Multimodality Helps Few-shot 3D Point Cloud Semantic Segmentation
Authors: Zhaochong An
Guolei Sun
Yun Liu
Runjia Li
Min Wu
Ming-Ming Cheng
Ender Konukoglu
Serge Belongie
Venue: ICLR 2025 Spotlight (2025)
Keywords: 3D segmentation
3D point cloud
few-shot segmentation
multimodality
few-shot point cloud semantic segmentation
Primary area: transfer learning, meta learning, and lifelong learning

Abstract: Few-shot 3D point cloud segmentation (FS-PCS) aims at generalizing models to segment novel categories with minimal annotated support samples. While existing FS-PCS methods have shown promise, they primarily focus on unimodal point cloud inputs, overlooking the potential benefits of leveraging multimodal information. In this paper, we address this gap by introducing a multimodal FS-PCS setup, utilizing textual labels and the potentially available 2D image modality. Under this easy-to-achieve setup, we present the MultiModal Few-Shot SegNet (MM-FSS), a model effectively harnessing complementary information from multiple modalities. MM-FSS employs a shared backbone with two heads to extract intermodal and unimodal visual features, and a pretrained text encoder to generate text embeddings. To fully exploit the multimodal information, we propose a Multimodal Correlation Fusion (MCF) module to generate multimodal correlations, and a Multimodal Semantic Fusion (MSF) module to refine the correlations using text-aware semantic guidance. Additionally, we propose a simple yet effective Test-time Adaptive Cross-modal Calibration (TACC) technique to mitigate training bias, further improving generalization. Experimental results on S3DIS and ScanNet datasets demonstrate significant performance improvements achieved by our method. The efficacy of our approach indicates the benefits of leveraging commonly-ignored free modalities for FS-PCS, providing valuable insights for future research. The code is available at github.com/ZhaochongAn/Multimodality-3D-Few-Shot.

Published as a conference paper at ICLR 2025
MULTIMODALITY HELPS FEW-SHOT 3D POINT CLOUD SEMANTIC SEGMENTATION
Zhaochong An', Guolei Sun Yun Liu"; Runjia Li Min Wu', Ming-Ming Cheng',
Ender Konukoglu', and Serge Belongie*
1 Department of Computer Science, University of Copenhagen
2 Computer Vision Laboratory, ETH Zurich
3 College of Computer Science Nankai University
4 Department of Engineering Science, University of Oxford
5 Institute for Infocomm Research, A A
ABSTRACT
Few-shot 3D point cloud segmentation (FS-PCS) aims generalizing models to segment novel categories with minimal annotated support samples. While existing FS-PCS methods have shown promise, they primarily focus unimodal point cloud inputs, overlooking the potential benefits of leveraging multimodal information. In this paper, we address this gap by introducing multimodal FS-PCS setup, utilizing textual labels and the potentially available 2D image modality. Under this easy-to-achieve setup, we present the MultiModal Few-Shot SegNet (MM-FSS), a model effectively harnessing complementary information from multiple modalities. MM-FSS employs shared backbone with two heads to extract intermodal and unimodal visual features, and pretrained text encoder to generate text embeddings. To fully exploit the multimodal information, we propose Multimodal Correlation Fusion (MCF) module to generate multimodal correlations, and a Multimodal Semantic Fusion (MSF) module to refine the correlations using text-aware semantic guidance. Additionally, we propose a simple yet effective Test-time Adaptive Cross-modal Calibration (TACC) technique to mitigate training bias, further improving generalization. Experimental results S3DIS and ScanNet datasets demonstrate significant performance improvements achieved by our method. The efficacy of our approach indicates the benefits of leveraging commonly-ignored free modalities for FS-PCS, providing valuable insights for future research. The code is available this link.
INTRODUCTION
3D point cloud segmentation has wide-ranging applications (Xiao et al., 2024; Ren et al., 2024; Jiang al., 2024) across various fields. Despite numerous successes in fully supervised learning (Nie et al., 2022; Lai et al., 2022; Zhang et al., 2023b), its effectiveness is constrained by the semantic categories predefined in large-scale, expensive, and fully-annotated datasets (Dai et al., 2017; Armeni et al., 2016). To address this challenge, few-shot 3D point cloud semantic segmentation (FS-PCS) has recently attracted increasing attention, enabling models generalize unseen/novel categories with just few annotated samples. Existing FS-PCS methods (Zhao et al., 2021; Xu et al., 2023; Zhu et al., 2023; Mao al., 2022: Wang et al., 2023; Zhang al., 2023a; An et al., 2024) typically adhere to the meta-learning framework (Vinyals et al., 2016; Snell et al., 2017; Ren et al., 2018) to transfer knowledge from annotated support point clouds to query point clouds for segmenting novel classes.
However, these methods predominantly focus unimodal point cloud inputs, overlooking the potential benefits of leveraging multimodal information. Insights from neuroscience (Nanay, 2018; Quiroga et al., 2005; Kuhl & Meltzoff. 1984; Meltzoff & Borton, 1979) suggest that human cognitive learning is inherently multimodal, with different modalities the same concept exhibiting strong correspondence through the activation of synergistic neurons. Particularly, multimodal signals, such as vision and language, have been shown to play crucial roles, surpassing the performance of only
Corresponning authors: Guolei Sun and Yun Liu

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Published as a conference paper at ICLR 2025
Only used in pretraining
Support
Support Mask
Support
Suppoct Mask
2D Images
Used training & inference
Text
Chair
Fow-shot Model
Fow-shot Model (Muliimodallites)
(Uni-modality)
Query
Prediction
Quecy
Prediction
Figure 1: Comparison between traditional unimodal FS-PCS and our proposed multimodal FS-PCS. Previous FS-PCS methods only make use of point clouds as unimodal input. In contrast, our proposed model utilizes multimodal information without additional annotation cost to improve FS-PCS by considering the textual modality of class names (explicit) and learning simulated features of the 2D modality (implicit). During meta-learning and inference, the 2D modality is not needed.
utilizing vision information (Jackendoff, 1987; Smith & Gasser, 2005; Gibson, 1969). In the context of few-shot 3D point cloud semantic segmentation, apart from point cloud modality, additional useful modalities include the corresponding class names and 2D images. Motivated by these important observations, a pertinent question arises: How can we exploit additional modalities in few-shot 3D point cloud semantic segmentation?
In this paper, we explore the use of multimodal information in few-shot 3D learning scenarios. Specifically, propose to incorporate two additional modalities in FS-PCS without additional annotation cost, including the textual modality of category names and the 2D image modality that is usually obtained alongside the capture of 3D point clouds. For the textual modality, it contains condensed semantic information of the object class in the language domain. Since the knowledge of the target class name is typically available during the process of annotating support point clouds, the category name is readily accessible and can be utilized as the textual modality input for free in FS-PCS. For the 2D modality, pairs of 2D images and corresponding 3D point cloud provide dense correspondences between 2D pixels and 3D points, enabling the enhancement of 3D visual features by their 2D counterparts. Notably, we only use the 2D modality during pretraining an implicit manner by utilizing 3D features to simulate 2D features. During meta-learning and inference, no 2D images are needed, ensuring that our model remains independent of images from point clouds. We also demonstrate that training on a dataset without 2D images (e.g.. S3DIS (Armeni et al., 2016)) can be achieved by employing the feature extraction module pretrained on other datasets (e.g., ScanNet (Dai al., 2017)). Thus, the multimodal information used by us is cost-free, shown in Fig. 1.
