Novel Class Discovery for Point Cloud Segmentation via Joint Learning of Causal Representation and Reasoning
Authors: Yang Li
Aming WU
Zihao Zhang
Yahong Han
Venue: NeurIPS 2025 poster ()
Keywords: Point Cloud Segmentation
Novel Class Discovery
Causal Learning
Primary area: applications

Abstract: In this paper, we focus on Novel Class Discovery for Point Cloud Segmentation (3D-NCD), aiming to learn a model that can segment unlabeled (novel) 3D classes using only the supervision from labeled (base) 3D classes. The key to this task is to setup the exact correlations between the point representations and their base class labels, as well as the representation correlations between the points from base and novel classes. A coarse or statistical correlation learning may lead to the confusion in novel class inference. lf we impose a causal relationship as a strong correlated constraint upon the learning process, the essential point cloud representations that accurately correspond to the classes should be uncovered. To this end, we introduce a structural causal model (SCM) to re-formalize the 3D-NCD problem and propose a new method, i.e., Joint Learning of Causal Representation and Reasoning. Specifically, we first analyze hidden confounders in the base class representations and the causal relationships between the base and novel classes through SCM. We devise a causal representation prototype that eliminates confounders to capture the causal representations of base classes. A graph structure is then used to model the causal relationships between the base classes' causal representation prototypes and the novel class prototypes, enabling causal reasoning from base to novel classes. Extensive experiments and visualization results on 3D and 2D NCD semantic segmentation demonstrate the superiorities of our method.

Novel Class Discovery for Point Cloud Segmentation via Joint Learning of Causal Representation and Reasoning
Yang Li', Aming Wu?, Zihao Zhang', Yahong Han
'School Artificial Intelligence, College of Intelligence and Computing, Tianjin University, China School of Computer Science and Information Engineering, Hefei University of Technology, China 1iyang13890t.ju edu.u.ueedeeeo en, zhangzzhao249000:jju edu. amwußhfut edu. en, yahong@tju. edu..dd... cn
Abstract
In this paper, we focus on Novel Class Discovery for Point Cloud Segmentation (3D-NCD), aiming to learn a model that can segment unlabeled (novel) 3D classes using only the supervision from labeled (base) 3D classes. The key to this task is to setup the exact correlations between the point representations and their base class labels, well as the representation correlations between the points from base and novel classes. coarse or statistical correlation learning may lead to the confusion in novel class inference. If we impose causal relationship as strong correlated constraint upon the learning process, the essential point cloud representations that accurately correspond the classes should be uncovered. To this end, we introduce a structural causal model (SCM) to re-formalize the 3D-NCD problem and propose new method, i.e., Joint Learning of Causal Representation and Reasoning. Specifically, we first analyze hidden confounders in the base class representations and the causal relationships between the base and novel classes through SCM. We devise causal representation prototype that eliminates confounders capture the causal representations of base classes. A graph structure is then used to model the causal relationships between the base classes' causal representation prototypes and the novel class prototypes, enabling causal reasoning from base to novel classes. Extensive experiments and visualization results on 3D and 2D NCD semantic segmentation demonstrate the superiorities of our method.
Introduction
Point Cloud Semantic Segmentation is one of the key tasks in autonomous driving Landrieu and Simonovsky (2017) and robotic perception Ghosh et al. (2017). However, traditional approaches adopt a "closed-worll assumption, which limits their applicability in real-world scenarios. The task of novel class discovery in point cloud semantic segmentation aims to learn a model that can segment unlabeled (novel) 3D classes using only the supervision from labeled (base) 3D classes Riz et al. (2023 2024); Xu et al. 2024). The key to this task is to setup the exact correlations between the point representations and their base class labels, well as the representation correlations between the points from base and novel classes. However, in practical scenarios, the base class classifier often learns shortcut features from the point cloud Geirhos et al. (2020); Hermann and Lampinen (2020), which are spurious correlations that hinder the learning of unbiased semantic representations for the base class, thereby easily misleading the model into making wrong predictions. Furthermore, due the absence of labels and clear semantic connections with the base class, the novel class is prone to being misclassified as a base class. To this end, we analyze the problems as follows:
Corresponding author.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Mutivicaal correlation
Conselity
Gugai Gasoroaian
OngteiNaachel
Siatire
wing
Not for nount Sabtrer of
lle cumedc of clss
Sout
Lakee
Bew
Cleor
Bow Clzun
Novel Class
Haua Class
(a)
(b)
Figure 1: (a) On the left, misled by the shorteut feature of 'circular support', learning with statistical correlation fails to identify the novel class 'chair' but assign the label 'stool' straightforwardly. On the right, causality-basee a key element for causal reasoning, correctly predicting the novel class. (b) SCM of 3D NCD: B is the base class, N the novel class, X the original point cloud, and U the confounding factor.
learning captures the base classes' causal representation (leg) and uses it as
Firstly, traditional base class classifiers are essentially statistical models that focus on superficial dependencies between data and labels, without uncovering the intrinsic causal mechanisms, As result, they tend to learn shortcut features, which are regarded as confounding factors in the field of causal learning Schölkopf et al. (202T). As shown Figure (a), statistical correlation learning may cause confusion in novel class inference. If we impose causal relationship strongly correlated constraint during the learning process, the essential point cloud representations that accurately correspond to the classes should be uncovered.
