proposed method.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "1. Practical Motivation: Removing the need for pre-training improves model adaptability and reduces computational cost, which is valuable for real-world 3D segmentation scenarios, especially in cross-domain settings.\n2. Conceptual Novelty: The VIP module, inspired by multi-step reasoning (Chain-of-Thought), introduces a novel perspective to prototype refinement in few-shot segmentation.\n3. DyTaylorConv: The idea of incorporating Taylor series principles into convolution is interesting and adds interpretability to the modeling of local geometry. \n4. Strong Empirical Results: The method demonstrates consistent gains on two benchmarks, indicating good generalization and effective design.", "was_revised": true, "final_justification": "The authors' responses have largely addressed my questions. I have decided to raise my previous rate and hold an \"accept\" stance on the paper.", "rebuttal": "Dear Reviewer jzmm, thank you very much for dedicating your valuable time and for your positive evaluation of our method. To fully address your questions, we have conducted additional experiments. We sincerely hope to resolve your concerns.\n\n> **Q1: Could the authors provide inference-time cost (e.g., FPS or latency)? While the proposed pre-training-free design is claimed to offer faster deployment and efficiency, Table 6 only reports training-related timings. Would the authors be able to include inference speed or FPS comparisons to more directly support this claim?**\n\n**R1:** Thank you for this important question. We have conducted comprehensive inference efficiency analysis comparing our method with baselines. The detailed results are shown below:\n\n| Method | Params(M) | Inference Time(s) | GPU Usage(G) | 2-way 1-shot | | |\n|--------|-----------|-------------------|--------------|---|---|---|\n| | | | | S0 | S1 | Avg |\n| Seg-PN | 0.24M | 46s | 1.3G | 63.15 | 64.32 | 63.74 |\n| VIP-Seg_small | 0.26M | 51s | 1.6G | 67.21 | 68.53 | 67.87 |\n| VIP-Seg (full) | 2.77M | 64s | 2.8G | 73.50 | 74.92 | 74.21 |\n\nOur analysis shows that while VIP-Seg introduces additional computational overhead due to the multi-step reasoning process, the performance gains significantly outweigh the computational costs. Importantly, our pre-training-free design eliminates the lengthy pre-training phase (typically 4-6 hours), making deployment much more efficient despite slightly higher inference costs.\n\n> **Q2: Can the authors clarify the reasoning behind the significantly larger parameter count? Table 6 shows that the proposed model introduces noticeably more parameters than other methods. Could the authors elaborate on the design trade-offs involved, and whether this affects scalability, memory usage, or real-time deployment?**\n\n**R2:** Thank you for this insightful question. The larger parameter count is primarily due to two key components:\n\n1. **DyTaylorConv Module**: Contains multiple high-order convolution experts (8 experts by default) with learnable power parameters and dynamic attention weights\n2. **VIP Module**: Implements multi-step reasoning with both PEM and PDM components requiring additional transformation matrices\n\nHowever, we address this concern by providing VIP-Seg_small variant that maintains comparable parameter count to existing methods while still achieving significant performance improvements. The design trade-offs are:\n\n- **Performance vs. Efficiency**: Full VIP-Seg prioritizes maximum performance for applications where accuracy is critical\n- **Scalability**: VIP-Seg_small demonstrates that our architectural innovations are effective even with reduced parameters\n- **Memory Usage**: Our GPU memory consumption (2.8G) remains reasonable for modern hardware\n- **Real-time Deployment**: The inference time increase (18s) is acceptable for many practical applications\n\n> **Q3: Have the authors considered evaluating under newer few-shot segmentation protocols? Recent method [1] proposes more challenging settings for PC-FSS. Would the authors consider testing their method under such settings to further validate its generalization ability?