Under this cost-free multimodal FS-PCS setup, we introduce a novel model, MultiModal Few-Shot SegNet (MM-FSS), to effectively address FS-PCS by harnessing complementary information from different modalities. MM-FSS processes 3D point cloud inputs by a shared 3D backbone with two heads extract intermodal and unimodal (point cloud) features, respectively. The intermodal features are firstly pretrained to be aligned with the corresponding 2D visual features extracted from vision-language models (VLMs) such as LSeg (Li al., 2022) using 2D modality. Then, our model can perform few-shot segmentation using intermodal/uiimddal features and text embedding extracted from the VLM's text encoder on textual modality. This design enables the flexible application of our model even if there is no 2D modality available. Specifically, we develop a Multimodal Correlation Fusion (MCF) module to effectively fuse correlations computed from different information sources. The following Multimodal Semantic Fusion (MSF) module further improves the fused correlations by utilizing semantic guidance from textual modality, i.e., the target classes, to enhance the point-wise multimodal semantic understanding. Additionally, Adaptive moddl Calibration (TACC) technique to mitigate training bias inherent in few-shot models (Cheng al., 2022). This technique adaptively calibrates predictions during test time by measuring an adaptive indicator for each meta sample to achieve better generalization.
propose simple yet effective Test-time
We systematically compare MM-FSS against existing methods (Zhao et al., 2021; He et al., 2023; Ning et al., 2023; An et al., 2024) on S3DIS (Armeni et al., 2016) and ScanNet (Dai et al., 2017) datasets ($4.2), suggesting the significant superiority of MM-FSS across various settings.
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With extensive ablation studies ($4.3), we offer further insights into the efficacy of our designs and showcase the benefits of utilizing free modalities for ES-PCS, shedding light on future research.
Our contributions are three-fold. (i) We study the value of multimodal information (textual and 2D modality) in FS-PCS by proposing a novel cost-free multimodal FS-PCS setup. To the best of our knowledge, this is the first work to explore multimodality in this domain. (ii) We introduce novel model, MM-FSS, to effectively exploit information from different modalities, which includes multimodal correlation fusion, multimodal semantic fusion, and test-time adaptive cross-modal calibration modules. (iii) Extensive experiments are conducted and validate the value of the proposed setup and the efficacy of the proposed method across different few-shot settings. Our work will inspire future research in this field.
2 RELATED WORK
2.1 FEW-SHOT 3D POINT CLOUD SEGMENTATION
While many prior works have shown success in sully-super 3D point cloud segmentation (Lin et al., 2020; Zhou et al., 2021; Ran et al., 2021; Nie et al., 2022; Lai et al., 2022: Park et al., 2022; Zhang et al., 2023b; Wu et al., 2024; Kolodiazhnyí 2024), the high labor cost of annotating point clouds has spurred interest in few-shot methods. The pioneering FS-PCS approach by attMPI (Zhao et al., 2021) introduced label propagation from support to query points in transductive manner. Subsequent research has focused on bridging the semantic gap between prototypes and query points (He et al., 2023; Ning et al., 2023; Zhu et al., 2023; Zheng et al., 2024; Xiong et al., 2024; Li et al., 2024) and enhancing representation learning (Mao et al., 2022; Wang et al., 2023; Zhang et al., 2023a; Huang et al., 2024; Zhu et al., 2024; An et al., 2024). For instance, PAP (He et al., 2023) converts support prototypes to better align with query features for alleviating the intra-class variation, QGE (Ning et al., 2023) refines support prototypes in two steps by firstly adapting the support background prototypes and secondly optimizing support prototypes holistically, and 2CBR (Zhu et al., 2023) aligns the support and query distributions by rectifying the bias in support based co-occurrence features. BFG (Mao et al., 2022) enhances the prototypes with global perception through bidirectional feature globalization. CSSMRA (Wang et al., 2023) uses contrastive sulf-supervisoon to overcome pretraining biases. SCAT (Zhang et al., 2023a) leverages multi-scale query and support features for exploring detailed relationships. Seg-NN (Zhu et al., 2024) designs hand-crafted filters for extracting dense features in order to alleviate domain gaps between training and inference. Notably, recent work, COSeg (An et al., 2024), highlights two issues in the previous FS-PCS experimental setting, i.e., foreground leakage and sparse point distribution, and introduces a reasonable setting with new benchmark to facilitate this field.
al., 2024; Wang al., 2024a; Han al.,
2.2 MULTIMODAL 3D POINT CLOUD SEGMENTATION
As the unimodal 3D point cloud segmentation models (Liu et al., 201 9b;a; Hu et al., 2020; Zhang et al., 2022; Wang et al., 2024b; Zhang et al., 2024; Xu et al., 2024b) show huge progress, increasing attention has been put to explore multimodality for further improvements. The most common modality used to help 3D segmentation is the 2D images. Due to its richer texture and appearance features compared to point clouds, many works have achieved better segmentation performance by learning from the two modalities. The first category, fusion-based methods (Su et al., 2018; Krispel et al., 2020; El Madawi et al., 2019; Meyer et al., 2019; Kundu et al., 2020; Zhuang et al., 2021; Maiti et al., 2023; Liu et al., 2023), fuses the semantic features predictions from 2D images with the corresponding 3D parts to benefit from both modalities. The second category, distillation-based works (Yan et al., 2022; Tang et al.. 2023), applies knowledge distillation (Hinton al., 2015) train the student branch on 3D unimodality to learn from the fused multimodal features. Besides the 2D modality, the language modality is also utilized for 3D visual perception, enabling models (Rozenberszki et al., 2022; Jatavallabhula et al., 2023; Peng et al., 2023; Ding et al., 2023; Ha & Song, 2023; Chen et al., 2023; Mei et al., 2024; Xu et al., 2024a) to achieve open-vocabulary 3D segmentation by learning 3D features guided by CLIP (Radford et al., 2021) or other 2D vision-language models (Li et al., 2022: Ghiasi et al., 2022). However, these multimodal methods are designed either for fully supervised or veccbuocayl segmentation. When it comes to FS-PCS, prior methods only make use of unimodal point clouds, potentially due to challenges in integrating additional modalities (further discussion in Appendix C). In contrast, we propose MM-FSS to leverage cost-free multimodal information
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Text $ Encoder
"Bookcase" 1 "Others"
Text Embedding T
F
BXW
3SK
Cx
Backhons
Head
F
Query Xa
Decoder
Adapter Indicator
Fg
Conrelation Generation
UF Head
G
Support X,
F
Currelation
Generation
TACC
Addition
Multiplication
MCF
Prediction P.
Support Mask
Only testing
Figure 2: Overall architecture of the proposed MM-FSS. Given support and query point clouds, we first generate intermodal features F from the IF head and unimodal features F from the UF head. These features are then forwarded the MCF module to generate initial multimodal correlations Co- Moreover, exploiting the alignment between intermodal features F; and text embeddings T, use their affinity Gg as the informative textual semantic guidance to refine the multimodal correlations in the MSF modules. Finally, we propose the TACC, parameter- seremeter-free module that adaptively calibrates predictions during test time to effectively mitigate the base bias issue. For clarity, we present the model under the 1-way 1-shot setting.
for improving FS-PCS by fusing textual class names and simulated 2D features. To the best of our knowledge, this is the first study to explore multimodal FS-PCS.
3 METHODOLOGY
3.1 PROBLEM SETUP
ES-PCS. This task can be formulated as the popular episodic paradigm (Vinyals et al., 2016), following prior works (Zhao al., 2021; An et al., 2024). Each episode corresponds to N-way
K-shot segmentation task, containing a support set S = { { k
and a query set J {
Q = [X2, Y= We use X %/4 and Y to denote point cloud and its corresponding point-level label, respectively. The support set S includes the samples for N target classes, with each class described by a K-shot group (X..A, Y".k JA=1. containing the exclusive labels for that semantic class. The goal FS-PCS to segment the query samples {X"] into target classes and 'background' by leveraging the knowledge of the N novel classes from support samples in S.