Secondly, novel classes are often variations of base class through some causal mechanisms. For example, in point clouds, the posture of a 'rider' can be viewed as a causal variation of 'person' under the influence of the "bicycle' context. Similarly, the structural similarity between 'truck' and 'car' reflects shared causal prior knowledge of 'vehicle." Therefore, we need to leverage the causal relationships between base and novel classes to enhance the model's ability infer novel classes.
To this end, we introduce a structural causal model (SCM) Pearl et al. (2016) to re-formalize the 3D-NCI problem. As shown in Figure (b) This SCM consists of four causal variables: the base class B and novel class N extracted from the original point cloud data X, the confounding factors (representing shortcut features or non-causal features). The path U + B indicates that the confounding factor affects the point cloud representation learning of the base class, while the path B + N reflects the causal impact of the base class on the novel class. Our goal is remove U to obtain the causal point cloud representation of base class and establish the causal relationship between the base and novel classes, thus enabling causal reasoning from B to N.
To achieve this, we first introduce causal representation prototype learning, which eliminates con- founding factors through causal adversarial mechanisms and extracts causal representations obtain base class prototypes. Next, to enable causal reasoning, we introduce a graph structure with nodes representing the prototypes of the base and novel classes, and edges denoting their causal rela- tionships. We then apply two constraints: causal pruning, which removes edges with low causal correlations reduce irrelevant base class features on novel class learning, and inference direction consistency, which ensures the information flow in the graph aligns with causal relationships. Finally, the optimized graph is input into graph convolutional network Kipt and Welling (0166) base class labels and novel class pseudo-labed
generate
Our contribution can be summarized in three folds. First, introduce causality- based approach to excavate intrinsic causal mechanisms. This is the first method to incorporate causality into 3I) NCD. Second, propose causal representation prototype learning and graph-based causal reasoning methods to learn base classes' causal representation and model the causal relationships between base and novel classes. Third, extensive experiments and visualization results 3DD and 2D NCD semantic segmentation demonstrate the superiorities of our method.
Related work
Point Cloud Semantic Segmentation. The goal of point cloud semantic segmentation is to partition point cloud scene into different meaningful semantic parts. Traditional 3D semantic

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segmentation methods Charles et al. (2017): Qi et al. (2017); Milioto et al. (2019) rely on full
annotations, segmenting classes labeled in the training set. Several task settings extends the traditional 3D segmentation to the novel class. Among these, few-shot 3D segmentation An et al. [2025] 2024)
reduces annotation burden but still requires limited labeled samples for novel classes Similarly,
zero-shot Liu et al. (2024a): Yang et al. (2023) and open-vocabulary Wei et al. (2025); Liu et al.
(2024b) 3D segmentation rely on external semantic information like textual descriptions to identify novel classes. Novel class discovery Riz et al. (002 2024); Xu et al. (2024) in 3D segmentation aims to learn a model that can segment novel classes using only the supervision from labeled known classes, Critically, 3D-NCD demands neither the novel class samples of few-shot learning guidance required by zero-shot and vocabuaaay methods. Instead, 3D-NCD autonomously discovers and segments novel classes by identifying patterns distinct from the known classes, offering a more practical and unconstrained solution for dynamic open-world environments (e.g., autonomous driving and domestic robotics), where objects may suddenly appear without prior definition labels.
the textual
Novel Class Discovery. NCD leverages known classes to infer the semanties of novel classes. The 2D- NCD primarily follows two approaches. The first approach Hsu et al. (2018); Yen-Chang Hsu (2019) trains a classification network on known data and then clusters novel data based on the network's predictions. However, this method often overfits to the base classes, thereby limiting its performance on novel classes. The second approach Han et al. (2021): Wang et al. (2020a) incorporates both known and novel samples into clustering to promote shared feature representations, thus improving generalization. Although NCD has made some progress in 2D. research on NCD in the domain of 3D remains limited Weng et al. (2023); Riz al. (2023 2024): Xu et al. (2024). Additionally, these 3D-NCD methods rely on statistical similarity and overlook the causal relationships between base-novel classes. To address this, we introduce causal representation learning (Schölkopf al. (002)) Zhang et al. (2023) to capture the causal representation of base classes and model the causal relationships from base novel classes, using graphical structure to implement causal reasoning.