**\n\n**R3:** Thank you for this excellent suggestion. We have implemented and evaluated our method under the challenging settings proposed by COSeg [1]. Although COSeg requires significant computational resources and pre-training (making it more time-consuming), we adapted their sampling strategy and point count settings to our VIP-Seg framework. The experimental results are shown below:\n\n**S3DIS Dataset:**\n| Method | 2-way-1-shot | | | 2-way-5-shot | | |\n|--------|---|---|---|---|---|---|\n| | S0 | S1 | Avg | S0 | S1 | Avg |\n| AttMPTI | 31.09 | 29.62 | 30.36 | 39.53 | 32.62 | 36.08 |\n| QGE | 33.45 | 30.95 | 32.20 | 40.53 | 36.13 | 38.33 |\n| QGPA | 25.52 | 26.26 | 25.89 | 30.22 | 32.41 | 31.32 |\n| COSeg | 37.44 | 36.45 | 36.95 | 42.27 | 38.45 | 40.36 |\n| VIP-Seg(ours) | 39.46 | 44.03 | 41.75 | 45.11 | 41.68 | 43.40 |\n\n**ScanNet Dataset:**\n| Method | 2-way-1-shot | | | 2-way-5-shot | | |\n|--------|---|---|---|---|---|---|\n| | S0 | S1 | Avg | S0 | S1 | Avg |\n| AttMPTI | 25.99 | 23.88 | 24.94 | 30.41 | 27.35 | 28.88 |\n| QGE | 26.85 | 25.17 | 26.01 | 28.35 | 31.49 | 29.92 |\n| QGPA | 21.86 | 21.47 | 21.67 | 30.67 | 27.69 | 29.18 |\n| COSeg | 28.72 | 28.83 | 28.78 | 35.97 | 33.39 | 34.68 |\n| VIP-Seg(ours) | 29.65 | 29.51 | 29.58 | 36.10 | 37.31 | 36.71 |\n\nOur method demonstrates consistent superiority under these more challenging settings, validating the robustness and generalization ability of our approach across different evaluation protocols.\n\n> **Q4: To enhance the reproducibility and facilitate future research, it would be highly beneficial if the authors could consider releasing the source code and trained models. Additionally, providing some qualitative visualizations or the segmentation results would offer valuable insight into the behavior and limitations of the proposed method.**\n\n**R4:** Thank you very much for your interest in our work. Our code is already prepared and will be made publicly available immediately upon paper acceptance. We appreciate your patience in waiting for the release. Additionally, due to rebuttal format limitations, we cannot provide visualization results in this text box, but we will include comprehensive qualitative visualizations and segmentation results in the final accepted version to offer valuable insights into our method's behavior and limitations.\n\nWe believe these comprehensive responses address your concerns about inference efficiency, parameter justification, evaluation under challenging settings, and reproducibility. If you feel we have addressed all of your concerns, we would appreciate it if you could improve our score. Thank you again for your constructive feedback that helps strengthen our work.\n\n[1] An Z, Sun G, Liu Y, et al. Rethinking few-shot 3d point cloud semantic segmentation[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2024: 3996-4006."}, {"review_id": "lOqmRW7Ee0", "reviewer": "Reviewer_fKVb", "summary": "This paper mainly addresses the two challenges in the point cloud few-shot semantic segmentation, including feature misalignment between different domains and semantic class representation differences. In feature extraction, DyTaylorConv captures detained point features and improves intra-semantic difference. In addition, the main contribution lies in adopting the idea of Chain-of-Thought and using Visual Introspective Prototype (VIP), consisting of iterative PEM and PDM, for addressing the main challenges. Experiments show the SOTA performance comparing the current works on indoor datasets, and ablation studies show the effectiveness of VIP for few-shot learning in semantic segmentation.", "questions": "- Further explain the relationship between DyTaylorConv and the Visual Introspective Prototype. A question should be answered: why can the two contributions be put in one paper?\n- Perform inferencing efficiency evaluation to show the efficiency of applying the iterative VIP.\n- Provide the visualization of the segmentation results. \n- I suggest further explanation of the experiment results. For example, explain why improvement is best on the 3-way-5-shot, which gives the most examples for few-shot learning, addressing the main challenges through the proposed method. This will further show the significance of the contributions.\n\n- For FEM and FDM, the current statements are still little confusing about how the structure designs improve the discriminability of prototype features and reduce intra-class diversity. I suggest directly explaining the function of each design for the challenges to make the contributions clearer.", "limitations": "Yes. The authors clearly claim the limitation on single modality and single field application.