-===1
In=1
Multimodal FS-PCS. Different from the existing setup, we propose a multimodal FS-PCS setup where additional modalities exist: the textual modality and the 2D image modality. Formally, for the episode introduced above, we additionally have class names for S, e.g., 'chair", 'table', 'wall", etc. For the 2D image modality, have 2D RGB images accompanying 3D point clouds during pretraining, but 2D images are not required during meta-learning and inference. In the following discussions, unless stated otherwise, we focus the 1-way 1-shot setting for clarity. The support and query sets are represented as S = (X., Y,} and Q = {Xq. Y respectively.
3.2 METHOD OVERVIEW
Our Idea. Since existing FS-PCS datasets containing three modalities (3D point clouds, class names, and 2D RGB images) are generally on a small scale, difficult to directly train models to learn meaningful representations of these modalities. Inspired by the rapid advancements in vision-language models (VLMs), we propose to leverage existing VLMs such as LSeg (Li et al., 2022) and OpenSeg (Ghiasi et al., 2022) exploit additional modalities for FS-PCS.
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Specifically, we adopt the pretrained text encoder of LSeg (Li et al., 2022) to extract text embeddings for class names. These powerful text embeddings provide additional guidance for learning FS-PCS, which is supplementary to the visual guidance extracted from the support set. To utilize the potentially available 2D modality, we propose to use the visual encoder of I.Seg to generate 2D visual features, which exhibit excellent generalizability since the LSeeg model is pretrained on large-scale 21) datasets. Considering that the 2D modality is not always available for all FS-PCS datasets (Armeni et al., 2016), we employ the extracted 2D features to supervise the learning of 3D point cloud features during pretraining, effectively using 3D features to simulate 2D features. The learned features are referred to as intermodal features since they are aware of both 3D and 2D information. This design offers two key advantages: i) Our model uses 2D modality in an implicit manner and does not require it as input during meta-learning and inference; ii) Since the learned intermodal features are aligned with L.Seg's s 2D visual features, they are therefore aligned with text embeddings. This alignment provides important guidance for subsequent stages, which will be explained in detail later.
Method Overview. The overall architecture of the proposed MM-FSS is depicted in Fig. 2. Given support and query point clouds, we first generate two sets of high-level features; intermodal features from the Intermodal Feature (IF) head and unimodal features (point cloud modality) from the Unimodal Feature (UF) head. Both intermodal and unimodal features are then forwarded to the Multimodal Correlation Fusion (MCF) module to produce multimodal correlations between support and query point clouds. Beyond mining visual connections, we use the L.Seg text encoder (Li et al., 2022) to generate text embeddings for class names. We then exploit useful semantic guidance from the textual modality to refine the multimodal correlations in the Multimodal Semantic Fusion (MSF) module. During inference, to mitigate training bias (Cheng et al., 2022), we further propose Test-time Adaptive Cross-modal Calibration (TACC) to generate better predictions for novel classes.
Existing FS-PCS approaches (An et al., 2024; Zhao et al., 2021) typically have two training steps: a pretraining step for obtaining an effective feature extractor, and meta-learning step towards few-shot segmentation tasks. Our method follows this two-step training paradigm. First, we pretrain the backbone and IF head using 3D point clouds and 2D images. Second, we conduct meta-learning to train the model end-to-end while freezing the backbone and IF head. Further training details are provided in Appendix B. In the following, we elaborate on feature extractors (3D backbone, IF and UF heads, text encoder) as well as MCF, MSF, and TACC modules.
3.3 FEATURE EXTRACTORS
Visual Features. Our method processes point cloud inputs through a joint backhone and two distinct heads of IF and UF, as depicted in Fig. 2. The IF head extracts intermodal features that are aligned with 2D visual features by exploiting the 2D modality, while the UF head focuses solely on 3D point cloud modality. Given the support/query point cloud X./qq we utilize a shared backbone & to obtain general support features F, = + (X.) E Ne and query features F, + (Xx,)) ND where D is the channel dimension, .Ns and NQ are the respective point counts in X, and Xq. Subsequently, these features processed by the IF head (HIF) and the UF head (HUF) to generate intermodal and unimodal features for both support and query inputs, given by:
F HiF (Fs)
E R NsxD. F Hur (F,( ER NexD
F- HIF (Fa) E I NOXD. De
F. Hur E NoXD.
Dr denotes the channel dimension of intermodal features, which is aligned with the emhedding space of LSeg (Li et al., 2022). The resulting Fl and F. represent the intermodal and unimodal features for the support point cloud, respectively. Fi and F serve the same purpose for the query point cloud.
As mentioned above, the intermodal features, F! and Fi are specifically trained in the first step to align with 2D visual features from the visual encoder of VL.Ms (Li et al., 2022; Ghiasi et al., 2022). Following Peng et al. (2023), employ cosine similarity loss to minimize the distance between 3D point intermodal features and corresponding 2D pixel features (see Appendix B). Once this step finishes, we fix the backbone and IF head to keep the intermodal features for providing critical guidance for FS-PCS. Then, we start meta-leerning end-to-end to fully exploit the intermodal and unimodal features along with text embeddings in conducting FS-PCS.
Text Embeddings. We compute embeddings for the 'background' and target classes using the LSeg (Li et al., 2022) text encoder, denoted as T (to, ,tN] E IR xD, where to represents
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the text embedding of the 'background" class, and the others represent the text embeddings of the target classes. Here, Ne = + denotes the number of all classes in the N N-way setting.
3.4
MODAL INFORMATION FUSION
We have intermodal and unimodal features for support/query point clouds and text embeddings for target classes. Our goal is predict the segmentation mask of the query point cloud using all available information from different modalities. As in Min et al. (2021). Hong et al. (2022), and An al. (2024), the core of few-shot segmentation is build informative correlations between query and support point clouds. To this end, we propose two novel modules for cross-modal knowledge fusion: MCF and MSF. The former integrates intermodal and unimodal features to generate multimodal correlations, The latter exploits the textual semantic guidance to further refine the correlations. The details of these two modules are explained below.
Multimodal Correlation Fusion. Contrary to traditional FS-PCS models that rely solely on unimodal inputs (Zhao et al., 2021: He al., 2023; An al., 2024), our method calculates multimodal correlations by integrating the two correlations from intermodal and unimodal features. Initially, foreground and background prototypes are generated from the annotated support points for both Fl and F. using farthest point sampling and points-to-samples clustering, as described in An et al. (2024) and Zhao et al. (2021). This prototype generation, denoted as Frooto results in:
P Pi by proto F Y, Lg),
P Pi RNNxD. P Phg ERNDD
P P F proto F Y., L,),
where L, represents the 3D coordinates of support points, and Np is the number of prototypes. These prototypes are then concatenated to obtain: P Piotto Pig + Pig (RNoxx)))xx Di and
bg E (NNcxN))xD Subsequently, we calculate the correlations between the query points and these prototypes:
Pu proto P @ P
Fl PIT proto
Fsl F
Pu proto
C
C"
F
P proto
PuT
proto
yielding C' (Nxx and C" NNNx) which represent the point-category relationships between query points and support prototypes within the intermodal and unimodal feature spaces, respectively. This process is termed correlation generation Fig. Next, our MCF module transforms these correlations using two linear layers and then combines them to obtain the aggregated multimodal correlation Co. as follows:
Co Fin((C) + Finn (C"), Co CN
where Fim represents the linear layer projecting the Np channels in CV/u D. The MCF module effectively aggregates point-to-prototypp relationships informed by different modalities, enhancing the correlation Co with a comprehensive multimodal understanding of the connections between query points and support classes. This enriched understanding facilitates knowledge transfer from support to query point cloud, improving query segmentation.