Causal Learning in Computer Vision. Causal relationships have recently been widely applied in learning-based 2D computer vision Lis et al. (2024c); Zhang al. (023); Chen et al. (2023). Incorporating causality into machine learning helps to produce more learnable and interpretable models, since traditional CNN architectures consider only statistical correlation without accounting for causal structure. In the 3D point cloud domain, previous work such as CausalPC [aanng et al. (2024) constructs SCM Pearl et al. (2016), treating adversarial perturbations and sensor noise the confounding factor U, and purifies point-cloud inputs by identifying and removing its causal effect to maintain classifier accuracy under various attacks. In contrast, in our method U denotes non-causal shortcut features Geirhos et al. (020)): Hermann and Lampinen (2020) in the point cloud, which are eliminated via an adversarial network to enable novel class discovery and precise segmentation.
3 Method
Problem Definition. In point cloud segmentation, the NCD problem aims to train segmentation network to process 3D datasets that contain partially labeled points from known classes and unlabeled points from novel classes, enabling the network to classify the 3D points in the scene into known or novel semantic classes. Formally, the training set consists of multiple 3D scenes, each containing
two parts: 1) Labeled part: D. = {pp5)
( r! ) where p represents the i-th point, and (( E A,
1 { where p(4 represents the j-th point belonging to the novel class set A., and A, n A., = Ø (the known class set and the novel class set do not overlap). The goal is to train a point cloud segmentation network F that can accurately classify each point in novel scenes from the test set into one of the classes in A, U A.,
its corresponding known class label. 2) Unlabeled part: D,, = (pi (
3.1 A Causal View of Novel Class Discovery in Point Cloud Segmentation
We model this task using an SCM al. (2016) shown in the Figure (b). This SCM consists of four causal variables: the base class B and novel class N extracted from the original point cloud data X. the confounding factors U. We now present the three main causal paths in this task.
1) B < X N: This represents the the base class point cloud data B and novel class point cloud data N extracted from the original point cloud data X, with causal path between them.
3

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2) B - N: The base class B influence the novel class N through some causal mechanisms. For example, the features of 'person' are adjusted by the context of 'bicycle' to generate the features of "rider'. The similarity between 'truck' and "car" reflects shared causal prior knowledge of 'vehicle."
3) U - B: The model often learns shortcut features Geirhos et al. (2220) from the base class point cloud data B, which are regarded as confounding factors U that hinder the learning of base classes' causal representations. As shown in Figure I (a), U could be a visually prominent but non-causal element like a 'circular support', whereas the base classes' causal representation is the 'leg'.
3.2 Causal Representation Prototype Learning
We first use the feature extractor So to extract the initial features Z E &Rdx for the base class and Z' E R2xd for the novel class from the point cloud data X. where P is the number of sampled points and d is the feature dimension. We use MinkowskiUNet Choy et al. (2019) the backbone. For the base class, we denote the point cloud as XB, and YB is the corresponding label. After clustering, we obtain the set of novel class prototypes {π1, 122, ......... 77 }, and at time = O, we define the base class causal representation prototypes as C' = {cl , C2
(0)) ....,,C/)) (0)
In Figure I (b), the causal path U - B leads to spurious correlations in the learned P(B[Z) rather than the true causal relationship P(B]do Zcausa)), (ZB)do(Zcuusa),, where the do(-) operator denotes causal intervention [Pearl et al. (2016). Our core objective is to learn a pure causal representation Ze for the base class B, uncontaminated by the confounding factor U. Theoretically. if U were observable, its influence could be eliminated via the backdoor adjustment formula Pearl et al. (0016): P(B]do Z))
Σ., Z( u) P(u). However, confounding factors are often unknown or difficult to define explicitly. Existing representative 2D causality-based methods simplify them to the average of visual features Wang et al. (2021 2020b), but this may not be accurate enough for complex point cloud data.
Therefore, we aim to learn a feature representation Z that is mutually independent of the confounding factor U (i.e., Z U) which involves minimizing the mutual information [Z: U) between them. This objective aligns closely with the following fundamental causal principle:
Principle 1 (Schoelkopf et al. (2012).. Independent Causal Mechanisms (ICM) Principle: The conditional distribution of each variable given its causes (i.e., its mechanism) does not inform or influence the other mechanisms.
According to the ICM principle, the true causal mechanism generating the base class B should be independent of the mechanism producing the confounder U. Striving for Z - U helps to disentangle these mechanisms, cnabling & to stably represent the intrinsic properties of without being perturbed by variations in U. Adversarial training provides an indirect way to handle implicit confounding factors. Drawing inspiration from GAN Goodfellow et al. (2014). we design an adversarial process:
min max LADV = Coto (fo( YB) - Auav (go( U).
(I)
Here, Aade is the weight coefficient of the adversarial loss, balancing the losses. The feature extractor fo aims to extract the causal features of base class data XB while minimizing components related to U, whereas the adversarial network Is attempts to recover the confounding factor U from Z. If go successfully recovers U, it indicates that Z still contains confounding information; if not, Z has effectively removed the interference from U. This process can be seen as a mechanism for approximating Z 1. U and encouraging the learning of representations compliant with the ICM principle. Lote is the classification loss,
i implemented with a cross-entropy loss function:
Las Las(fo(XB)) = = - [YloogfooXx))
(2)
Ladv is the adversarial loss, aiming to maximize the ability of the adversarial network go to recover U from Z, while minimizing the correlation between Z and U. This is achieved using binary cross-entropy loss:
Lade (g U)) = -Ello((o U + (1- (1 (1-00 U)]]-
(3)
After adversarial training, we update the prototypes C' from the optimized features Z. The normalized similarity weight between j-th point cloud feature and i-th prototype is:
cxp(sim( Zz, C:))
Wo EM 1= exp(sim(Z, Cz))
(4)

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Inper Polnt Cloud
Neveiclos
Bos Claw
Protscpo
Nodey Repr.