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "strengths:\n\n- The idea of using chain-of-thought in the 3D vision task is interesting, and the ablation studies show its significant improvement on the semantic segmentation performance. \n- Sufficient ablation studies are performed. Most hyperparameters are studied through experiments.\n\nweaknesses:\n\n- Contribution Confusion: The authors include DyTaylorConv in the contributions. However, the ablation studies show DyTaylorConv contributing little to the final performance, and the logical relationship between DyTaylorConv and the core contribution of the paper, Visual Introspective Prototype, is confusing.\n- No Inferencing Efficiency Evaluation: Though the training efficiency is shown, the iterative VIP structure may take more time in the inference compared to the other methods. But no inferencing efficiency evaluation is provided.\n- Few visualizations: Few visualizations are provided in this paper, making it unclear how complex indoor scenes the proposed method is applied to.", "was_revised": true, "final_justification": "After the discussion period, the main concerns of efficiency has been resolved. In addition, in the rebuttal, the authors claim the function of DyTaylorConv, which is used for local feature extraction. This classifies my concerns of how the two contributions can be put in one paper since they are both the main components of the few-shot point cloud segmentation but the performance improvement of this component is small, which makes importance of this contribution a little weak. Thus, the final rating keeps borderline accept because of this minor drawback.", "rebuttal": "We sincerely thank the reviewer for dedicating valuable time and for the recognition of our paper along with valuable suggestions. To fully address your concerns, we have supplemented additional experiments and explanations, hoping to resolve all your questions.\n\n> **Q1: Further explain the relationship between DyTaylorConv and the Visual Introspective Prototype. A question should be answered: why can the two contributions be put in one paper?**\n\n**R1:** Thank you for this important question. Few-shot point cloud semantic segmentation faces not only the challenge of reducing domain gaps but also the fundamental issue of effective feature extraction that influences the quality of semantic prototype representation. Previous works predominantly used DGCNN as backbone and required pre-training. Seg-PN discovered that the DGCNN backbone significantly affects the final semantic prototype features for each category.\n\nTo address this issue, we propose DyTaylorConv inspired by Taylor series principles. This convolution effectively extracts discriminative geometric information from local point cloud neighborhoods, thereby providing high-quality semantic class prototypes to the VIP module. The relationship is **complementary and synergistic**:\n\n- **DyTaylorConv** focuses on **local feature extraction**: It captures fine-grained geometric details and reduces intra-class diversity at the feature level\n- **VIP Module** focuses on **global prototype refinement**: It addresses domain gaps and further refines prototypes through multi-step reasoning\n\nBoth contributions are essential for few-shot point cloud semantic segmentation: DyTaylorConv provides better input features, while VIP ensures these features are optimally utilized for cross-domain prototype matching. This forms a complete pipeline from local feature extraction to global prototype optimization.\n\n> **Q2: Perform inferencing efficiency evaluation to show the efficiency of applying the iterative VIP.**\n\n**R2:** We have evaluated our method's performance during the inference stage. The results are shown in the table below. We also provide VIP-Seg_small with reduced model dimensions for fair comparison with Seg-PN:\n\n| Method | Params(M) | Inference Time(s) | GPU Usage(G) | 2-way 1-shot | | |\n|--------|-----------|-------------------|--------------|---|---|---|\n| | | | | S0 | S1 | Avg |\n| Seg-PN | 0.24M | 46s | 1.3G | 63.15 | 64.32 | 63.74 |\n| VIP-Seg_small | 0.26M | 51s | 1.6G | 67.21 | 68.53 | 67.87 |\n| VIP-Seg (full) | 2.77M | 64s | 2.8G | 73.50 | 74.92 | 74.21 |\n\nThe experimental results demonstrate that our method is not only efficient but also maintains high accuracy. With current computational resources, real-time deployment is completely feasible. The modest inference time increase (18s for full model, 5s for small model) is justified by the substantial performance gains.\n\n> **Q3: Provide the visualization of the segmentation results.