Multimodal Semantic Fusion. While the MCF module effectively merges correlations from different information sources, the semantic information of text embeddings remains untouched, which could provide valuable semantic guidance to improve the correlations. Therefore, propose the MSF module, as illustrated in Fig. 2. MSF integrates semantic information from text embeddings refine the correlation output of MCF, Additionally, since the relative importance of visual and textual modalities varies across different points and classes (Yin et al., 2021; Cheng et al., 2021), MSF dynamically assigns different weights the textual semantic guidance for each query point and target class, accounting for the varying importance between modalities.
Given text embeddings T and intermodal features F; of the query point cloud, since the intermodal features Fi are pretrained to simulate the 2D visual features from VLMs (Li et al., 2022), F is well- aligned with text embeddings T, and the affinities between them provide informative guidance how well the query points relate to the target classes. Therefore, we first compute the similarity between the query intermodal features and text embeddings to generate semantic guidance G2 E No

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for segmenting the target classes, given by: Gq = F T
(S)
Then, our MSF module consists of K MSF blocks, with the correlation input to the current block denoted as C& (A E 10, K - -. In each block, point-category weights to consider varying importance between visual and textual modalities are dynamically computed as follows:
Wg Fmlp (G)p)ad(Gg) @ Ck), Wa ER NNoxNexx
(6)
where Fexpand expands and repeats on the last dimension of Gq. transforming it to R xNoxNexD
and Filp represents a multilayer perceptron (MLP). Next, the semantic guidance Gq. weighted by W q, is aggregated into the correlation input Cs. A linear attention layer (Katharopoulos et al., 2020) and a MLP layer are used further refine the correlations, given by:
CK = G Wa Ck. Ck+1 = (Fmlp(Fatention) (Ck));
(7)
(8)
where @ denotes the Hadamard product and Fattention represents the linear attention layer. Note that the residual connections after Fatteution and Fulp are omitted here for simplicity.
This MSF module fully leverages useful semantic information from textual modality to enhance the correlations between query and support point cloud, helping to determine the best class for query points. Note that it computes the relative importance between visual and textual modalities for all pairs of points and classes, improving the effective integration of textual modality.
Loss Function. After the MSF module with K blocks, the refined correlation Ck is transformed into the prediction P, E R Ne by a decoder comprising a KPConv (Thomas et al., 2019) layer and MLP layer. The whole model is optimized end-to-end by computing cross-entropy loss between the prediction P, and the ground-truth label Yg for the query point cloud.
3.5 TEST-TIME ADAPTIVE CROSS-MODAL CALIBRATION
Few-shot models inevitably introduce bias towards base classes due to full supervision on these classes during training (Lang et al., 2022; Cheng et al., 2022; Wang et al., 2023; An et al., 2024). When the sho- model is evaluated on novel classes, this base bias leads to false activations for buse classes existing in test scenes, impairing generalization.
To mitigate it, we propose a simple yet effective TACC module, exclusively employed during test time. The TACC module exploits the semantic guidance G. to calibrate the prediction Pa Notably, G2 is derived from the query intermodal features and text embeddings, which are not updated throughout the meta-leaming process. Thus, G2 includes much less bias towards the training categories. Furthermore, Gg contains rich semantic information for the query point cloud, and G2 [ represents the probability of assigning ith point to target classes. Building upon this, propose an adaptive combination of G. and P, through adaptive indicator 7, enabling appropriate utilization of the semantics in G in the final prediction:
(9) Here, 7 is an adaptive indicator reflecting the quality of semantics contained in G If is high, the quality of G2 is good, and more information in G2 is used. If 7 is low, the quality of Gg is unsatisfactory, and less information in G2 employed.
P -
Adaptive Indicator. The proposed adaptive indicator y is dynamically calculated for each few-shot episode by evaluating G, for support samples. Using the support intermodal features Fi and the text embeddings T, we compute Gg, which is then used to generate predicted labels P.. With the available support labels Y, each episode, the quality of G, is quantified by comparing the predicted labels P. to Y, using the Intersection- cereooonn-vv---ueee Union (IoU) score. Since G2 and G, are computed in the same way using intermodal features and text embeddings, this score serves as 7, indicating the reliability of the semantic guidance in Gq:
Σ, 1P P = arg max G3 (i. G. = F TT,
== Σ, ((P.00-IY..00-)
(10)
7

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Methods
1-way I-shot
1-way 5-shot
2-way 1-shot
2-way 5-shot
S
g*
Mean
S"
g²
Mean
.
S²
Mcan
5
S
Mcan
ARMPTI
36.32
38.36
37.34
46.71
42.70
44.71
31.09
29.62
30.36
39.53
32.62
36.08
QGE ot
39.09
50.59
33.45
30.95
32.20
38.33
QGPA (12))
35.50
35.83
35.67
38.07
39.70
38.89
25.52
26.26
25.89
30.22
32.41
31.32
COSeg at 2000
46.31
48.10
47.21
51.40
48.68
50.04
37.44
36.45
36.95
42.27
38.45
40.36
COSeg' (Anumai 2000
48.37
47.77
50.93
49.88
37.15
38.07
42.73
41.49
MM-FSS (ours)
49.84
54.33
52.09
51.95
56.46
54,216338
41.98
46.61
44.30k.62)
46,02
54.29
50.16.87)
Table I: Quantitative comparison with previous methods in mIoU (%) on the S3DIS dataset. There are four feo-shot settings: 1 1/2-way 1/5-shot. 5 /5 refers to using the split 0/1 1 for evaluation, and 'Mean' represents the average mloU on both splits. The best results are highlighted in bold.
Methods
1-way I-shot
1-way S-shot
way I-shot
2-way S-shot
S"
S
S*
Mcan
Mean
S
S'
Mcan
ARMPTI 2133
34.03
30.97
32.50
39.09
37.15
38.12
25.99
23.8%
24.94
30.41
27.35
88
QGF, 2122
37.38
33.02
35.20
45.08
41.89
43.49
26.85
25.17
26.01
28.35
31.49
29.92
QGPA
34.57
33.37
33.97
41.22
38.65
39.94
21.86
21.47
21.67
30.67
27.69
29. 18
COSeg COSeg.tust dl 2024
41.73
41.82
41.78
48.31
44.11
46.21
28.72
28.83
28.78
35.97
33.39
34.68
COSeg dd. 2024)
41.95
42.07
42.01
48.54
44.68
46.61
29:54
28.51
29.03
36.87
34.15
35.51
MM-FSS (aurs)
46,08
43.37
44.73-25
54.66
45,48
50.07:-35)
43.99
34.43
39.21
48,86
39.32
44.19:-k4)
Table 2: Quantitative comparison with previous methods in mIoU (%) on the ScanNet dataset.
where 1 (x) is the indicator function that equals one x true and zero otherwise, and Pa [ denotes the predicted class index for the h support point. This adaptive indicator ensures that the TACC module effectively mitigates training bias by dynamically calibrating predictions during test time, leading to improved few-shot generalization.