Canual
GCN Preadu Lobeling
Encotee
Causal Represcntation Protetype Learning
Base Class
Eacifective Edge
Wrong Dirrrtion Edge
Repe.
Casual Remsniny
Update Structure
Figure 2: Overview of our method. We extract the base and novel class representations from the initial point cloud. In the base class point cloud data, there exist noisy confounding factors. We obtain causal representations through causal adversarial deconfounding and then generate base class prototypes. Meanwhile, the novel class representation also generates prototypes, which are combined with the base class causal representation prototypes as nodes to construct the graph. The causal adaptive adjacency matrix dynamically adjusts the graph structure, Lproinng aims to identify and remove ineffective edges, and Ldirection ensures that the information propagation direction between nodes aligns with the actual causal path. After these causal reasoning optimizations, the updated graph is input into the GCN to generate labels for base classes and pseudo-label for novel classes.
where sim( 2, C.) is the similarity between the point cloud feature Zj and the prototype C₁ imple- mented using cosine similarity. Here, j E {1, ..,,,.., P} represents all sampled points in the point cloud. The updated value of the i-th prototype after the 1-th iteration is computed as:
E, WAy Zz c c((+1)
(5)
Σ, Wij
To ensure efficient and effective matching between the point cloud features Z and the prototypes C, we use the causal prototype matching loss, which is computed as:
CPRO Σ>>>>> Wi sim C.) - |
(6)
where À is the regularization coefficient that controls prototype complexity and prevent overfitting.
Please note that the extraction of causal representations relies on learning clear and reliable causal relationships from data. Although novel class features also contain confounding factors, due to the lack of explicit labels, no direct supervision can be provided. The pseudo-labels for the novel class relies on model's assumptions and reasoning, which may introduce bias or noise, and we cannot ensure that the features learned from the pseudo-labed relationships. Thus, this work focuses on extracting causal representations for base classes. During novel class inference, these representations serve reliable prior knowledge, helping the model understand novel class features through causal reasoning.
consistent with the true causal
3.3 Causal Reasoning Graph Construction
Figure T (b) illustrates the causal path from the base the novel class (B N), and key challenge how to effectively model this causal path in a high-dimensional representation space. A natural idea to impose structured representation of the potential causal influences from base to novel classes, yielding more robust and interpretable framework for knowledge transfer. To this end, we draw inspiration from Causal Bayesian Networks (CBNs) Pearl (2009), which use directed edges to unambiguously encode causal dependencies. Underlying CBNs is fundamental assumption:
Principle (Spirtes et al. (1993)). Causal Markoy Principle: In causal DAG G, each variable is conditionally independent of all hon-descendant variables given its direct parents.
This aligns with the essence of NCD task: the representation of novel class should be influenced by the hase classes that are direct causes, while also incorporating novel features inherent to the novel class and not fully explained by the bases. Based the above thinking, construct
5

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graph to explicitly model the hypothesized causal path (B - N). The graph consists of causal representation prototypes of M base classes, denoted as C {ci, C2..... ....... Cas). and K novel class prototypes, denoted as N = n, 12, ... .. The node set is V CU N. The graph contains Frcausal = ((G, n2) SiS M, S S K] where Waj is the weight of the edge.
set of edges
To address dynamic novel class inputs and the catastrophic forgetting problem in open world, we design a causal adaptive adjacency matrix A [Aij]M MxK, where each element A:j waj represents the causal relationship strength from the node ci to the node n.j. By introducing a self-attention mechanism Vaswani et al. (2017). the weight Wiy of each edge is dynamically adjusted:
Attention(c;,1))
Wij softmax
where 7 is the temperature coefficient. During information propagation in the set Ecausal. the causal path B > N may not always be strictly followed, leading to ineffective causal knowledge transfer interference. To ensure causal directionality, introduce the inference direction consistency constraint:
dirrction
Σ
(we (1 I(c, - ng)))²
8
(GLR))E
I[(; + n.)) is indicator function where II(; - n2)) = 1 indicates correct causal path with zero loss. If the propagation direction incorrect, the loss increases with the edge weight Wijs guiding the model learn the correct direction.
In the NCD task, many base class nodes may have a weak or irrelevant impact on the novel class nodes, leading to ineffective information transmission and interfering with the generation of pseudo- labels for the novel class. To mitigate this, we introduce a causal pruning constraint, removing edges with causal weights below a learnable threshold 0:
Crruning (0)
Σ (Gun))EE
I(wiy < <) (.0)wuj w.y.