**\n\n**R3:** Due to space limitations, we only showed the query prediction results in Figure 2. Due to conference requirements during the rebuttal phase, we cannot provide visualization results in this text box. We will provide more comprehensive visualization results in the final version after paper acceptance. Additionally, we have prepared our open-source code and will release it immediately upon acceptance.\n\n> **Q4: I suggest further explanation of the experiment results. For example, explain why improvement is best on the 3-way-5-shot, which gives the most examples for few-shot learning, addressing the main challenges through the proposed method.**\n\n**R4:** Thank you for this insightful observation. The superior performance on 3-way-5-shot settings can be explained by several key factors:\n\n1. **Richer Prototype Information**: With 5 support samples per class, our VIP module has more diverse examples to learn from, enabling better prototype initialization and more effective multi-step refinement.\n\n2. **Enhanced Domain Gap Mitigation**: More support samples provide better statistical coverage of intra-class variations, allowing our PDM to more accurately model the differences between support and query distributions.\n\n3. **Improved Multi-step Reasoning**: The iterative PEM-PDM process benefits significantly from richer support information. Each reasoning step can make more informed decisions when more examples are available.\n\n4. **Reduced Overfitting to Single Examples**: In 1-shot scenarios, the model might overfit to specific support instances. With 5-shot, the VIP module can learn more generalizable prototype representations.\n\nThis validates our core hypothesis that multi-step reasoning becomes increasingly effective as more contextual information becomes available, demonstrating the scalability and significance of our contributions. It should be noted that the effect of our method in other settings is also significantly improved compared to the baseline.\n\n> **Q5: For PEM and PDM, the current statements are still little confusing about how the structure designs improve the discriminability of prototype features and reduce intra-class diversity. I suggest directly explaining the function of each design for the challenges to make the contributions clearer.**\n\n**R5:** Thank you for this clarification request. Let us explicitly explain how each module addresses the specific challenges:\n\n(1) **Prototype Enhancement Module (PEM) - Addressing Intra-class Diversity:**\n- **Self-correlation Enhancement**: Computes internal structural relationships within support/query features, learning invariant patterns that remain consistent across different instances of the same class\n- **Cross-correlation Enhancement**: Identifies shared discriminative features between support and query sets, filtering out instance-specific variations while preserving class-relevant information\n- **Function**: Enhances prototype discriminability by emphasizing consistent class characteristics while suppressing intra-class variations\n\n(2) **Prototype Difference Module (PDM) - Addressing Domain Gaps:**\n- **Difference Computation**: Explicitly models the distribution mismatch (△G = F'ᵀ_q F'_q - F'ᵀ_s F'_s) between support and query feature statistics\n- **Adaptive Adjustment**: Uses this difference information to recalibrate prototypes via sigmoid(△G) ⊙ F^e_p, effectively compensating for domain-specific biases\n- **Function**: Bridges domain gaps by learning domain-invariant representations that work across different data distributions\n\n(3) **Synergistic Effect**: The alternating PEM-PDM process creates a feedback loop where PEM establishes strong discriminative prototypes, PDM adjusts them for domain compatibility, and this process iterates to achieve optimal prototype representations that are both discriminative and domain-robust.\n\nThank you again for these valuable questions that help clarify our contributions and strengthen the paper's clarity."}]

Author rebuttal:

Meta-review: This paper receives 3x borderline accepts and 1x accept. The reviewers think that the paper is novel, technically sound,  well-motivated,  and shows strong results. The authors also have done a good job in clarifying the doubts of the reviewer in the rebuttal and the author-reviewer discussion phase. The authors are encouraged to put all the clarifications made during the rebuttal into the final paper to improve the clarity. The ACs follow the suggestions of the reviewers to accept the paper.
Decision: Accept (poster)