4
EXPERIMENTS
4.1 EXPERIMENTAL SETUP
Datasets. We evaluate our method on two popular FS-PCS datasets: S3DIS (Armeni et al., 2016) and ScanNet (Dai et al., 2017). ScanNet provides 2D RGB images of 3D scenes while S3DIS lacks. The two datasets allow us to demonstrate our model's effectiveness in exploiting multimodal data and its capability to excel in FS-PCS even without 2D images on a given dataset. Our model leverages the 2D modality implicitly, enabling the flexible use of pretrained weights from ScanNet to initiate meta-lcarning on S3DIS. Following Zhao et al. (2021), we divide the large-scale scenes into Im x Im blocks. We adhere to the standard data processing protocol from An et al. (2024), voxelizing raw input points within each block using a 0.02m grid size and uniformly sampling to maintain a maximum of 20,480 points per block.
For our architecture, we employ the first two blocks of Stratified Transformer (Lai et al., 2022) as our backbone, with the IF and UF heads following the design of its third stage. By default, we utilize 2 MSF blocks for S3DIS and 4 MSF blocks for ScanNet. The initial pretraining phase spans 100 epochs, while the subsequent meta-learning phase includes 40,000 episodes. following An et al. (2024). For optimization, we use the Adam W optimizer, setting weight decay of 0.01 and a learning rate of 0.006 during pretraining. The learning rate reduced to 0.0001 during the meta-learning phase. As in An et al. (2024), the evaluation sets consist of 1,000 episodes per class in the 1-way setting and 100 episodes per class combination in the 2-way setting.
Implementation Details.
4.2 COMPARISON WITH STATE-OF-THE-ART METHODS
We compare MM-FSS with previous models on the S3DIS (Armeni et al., 2016) and ScanNet (Dai et al., 2017) datasets, detailed in Tab. I and Tab. 2, respectively. We also evaluate variant of the previously leading method COSeg (An et al., 2024), denoted as COSeg1, retrained using the same 2D-alinned pretrained backbone weights our model. Despite leveraging the 2D-aligned backbone weights, COSegi does not significantly improve over COSeg, highlighting the critical role of well-designed fusion modules in achieving significant advancements.
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MCF MSF 1-shot
5-shot
IF head UF head 1-so] 5-shot
1-shot
5-shot
3D Image Text
1-shot
5-shor
40.69
45.51
35.10
37.32
41.45
46.38
40.69
45.51
3
43.33
45.97
40.69
45.51
42.21
46.46
42.83
4
42.83
48.04
4
41.45
46.38
48.04
44.73
5
44.69
48.36
4
v
44.73
50.07
(a)
(b)
(c)
(d)
1:0
1:0.5
1:1
0:1
7:1
Methods
1-shot 5-shot
Methods
FLOPPs
Params
1-shot
10 43.24
43.76
42.83
44.73
MSF-linear
40.80 45.79
COSeg 2024)
27.76G 7.75M
5-shol
37.32 48.12 48.85
48.04
50.07
Default
42.83 48.04
MM-FSS (ours) 29.21G 10.25M
(c)
Table Ablation study. (a) Effect of fusion modules. (b) Effect of interactions between two feature heads. (c) Impact of the number of MSF layers. (d) Performance gains from each modality. (e) Impact different coefficients in TACC. (f) Weighting methods in MSF. (g) Complexity analysis.
Support
Suppori Mask
Quary
Ground Truth
COSeg
MM-FSS (Oum)
Figure 3: Qualitative comparison between COSeg and our proposed MM-FSS the 1-way 1 1-shot setting the S3DIS dataset. The target classes in the first and second rows sofa and window, respectively. Colored circles highlight regions where predictions from COSeg and MM-FSS differ significantly facilitate visual comparison.
Support
Suppori Musk
Query
Ground Truth
IF Head
UF Head
Detiult
Figure 4: Qualitative comparison of predictions from each head and our final prediction using TACC (Default) in the 1-way 1-shot setting the S3DIS dataset. The target classes in the first and second rows are door and board, respectively.
In contrast, MM-FSS consistently outperforms the former state-of-the-arr across all settings, demon- strating superior cross-modal knowledge integration to enhance novel class segmentation. Specifically, on the ScanNet dataset, MM-FSS records average mloU increases of +3.41 % in the 1-way and +9.92% % in the 2-way settings over COSeg. Similarly, it achieves +4.53 % and +8.58% % improvements on the S3DIS dataset in the 1/2-way settings. respectively. Visual comparisons in Fig. further illustrate MM-FSS's advanced few-shot segmentation capabilities.
Overall, our model secures average mloU improvements of +3.97% and +9.25% across the 1/2-way settings both datasets. The greater gains in 2-way settings can be attributed to the higher demands on a model's s ability to learn novel knowledge under these 2-way conditions. With limited input from support point clouds, models typically struggle to fully learn novel classes for accurate segmentation. However, MM-FSS excels in integrating knowledge from multiple modalities, fostering a deeper comprehension of novel classes. This performance gap underscores utilize multimodal knowledge for FS-PCS and the importance of considering commonly-ignore, multimodal information enhance few-shot generalization for future research.
model's superior ability to

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4.3 ABLATION STUDY
In this section, unless stated otherwise, we report the mloU results for both 1-way 1/5-shot settings on ScanNet as the mean of all splits.
Impact of the Fusion Modules. Tab. 3a evaluates the effectiveness of our fusion modules. Results show that employing either MCF or MSF individually enhances mloU, demonstrating their ability to effectively utilize multimodal knowledge. Moreover, combining both MCF and MSF together further improves performance, confirming that their fusion strategies are both essential and complementary for enhancing few-shot learning.
Ablation on the Feature Heads. Tab. 3b examines the interaction effects between the IF and UF heads. Results in the third row indicate that our cross-modal fusion modules effectively combine the capabilities of both heads to learn enhanced multimodal knowledge. Additionally, the TACC module leverages the head's semantic guidance to mitigate the UF head's training bias, leading to further mloU gains, shown in Fig. 4.
Impact of the Number of MSF Blocks. Tab. 3c showeases the performance of different numbers of MSF blocks, evaluated in the absence of the TACC module. The results demonstrate that increasing the number of MSF blocks enhances few-shot performance. By default, We use 4 MSF blocks for the ScanNet dataset.
Performance Gains from Each Modality. In Tab. 3d, we provide results for different modality
combinations evaluate their respective contributions. Tab. 3d shows that adding the image modality improves the 3D-only baseline, and further incorporating the textual modality leads to better results. This demonstrates our model's effectiveness in fully leveraging the complementary strengths of different modalities for a comprehensive understanding of novel classes
Influence of the Coefficients in TACC. Tab. 3e assesses how varying coefficients affect prediction calibration in TACC, denoting the coefficients for Gg and P. in Eq. (9) as a:b. Using only G2 (1:0) yields the lowest performance due the IF head's limitations utilizing support samples for learning novel classes. Fixed coefficients (1:1 and 1:0.5) are unable to dynamically adjust calibration and only slightly improve over the baseline (0:1). Conversely, the adaptive indicator y notably enhances mloU, proving its superiority in dynamically calibrating predictions for each meta sample.
Weighting Methods in the MSF Module. In MSF, considering the varying relative importance between textual and visual modalities, we dynamically assign weights Wg in Eq. (7) to the textual semantic guidance across points and classes. Tab. 3f compares the results of using a simple linear combination (denoted as MSF-linear) in Eq. (7) against our detailed weighting method (denoted as Default) within the MSF layers, demonstrating the effectiveness of our proposed weighting approach in appropriately integrating the textual semantic guidance.