Here, I(wy < <0) indicates edge removal, while w/g penalizes weak connections. As causal relation- ships in the Ecausal change with novel class nodes input, the model dynamically adjusts the threshold 8, which is set as a learnable parameter.
3.4 Pseudo-label Generation Based on GCN
Existing 3D NCD methods generate pscudo-labels by directly measuring similarity between novel and base classes, ignoring the complex higher-order dependencies between classes. To address this, we introduce graph convolutional network, which can handle the complex relationships between novel and base classes by leveraging multi-layer propagation and neighbor aggregation to generate high-quality labels. The update rule for each novel class node ny is:
M Σ
n (++1) =
Wij
Wysk
n
(t)
(10)
dgdj
dddx
k=
Here, di, dj. dy are node degrees, and a is LeakyReLU. After multiple GCN layers, the final repre- sentation of novel class node final used assign pseudo-labels:
U = arg min sim(n final c.).
(11)
Where új is the predicted pseudo-label for the novel class final representation and similarity is measured via cosine similarity.
4 Experiments
4.1 Experimental Setup
Datasets and Evaluation Metries. We evaluated our method on the SemanticKITTI BBeheeeeTTTBBBBeeeyyyy et al. (2019) and SemanticPOSS Pan et al. 20200) datasets. We adopted the same dataset partitioning

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Table 1: The novel class discovery results on SemanticPOSS dataset. 'Full' denotes the results obtained by supervised learning. The green values are the novel classes in each split.
Spdit
Method
build.
Jence
pers.
plants
rider
traf.
trashe.
trank
Novel
Known
All
Full
48.3
86.9
57.2
38.5
49,8
79.1
63.8
$1.7
57.0
56.1
33.5
27.4
51.5
BUMS
2021
25.7
16.4
29.4
36.8
43.8
26.3
13.1
26.3
18.2
16.9
17.4
20.3
NOPS Kir
30.4
1.2
13.5
24.1
68.1
44.7
42.1
19.2
47.3
24.6
21.3
35.7
26.6
29.4
9
SNOPS RI
34.2
56.8
10.0
13.2
18.7
77.3
45.8
58.6
17.3
46.4
22.6
22.9
51.3
25.3
53.6
DASI
46.3
51.5
6.0
35.7
48.5
K3,0
67.9
53.1
35.5
59.3
51.0
2.8
15.5
-65.
38,0
41.2
40.4
55.3
39.1
58.2
19.4
13.3
51.3
41.7
BUMS 2hap 2
sl.
2011
15.2
68.0
28.0
34.0
11:9
75.1
36.0
74.5
26.9
46/6
36.0
5.6
23.1
21.0
40.0
35.6
NOPSEE REPora
20.4
71.4
76.7
12.2
3.9
78.2
56.8
74.0
15.3
16.9
23.5
13.7
23.5
30.0
16.2
36.4
1 SNOPS SESTE
16.3
71.4
30.0
19.8
24.9
77.1
55.0
73.4
15.8
16.4
22.5
15.77
23.6
92.1
16.7
57.2
DASL
83.2
46.7
25.4
23.9
77.3
53.1
77.1
52.5
57.3
35.0
18.0
36.2
16.4
64.0
84.0
32.4
17.3
62.3
77.9
34.0
62.0
36.8
20.5
37.3
48.4
45.8
Zino EUMS/
EUMS
eL.
2021
40.1
69.5
27.7
13.5
54.9
76.0
54.7
75.6
5.3
39.2
7.8
8.5
11.9
X.3
44.0
55.7
NOPSTR
37.2
71.8
29.7
14.5
28.4
77.5
52.1
73.0
11.5
47.1
0.5
10.2
14.8
9.0
46.2
36.0
2 SNOPS
38.4
72.5
78.0
14.5
76.2
73.1
54.7
74.3
10.0
46.3
23.0
10.2
17.7
16,9
44.5
S8.1
DASI
45.3
82.8
49.5
78.4
46.3
76.7
66.2
77.2
10.9
58.4
18.6
7.3
8.2
12.6
$3.8
44.3
45.1
83.3
52.6
58.0
46.9
78.9
68.6
78.7
19.8
59.4
14.8
6.2
13.6
56.1
46.3
FUMS Phop ml. 2071
41.2
70.7
76.1
4.3
18.3
76.7
16.3
75.4
25.8
36.1
28.5
0.4
24.4
13.0
44.7
$7.4
NOPST Ferera
38.6
70.4
30.9
0.0
29.4
76.5
56.0
71.8
17.0
31.9
36.3
1.0
22.6
10.9
43.9
36.1
3 SNOPS
39.4
70.3
30.0
9.1
26.8
77.6
54.3
72.5
16.0
49.9
28.1
1.3
23.5
20.1
43.9
58.4
DASL
45.5
82.9
47.7
0.0
45.1
77.8
66.3
77.7
34.3
49.1
30.5
4.0
15.3
17.7
52.8
44.7
44.7
81.7
50.0
36.2
47.4
75.1
65.1
76.5
31.0
47.5
33.9
24.9
27.9
53.0
47.2
Table 2: The novel class discovery results on the SemanticKITTI dataset. 'Full' denotes the results obtained by supervised learning. The four groups represent the four splits in turn.