Complexity Analysis. Tab. 3g presents a comparison of the FLOPs and parameter count between model and the previous state-of-the-at method, COSeg (An et al., 2024). The results show that, when achieving significant performance gains, our model incurs only a small increase in computational cost and parameters, demonstrating superior balance between efficiency and performance.
5 CONCLUSION
In this paper, we explore the possibility of exploiting additional modalities for improving FS-PCS. We first propose a novel cost-free multimodal FS-PCS setup by integrating the textual modality of category names and the 2D image modality. Under our cost-free setup, we present MM-FSS, the first multimodal FS-PCS model designed to utilize the textual modality explicitly and 2D modality implicitly to maximize its adaptability across datasets. MM-FSS combines MCF and MSF to effectively aggregate multimodal knowledge, enriching the comprehension of novel concepts from both correlation and semantic perspectives, which are mutually important and complementary. Furthermore, to mitigate the inherent training bias issue in FSSPCS,, we introduce the TACC technique, which dynamically calibrates predictions during inference by leveraging semantic guidance from existing
textual modality for each meta sample. MM-FSS achieves significant improvements methods across all settings. Overall, our research provides valuable insights into the importance of commonly-ignoree free modalities in FS-PCS and suggests promising directions for future studies.
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ACKNOWLEDGMENTS
This research supported in part by the Agency for Science, Technology, and Research (A (A"STAR) under its MTC Programmatic Fund (No. M2317700211,, and the Pioneer Centre for AI, DNRF grant number Pl.
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A ADDITIONAL EXPERIMENTS
1-way 1-shot
1-way 5-shot
2-way 1-shot
2-way 5-shot
Methods
5
mean
5
Mean
5
Mcan
5
Mean
AIMPPI at 2021)
34.03
30.97
32.50
39.09
37.15
38.12
25.99
23.88
24.94
30.41
27.35
28.88
QGE (Nirg al. 2013)
37.38
33.02
35.20
45.08
41.89
43.49
26.85
25.17
26.01
28.35
31.49
29.92
QGPA el 2023)
34.57
33.37
33.97
41 22
38.65
39.94
21.86
21.47
21.67
30.67
27.69
29.18
COSeg (AB at. 2029)
41.73
41.82
41.78
48.31
44.11
46.21
28.72
28.83
28.78
35.97
33.39
34,68
COSeg
41.95
42.07
42.01
48.54
44.68
46.61
29.54
28.51
29.03
36.87
34.15
35.51
MM-FSS (LScg)
46,08
43.37
44.73
54.66
45.48
50.07
43,99
34.43
39.21
48.86
39.32
44.09
MM-FSS (OpenSeg)
43.74
43.19
43,47
50.60
47.09
48.85
37.80
35.40
36.60
44.31
38.48
41.40
Table 4: Quantitative comparison with previous methods in terms of mloU (%) on the ScanNet dataset. The last two rows represent the FS-PCS performance of our model using different 2D VLMs (LSeg (Li et al., 2022) and OpenSeg (Ghiasi et al., 2022)) in pretraining.
Aggregation
50
51
mean
Weights
1-shot
5-shot
mean
55,46
44.55
50.01
0.5
41.00
45.98
max
50.07
54.00
43.48
48.74
1:1
41.45
46.38
Table 5: Different choices to aggregate adap- tive indicator values in the 5-shot setting.
Table 6: Different weights for the MCF mod- ule.
0.0
0.6
Vilae
Support
Support
Query
(G.)
W.)
Welght(W.))
Figure 5: Visualization on the effects of weight Wa q between textual and visual modalities in Eq. (7). The last column displays the heatmap of W with the color bar referenced at the top. Higher values indicate larger weights assigned to textual guidance Gg. Each row represents the 1-way 1-shot setting on the S3DIS dataset targeting bookcase and table, respectively, arranged from top to bottom.
Ablation study on using different vision-languagg models (VLMs) in pretraining. In the initial training phase of model, we pretrain the backhone and IF head using both 3D point clouds and 2D images. The 2D modality is implicitly incorporated by learning from 2D) features extracted by existing VLMs. In $4, demonstrate the superior performance of our model pretrained with the well-known VLM LSeg (Li et al., 2022). Here, we further investigate the effects of using another VLM, OpenSeg (Ghiasi et al., 2022), for pretraining. The results in the last row of Tab. 4 show that our model pretrained with OpenSeg still outperforms prior methods by significant margin across all few-shot settings. Additionally, the overall performance of MM-FSS (OpenSeg) is comparable to that of MM-FSS (I.Seg), and in some few-shot settings, such as the 1-way 5-shot setting of split 1, can perform better. These results underscore the superior robustness and generalizability of our method in learning and harnessing the 2D modality across diverse pretraining sources to effectively address FS-PCS.
Ablation study on the computation of adaptive indicator in the 5-shot setting. In this ablation study, we compare different aggregation methods of computing the adaptive indicator y in the 5-shot value of y following Eq. (10).
context. For each support point cloud sample, can compute Thus, in the 5-shot setting, we obtain 5 values of yy from each shot, which can be aggregated into single value using mean, max, or min operations. We evaluate the performance effects of using
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these aggregation operations under the 1-way 5-shot settings in Tab. 5, including mloU results on splits O and of ScanNet. Both mean and max aggregation yield comparable performance, while the min aggregation results in approximately a 1.3% decrease in performance compared to the mean and max operations. This indicates that the min operation is less effective in reliably capturing the overall semantic quality of the cross-modality guidance Gq. By default, we employ max aggregation for the 5-shot setting.
A Ablation study on employing different weights for the MCF module. The MCF module is designed to generate multimodal correlations by leveraging cross-modal knowledge. It fuses two types of correlations derived from intermodal and unimodal features for new multimodal correlations. In Tab. 6, we present the performance in the 1-way 1/5-shot settings of using different weights for fusing these correlations as per Eq. (4). The notation a : b in Tab. 6 indicates the respective weights applied to Fiin(C) and Flin (C") before their summation. The results show minimal performance variance between the ratios 1 and 0.5 1, demonstrating the robustness of this module in learning to capture useful cross-modal information for enhanced multimodal correlations.
Visualization on the Effects of Weight W.. We analyze the effects of the weight Wa between textual and visual modalities as specified in Eq. (7). When target classes differ substantially in shape or appearance between support and query samples, textual guidance becomes crucial. In such cases, visual correlations alone struggle to establish meaningful connections, and the model relies more on textual guidance for better segmentation. In Fig. 5, the first row shows a bookcase target class with notable visual differences between support and query. Here, visual correlations alone (6th column) are insufficient, and Wa assigns higher weights (8th column) to regions highlighted by textual guidance (5th column), leading to improved predictions (7th column). In contrast, the second row shows lable target class visually similar in both support and query. Here, Wa q is more evenly distributed across the query points, balancing contributions from both visual and textual sources.
B ADDITIONAL IMPLEMENTATION DETAILS
Training Strategy. We provide more details on our training strategy. Our proposed model is designed as unified architecture with two heads sharing the same backbone network. The Intermodal Feature (IF) head generates intermodal features, while the Unimodal Feature (UF) head focuses solely on features from the point cloud modality. Effective training for extracting informative intermodal and unimodal features is crucial for achieving optimal performance. Simultaneously training both heads might complicate and destabilize the optimization process due to significant heterogeneity across different modalities (Morency & Baltrušaitis, 2017; Lu al., 2023) and distinct supervision objectives. Furthermore, existing cross-modal models (Peng et al., 2023) are typically trained under standard paradigms, and transferring such mrodstty alignment learning for the IF head into the episodic training paradigm can impact performance. Hence, we adopt a two-step training strategy to mitigate potential performance issues.
Pretraining Details. In the first step, we concentrate on training the IF head to learn robust 3D features aligned with 2D modality, providing solid foundation for subsequent episodic training. Specifically, given the 3D coordinates of a point E R3 in a point cloud and an RGB image I with resolution of H x W for the scene, we align the 3D point P to its corresponding 2D pixel u (u, v) on the image plane through the projection ũ = Mint Mext P. where Mint is the camera-to-pixes intrinsic matrix, Maxt is the world-to-cament extrinsic matrix, and ū and p are the homogeneous coordinates of u and p. respectively.
The 2D features F F2D "xx'x D, aligned with text modality can be extracted using the pretrained image encoder in LSeeg (Li et al., 2022) or other VLMMs (Ghiasi et al., 2022), and the 3D features FAD E M. of the point cloud with M points are derived from the IF head. Then, for matched 3D points and 2D pixels from the 2D-3D correspondences, we optimize the backbone and IF head using a cosine similarity loss to ensure close alignment between the 3D point features from F3D and their paired 2D pixel features in FED. following Peng et al. (2023).
Once the IF head and backbone are trained, they are frozen during the subsequent episodic training phase to maintain the integrity of the learned intermodal features. Therefore, we ensure that the expressive intermodal features from IF head are preserved and ready for cross modalityy integration within proposed fusion modules during episodic training.
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For datasets like ScanNet (Dai et al., 2017), which provide 2D images and camera matrices, direct feature alignment is feasible. For datasets without 2D images, such as S3DIS (Armeni et al., 2016), can directly use the pretrained IF head and backbone from ScanNet. The pretraining step is to align with the VLMs embedding space without using any semantic labels, making the pretrained weights class-agnostic, generic, and transferable. This allows us to directly employ pretrained weights from 2D-3D datasets for starting meta-learning on 3D-only datasets.
Model Details. Following An al. (2024), the Stratified Transformer (Lai et al., 2022) serves as our backbone on both S3DIS and ScanNet datasets, using the first two blocks from the Stratified Transformer architecture designed for S3DIS. The IF and UF heads are the same as the third block of the same architecture. Features from the backbone and the two heads are at 1/4 and 1/16 of the original point cloud resolution, respectively. For extracting intermodal or unimodal features, we perform interpolation (Qi et al., 2017) to upsample the 1/16 features from the IF or UF head 4x and concatenate them to the 1/4 backbone features. Then, MLP applied to the concatenated features to obtain the final intermodal or unimodal features. The channel dimension of unimodal features is 192, and the dimension of intermodal features is aligned with the pretrained VLMs used in the first pretraining step. For LSeg (Li et al., 2022), the dimension is 512, while for OpenSeg (Ghiasi et al., 2022), it is 768, Following An et al. (2024), input features from both datasets include XYZ coordinates and RGB colors. We extract 100 prototypes (Np 100) per class; for k-shot settings with k: > 1, we sample Np/k prototypes from each shot and concatenate them to obtain Np prototypes. Training and inference are conducted on four RIX 3090 GPUs. Our meta-learning and inference adopt the episodic paradigm (Vinyals et al., 2016). The episode construction follows prior works (Zhao et al., 2021; An et al., 2024) where support sets are selected by randomly sampling a target class and choosing point clouds containing that class.
C ADDITIONAL DISCUSSIONS
Challenges of Utilizing Multimodality in Few-shot Segmentation. In fully supervised or open- vocabulary segmentation (Liu et al., 2023; Peng et al., 2023), the input is a single point cloud and the output is the predicted semantic labels for that input. The leverage of multimodality in these tasks focuses enhancing feature representations of the individual input point cloud, which is relatively straightforward to design and implement. In contrast, few-shot segmentation involves multiple point cloud inputs (support and query), where the goal is to segment novel classes in the query based on knowledge derived from the support set. The essence of few-shot segmentation lies in mining meaningful connections between query and support point clouds (An et al., 2024). Therefore, when incorporating multimodality in few-shot cases, how to effectively leverage multimodal features to establish informative correlations and facilitate the knowledge transfer from support to query poses unique challenges. To this end, our proposed MCF and MSF modules effectively exploit visual and textual modalities to construct comprehensive multimodal connections, and TACC uses cross- moddll information to calibrate predictions, achieving more robust support-to-query knowledge transfer.
Insights of the MSF module for enhanced correlations. The MSF module enhances cross-modal visual correlations by incorporating textual semantic guidance, that is supplementary the visual guidance derived from the support set. The detailed process is as follows:
1. MSF computes the affinities between intermodal query features and text embeddings, pro- ducing the textual semantic guidance Gg (Eq, (5)). It quantifies how well each query point relates to the target classes in the text embedding space, adding semantic context beyond visual correlations.
2. Recognizing that the relative importance of visual and textual modalities varies across points and classes, MSF introduces point-category weights W These weights are determined based on information from both visual and textual modalities as in Eq. (6), dynamically weighting their contributions.
3. The weighted textual semantic guidance, Gg C Wa. is then aggregated with the visual correlations (Eq. (7)).
By leveraging supplementary textual guidance and dynamically weighting its contributions for each point and class, the MSF module results in enhanced multimodal correlations, effectively combining
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the complementary strengths of textual and visual information to better determine the best class for query points. The visualizations in Fig. further present the detailed effects of MSF.
Insights the integration of 2D modality. The 3D point cloud and 2D image modalities exhibit distinct yet complementary characteristics. 3D point clouds provide rich spatial geometry. However, they are inherently unstructured, lacking natural topology, and their pparsity-poing distributed irregularly and concentrated on surfaces-limin the representation of fine-grained details (Lai et al., 2022). 2D images, in contrast, offer dense, structured representations encoding texture, color, and details on a pixel grid. However, they lack direct geometric cues about depth or the spatial structure of the scene (Wu et al., 2023). Despite their differences, the two modalities share natural correspondence: point in the 3D point cloud typically aligns with a pixel in a 2D image captured from the same perspective. This alignment allows combining the two modalities, laying the foundation for leveraging the strengths of the 2D modality to enhance 3D few-shot segmentation.
Our approach leverages these insights incorporate the 2D modality an implicit manner during pretraining, requiring additional semantic labels. Specifically, we use the visual encoder of VLMs to generate 2D visual features, which supervise 3D features from the IF head to simulate 2D features. Then, the learned 3D features serve as source of 2D information, exploited to build multimodal understanding of novel classes during subsequent meta-learning and inference stages. Further pretraining details can be found in Appendix B.
Limitations and Broader Impacts. Though our method significantly outperforms existing methods by exploiting free modalities, it might learn inductive bias towards the studied datasets and its efficacy on other scenarios needs to be studied before deployed in a practical perception system. Since our method needs to be trained GPUs, the development and potential deployment lead to carbon emissions and have negative impact on the environment.
D ADDITIONAL VISUAL RESULTS
In this section, we provide more extensive visual comparisons to underscore the efficacy of our approach handling few-shot segmentation tasks. In Fig. and Fig. 7, we provide the additional segmentation results that compare MM-FSS with the previously established state-of-the-art model, COSeg (An et al., 2024). These comparisons clearly demonstrate the enhanced few-shot segmentation capabilities of MM-FSS, illustrating its effective integration of different modalities for capturing more comprehensive understanding of novel concepts.
Moreover, Fig. 8 includes further visual comparisons across the two feature heads in our model the Intermodal Feature (IF) head and the Unimodal Feature (UF) head-and the final predictions obtained by fusing outputs from both heads through our TACC module. These results illustrate that the IF head is less prone to training bias, contrast to the UF head which exhibits greater bias due to its optimization during the meta-learning step. Our proposed TACC effectively leverages the bias- resistant properties of intermodal features to calibrate final predictions during test time by dynamically controlling the calibration for each meta sample, greatly improving the few-shot generalization ability.