Method
blacle helst
build.
car
Tence
midee
moctet
oth-g.
oth-y.
pork,
pees,
pole
road
sidem,
terra.
traft.
truck
trunk veget.
Novel Known
All
4.8
50.2
K7.1
92.5
36.5
25.0
2.5
27.8
90.
77.1
63.7
41.5
SSD
Wi
50.5
BUMS
5.5
00.0
15.5
79.2
16.9
2.5
16.5
12.7
19
42.6
0.1
10.3
47.4
37.9
584
38.0
21.1
29.3
SNOPSIIzz
6.6
43.9
73.0
83.3
13.6
24.7
2.5
24.6
49.5
27.4
15.7
37.5
45.0
26.0
31.3
DASL Ku al
4124
5.5
31.1
7466
02.3
20.8
22.8
0.0
0.0
23.3
24.8
27.7
59.7
41.4
22.5
23.6
39.3
43.6
$1.1
66.4
45.7
33.7
36.8
Oli
5.2
43.9
70.5
9044
29.8
21.4
0.6
0.0
25,0
22.5
25.0
56.3
53.1
24.1
23.7
33.5
41.1
51.4
63.2
46.9
32.2
36,9
BUMS Zlean
7.5
76.8
19.6
1.4
56.3
12.0
72.4
35.6
NOPSEEZ 207 2ER
3023
7.4
51.2
84.5
53.9
7.3
25.9
1.8
0.0
22.2
19.4
30.4
37.6
90.1
72.2
60.3
16.8
57.3
49.3
85.1
25.4
46.2
40.7
SNOPS 2012
2024
7.5
43.5
K5.3
05.77
18.9
24.4
3.5
0.0
23.9
19.1
27.0
36.5
89.3
71.9
62.8
17.2
55.9
29.4
54.4
27.2
45.
80.4
3.7
0.0
66.7
04.5
67.6
EUMS Zhsaret m
302
8.3
50.8
83.0
85.1
17.9
2.8
2.3
0.2
25.4
25.0
20.2
83.5
71.0
57.9
8.6
27.2
35.4
77.0
12.4
42.2
36.6
NUPS
6.7
Wh.4
0.6
61:22
17.88
46.2
64.3
48.0
39.7
99.6
DASI
3.6
03.3
10.2
0.0
32.2
92.7
77.4
62.2
10.7
51.7
86.
20L1
50.4
43.5
Oli
9.5
66.0
89.2
93.3
27.7
21.7
0.0
0.2
9.6
33.8
34.0
33.1
93.1
77.9
61.7
20.5
7.8
52.8
66.3
18.5
51.8
43.0
BUMS Zhvo
4.9
14.0
55.3
16.2
76.2
7.1
35.7
NUPS Ki 01.
3041
2.3
27.8
860
89.9
25.1
24.5
19
3.1
18.2
3UL1
16.3
19
90.
73.5
61.00
17.4
49.8
64.0
K32
12.4
4910)
41.3
SNIIPIIE
26
4.7
$1.5
84.
86.7
22.8
21.5
8.3
2.6
17.9
76.7
18
16.9
89.7
72.5
408
16.1
41.1
45.7
60.9
16.9
47.9
40.9
DASI
0054
2.5
$2.5
83.1
93.3
28.1
24.0
6.1
23.7
35.6
15.5
59.8
93.2
77.6
61.4
37.8
56.6
52.1
66.7
12.6
54.6
45.8
6.2
87.4
15.1
46.8
strategy as NOPS Riz et al. (2023). where one subset is designated as the novel classes, and the remaining subsets as the base classes. We evaluated on sequences 08 and 03 from the SemanticKITTI and SemanticPOSS datasets, respectively, which include both known and novel classes. For the known classes, we report the IoU for each class. For the novel classes, we use the Hungarian algorithm Kuhn (2010) to match cluster labels with ground truth labels and then present the IoU values for each novel class.
Choy et al. (2019) architecture, consistent with existing methods Riz et al. (2023 2024);; Xu et al. (2024). We extracted point-level features from the penultimate layer. The number prototypes for both base and novel classes is consistent with the number of classes in the dataset. The optimizer used is AdamW, with an initial learning rate of Ic-3, decaying every 5 epochs until reaching a minimum value of 1c-5, The weight coefficient Aade, which balances classification and adversarial losses, and the threshold 0 in the causal pruning constraint are both initially set to 0.5 and are dynamically adjusted during network training. For the hyperparameters, we set the temperature parameter 7 to 0.06 and the regularization coefficient À to 0.02. The number of graph convolution layers is set to 3.
Implementation Details. We implemented our network using the MinkowskiUNet--e
4.2 Comparison with the State of the Art
SemanticPOSS dataset. The results in Table show that our method achieves the highest mIoU across all classes in all splits. In split 2, the base class mioU reaches 56. 16,1% a 3.3% improvement over DASL, demonstrating effective mitigation of base class confounding through causal representation learning. In the challenging split 3, our method outperforms DASL on novel classes by 10.2%, with
7

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DASL
Ours
GT
Road
Sidwalk
Terrain
Building
Vegitttinn
Knowa
Visualization comparison between our method, NOPS, and DASL on the Seman- ticPOSS and SemanticKITTI datasets. In the first row, NOPS and DASL, relying on statistical methods, fail to eliminate non-causal features, causing confusion between 'Plants' and 'Building'. : In the second row, they also confuse 'Road' with 'Sidewalk and 'Terrain' with 'Vegetation', In contrast, our method achieves better results and generates high-quality pseudo labels.
Figure 3:
Baseline
Baseline#CRP
Baseline+CRPP CPPCG
Ours
Car
Plants
Building
Ground
Known
Figure 4: Ablation experiment visualization.Thh introduction of CRP significantly reduced the misclassification between 'Plant' and 'Building." Subsequently, integrating CRG further alleviated the confusion between 'Plant' and 'Car.' Finally, incorporating GCPL effectively reduced the confusion among 'Plant,' 'Car,' and 'Building'.
notable 36.2% for the cone-stone class, while both DASL and NOPS achieve O. These results demonstrate that our use of causal reasoning enhances the segmentation accuracy for novel classes.
SemanticKITTI dataset. The results in Table 2 show that our method achieves the highest mloU across all classes in all splits. Our method improves novel class segmentation by 4.9% split 1. Specifically, in the car class, it achieves an IoU of 82.6%, a 13.9% improvement over SNOPS. In the motorcycle class of split 3, method achieves an 11.5 improvement. Figure presents visual comparison of NOPS, DASI, and our method.
4.3 Ablation Study
Our method consists of three components: causal representation prototype learning (CRP), causal reasoning graph (CRG), and graph convolutional based pseudo-label generation (GCPL). Table presents the performance improvements each stage. The first row shows the baseline model, modified MinkowskiUNeet 34C Choy et al. (2019). The second row demonstrates that introducing CRP enhances novel class segmentation by capturing causal features. In the third row, replace the causal representation prototypes with prototypes generated by traditional clustering as graph
Table 3: Component Analysis. The four classes represent the novel classes in split 0. The final column, Avg, shows the average mloU for novel classes across four split in the SemanticPOSS.
Method
Split 0
Overall
Baseline
CRP
CRG
GCPL.
Building
Car
Ground
Plants
Avg
Avg
58.6
6.0
44.9
43.7
38.3
24.7
52.4
7.3
52.3
41.6
25,9
56.3
2.6
73.2
49.8
45.5
28.8
/
56.5
7.1
73.1
52.9
47.4
30.1
60.4
8.3
80.8
55.3
51.2
32.5

---

Dvg
Pkone
Prrsa
Deg
Cal
Inage
Imep
IUMS
Figure 5: Comparison of novel class Grad-CAM visualization on the PASCAL-5' dataset.
Figure 6: Comparison of novel class feature map
visualization on the COCO-20' dataset.
nodes, further improving performance. The fourth row combines CRP and CRG, enhancing causal representation capture and causal relationship modeling. Here we generate pseudo-labels for novel classes using Sinkhorn- Linkhorn-Knopp Cuturil (2013). consistent with existing methods, The last row uses GCN to generate pseudo-labels for novel classes, boosting novel class segmentation. A visualization analysis is provided in Figure 4
4.4 Extension for 2D NCD Semantic Segmentation
Comparison with the SOTA method. We extend our method to 2D NCD semantic segmentation. The current SOTA method EUMS Zhao et al. (202I). which lacks the ability to capture causal representations and model the relationship between the base-novel classes effectively as To ensure fairness, we follow the same dataset partitioning strategy and experimental setup. We conducted experiments the PASCAL-5' Shaban al. (017 and COCO-20' Nguyen and Todorovic (2019) datasets. As shown the Table and and Table our method outperforms EUMS in most fold, showing that our causal representation and reasoning approach is equally effective in 2D NCD.
Table 4: Performance on PASCAL-5' dataset.
Table Performance on COCO-20* dataset.
P
COCO-20'
Method
Method
FoldO
Fold1
Fold2
Fold3
Avg
Fold0
Fold!
Fold2
Fold3
Avg
EUMS
69.8
60.1
56.3
50.2
59.1
EUMS
42.39
26.89
19.75
18.19
26,81
Ours
71.9
62.5
57.3
53.1
61.2
Ours
43.23
25.91
20.30
18,56
27.00
Visualization Analysis. Figure 5 presents the Grad-CAM [Selvaraju et al. (2016) visualization results. EUMS generates scattered Teature maps, highlighting vague regions of interest that may miss important causal features. In contrast, our method produces more focused and clearer feature maps, effectively highlighting key causal regions and demonstrating superior causal reasoning capabilities. Figure iillustrates that EUMS results in sparse and dispersed activation regions, failing to capture key causal features. Our approach, however, through causal representation and reasoning, generates more concentrated and clear activations, better identifying causal relationships and significantly improving feature localization and visual accuracy.