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Chair
Bookcase
Ceiling
Door
Floor
Support
Support Mask
Query
Ground Truth
COSeg
MM-FSS (Ours)
Figure 6: Visual comparison between COSeg (An et al., 2024) and our proposed MM-FSS on the S3DIS dataset. Each row represents one 1-way 1-shot segmentation task with the target class of the given color. MM-FSS predicts masks of higher quality and fewer artifacts compared to COSeg.
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Table
Ceiling
Door
Chair
Window
L
Support
Support Mask
Query
Ground Truth
COSeg
MM-FSS (Ours)
Figure 7: Visual comparison between COSeg (An et al., 2024) and our proposed MM-FSS on the S3DIS dataset. Each row represents one 1-way 1-shot segmentation task with the target class of the given color. MM-FSS predicts masks of higher quality and fewer artifacts compared to COSeg.
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Window
Table
Chair
Bookcase
Support
Support
Mask
Query
Ground Truth
IF Head
Head
Default
Figure 8: Visual comparison of predictions from each head and our final prediction using TACC (Default) on the S3DIS dataset. Each column represents one 1-way 1-shot segmentation task with the target class of the given color.
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Reviews:
[{"review_id": "7fpT0hlXXU", "reviewer": "Reviewer_YYsz", "summary": "This paper investigates the few-shot 3D point cloud semantic segmentation task. It proposes a cost-free multimodal FS-PCS setup. Based on this setup, the authors introduce MM-FSS to utilize the multimodal information to help with few-shot learning. The model includes MCS to generate multimodal correlations and MSF to refine the correlations by textual guidance. To mitigate training bias, the authors also propose a TACC technique to adaptively calibrate predictions at test time. The experiments demonstrate its effectiveness.", "questions": "See weakness.", "limitations": "", "rating": 8, "confidence": 5, "soundness": 4, "presentation": 3, "contribution": 4, "strengths": "+ The paper is well-written and easy to follow, with clear motivations for each proposed design.\n+ It introduces the first multimodal few-shot 3D segmentation setting, which is cost-free and doesn’t increase labeling effort, consistent with standard few-shot tasks. The idea of leveraging available free modalities to help few-shot learning could provide valuable insights for the field.\n+ The proposed model is novel and well-justified. The MCS and MSF modules use different modalities by exploiting cross-modal visual correlations and textual semantics, respectively. The TACC module is parameter-free and used at test time to calibrate predictions adaptively based on support samples.\n+ The performance gains are substantial, and the experiments are thorough. Detailed ablation studies and visualizations are provided and effectively validate the proposed design choices and justify their motivations.", "weaknesses": "Certain paragraphs could be more concise. For example, the paragraph (lines 235-240) explains the two training steps. Then the paragraph (lines 259-266) writes about these two steps again. These two parts could be merged to improve conciseness.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "YGrbOTbNMA", "reviewer": "Reviewer_8jox", "summary": "This paper studies the Few-Shot 3D Point Cloud Semantic Segmentation task by using multimodality information. Specifically, it uses 2D image modality to  extract additional intermodal features apart from the unimodal visual features. It also uses text embedding to refine the multimodal correlations generated by the proposed Multimodal Correlation Fusion module. Moreover, it presents the Test-time Adaptive Cross-modal Calibration technique to improve the generalization capability. The experimental results show that the proposed methods can achieve higher performance than previous works.", "questions": "Please answer the question in weaknesses.", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The writing is good, making this paper easy to follow.\n\n- The performance is good.\n\n- The experiments are adequate.\n\n- The introduction of the multimodality information is useful.", "weaknesses": "- How the design of the MSF module can achieve the improvement for the correlation. Please give more explanations.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "XFh2m2aJ2N", "reviewer": "Reviewer_k1NE", "summary": "This work proposes a multimodal few-shot segnet (MM-FSS) to leverage multimodal information from textual labels and 2D images. Specifically, they propose multimodal correlation fusion (MCF) to fuse the multimodal correlations and enhance the fused features by multimodal semantic fusion (MSF) with textual information. Besides, the authors proposed test-time adaptive cross-modal calibration (TACC) to calibrate the predictions during test time. Experimental results on both S3DIS and ScanNet show the superior performance of the proposed method.", "questions": "Please refer to the weakness.", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. It makes sense to use complementary information from multimodal inputs for point cloud segmentation.\n2. The ablation studies on ScanNet prove the effectiveness of the proposed models, including MCF, MSF, and TACC.\n3. The features from 2D images are learned during the pre-training step. In this way, the proposed method does not rely on 2D inputs during inference.\n4. The proposed MM-FSS achieves promising performance on two benchmark datasets compared to the state-of-the-art methods.", "weaknesses": "1. The idea of using multimodal information for 3D point cloud segmentation is not new. As noticed by the authors, such an idea has been explored for fully supervised or more challenging tasks like open-vocabulary segmentation. It is unclear why the prior few-shot methods only use unimodal information. It would be better to provide more discussions on the difficulties or challenges when using multimodal features for few-shot segmentation.\n\n2. The claim about “cost-free” is ambiguous as “cost” may mean many things, such as training cost. I don't think it is necessary to claim the \"cost-free\".", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "nfqWFFNX9S", "reviewer": "Reviewer_qoPY", "summary": "In the paper, the MultiModal Few-Shot SegNet was proposed, which segments novel categories with minimal annotated support samples utilizing textual labels and the potentially available 2D image modality.  The main contributions are as follows: 1) To incorporate the textual modality of category names and the 2D image modality in few-shot 3D learning scenarios without additional annotation cost; 2) A new model, MM-FSS, was introduced, and information from different modalities was utilized. Experiments validated the value of the proposed method and the efficacy of the proposed method across different few-shot settings.", "questions": "1. In Line 110，ii should be iii.\n2. Whether there is a comparison of the computational complexity and computational efficiency of different algorithms？\n3. The significance of the important regions depicted in Figure 3 is unclear. The authors should provide additional details to enhance reader understanding.\n4. The authors input multiple images into the VLM; how do they determine the viewpoints for the RGB images fed into the VLM?\n5. The authors demonstrate a notable performance increase by introducing textual information into the query. I am curious if there is potential to also incorporate textual or other types of information into the support set to achieve further gains.\n6. The selection of support appears somewhat random. If the authors could provide some theoretical guidance in the appendix regarding this selection process, I believe it would greatly enhance the significance of this work.", "limitations": "", "rating": 6, "confidence": 5, "soundness": 4, "presentation": 3, "contribution": 3, "strengths": "Based on the learning method, a large number of annotations are required for the identification and segmentation of new target categories in the point cloud, which is very time-consuming and laborious. The paper is of great significance to reduce the data annotation process through the combination of text and images.The MM-FSS model exhibits superior generalization capabilities, especially in few-shot settings, providing valuable insights for future research directions in the field.", "weaknesses": "1. The paper's description of the 2D image modality's integration into the multi-modal FS-PCS setup could be more detailed, providing clearer insights into how the 2D images are leveraged to enhance the model's performance without additional annotation costs.\n2. The article does not offer a detailed analysis of the performance enhancements specifically attributable to the multi-modal information fusion during the modeling process, making it challenging to assess the individual impact of textual and 2D image modalities on few-shot segmentation performance.\n3. The existing FS-PCS datasets are generally on a small scale, the practicability and effectiveness of the method in this paper need to be further verified in the future.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Spotlight)