5 Conclusion
We introduce a SCM to re-formalize the 3D-NCD problem and propose novel approach called joint learning of causal representation and reasoning. Specifically, first analyze the hidden confounding factors in the base class representations and the causal relationships between the base and novel classes through SCM. Based on this, propose causal representation prototype that captures the causal representation of the base class by eliminating hidden confounding factors. Then, we use a graph to model the causal relationship between the base class causal representation prototype and the novel class prototype, enabling causal reasoning from the base to the novel. Extensive experiments results 3D and 2D NCD semantic segmentation demonstrate the superiorities of our method.
Acknowledgment. This work is supported by the National Nature Science Foundation of China (Nos. 62376186, 62472333).

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Reviews:
[{"review_id": "8RUafs7foL", "reviewer": "Reviewer_KLAY", "summary": "This paper addresses the Novel Class Discovery (NCD) problem in 3D point cloud segmentation, where only labeled base classes are available during training. The authors introduce a causal learning framework to eliminate shortcut features (confounders) and model causal relationships between base and novel classes. The method involves: (1) learning causal representation prototypes via adversarial deconfounding; (2) constructing a graph-based causal reasoning module; and (3) using graph convolutional networks (GCN) to generate pseudo-labels for novel classes. The paper demonstrates superior performance on SemanticKITTI, SemanticPOSS, and also shows 2D extensions on PASCAL-5i and COCO-20i datasets.", "questions": "See Weaknesses", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 4, "originality": 4, "strengths_and_weaknesses": "Strengths:\n+ The integration of SCM and causal representation learning in 3D-NCD is novel and well-justified.\n\n+ The paper provides solid theoretical grounding (e.g., ICM principle, causal DAGs) and detailed loss formulations.\n\n+ Evaluation is thorough across multiple datasets, with clear ablation studies and visualizations. The performance is solid.\n\n\nWeaknesses and questions:\n- The “shortcut features” (U) are not concretely defined or visualized; more intuitive examples or empirical evidence would strengthen the argument.\n\n- While ablations exist, the impact of individual causal constraints (e.g., pruning, direction consistency) could be analyzed in more detail.\n\n- How robust is the causal reasoning framework when base and novel classes have no obvious semantic overlap (e.g., ‘person’ vs. ‘tree’)?\n\n- The choice of MinkowskiUNet as the 3D backbone, while serviceable, is relatively outdated compared to more modern architectures like Point Transformer V3. The authors should justify this decision, and it would be better to give its potential impact on overall performance and generality.\n\n- The paper employs an adversarial loss inspired by GANs to learn confounding factors. However, further explanation is needed regarding why this approach is theoretically sound in the causal learning context. Additionally, could more recent generative models (e.g., diffusion models) offer similar inspirations?\n\n\nMinors:\n- Please spell out \"Directed Acyclic Graph (DAG)\" in full at its first occurrence in the main text to aid clarity for readers unfamiliar with causal graph terminology. Check for other ones as well.\n\n- The font style and formatting in Figures 1 and 2 significantly hinder readability. In particular, the use of italicized text throughout Figure 1 is unnecessary and visually distracting. Meanwhile, the demo point clouds are too small. Clearer typesetting and label formatting would enhance the presentation quality.", "was_revised": true, "final_justification": "The authors' rebuttal has adequately addressed my concerns. I will maintain my rating of Accept.", "rebuttal": "We feel great thanks for your professional review work on our paper. The detailed responses are listed below. \n\n**1.Further Explanation of  “shortcut features” ($U$) :**\n\nWe define the confounding factor $U$ as a shortcut feature statistically correlated with the causal feature $L$. Formally, $U$ is a feature subspace satisfying:\n$$\nU := {u ∈ R^k | u = h(X), Cov(u,L) ≠ 0, I(u; Y|L) = 0}\n$$\nwhere:\n\n- $h: X → R^k$ represents a function mapping point cloud data $X$ to a $k$-dimensional space, capturing spurious correlations (i.e., shortcut features) in the data.\n- $Cov(u,L) ≠ 0$ indicates statistical dependence between the shortcut feature $U$ and the causal feature $L$ (non-zero covariance).\n- $I(·|·)$ denotes conditional mutual information. The condition$I(u; Y|L) = 0$ implies that, given the causal feature $L$, the shortcut feature $u$ contains no additional information about the target variable $Y$. All predictive information about $Y$ is already encoded in $L$, and $u$ provides no new insights.\n\nWe illustrate this with Figure 1 from the paper. The left panel shows how statistically driven models may misclassify novel class (e.g., distinguishing \"stool\" vs. \"chair\"). Here, the causal feature $L$ represents leg count (stools typically have 1 leg, chairs have 4), while the confounding factor $U$ corresponds to shared circular supports. During training, statistical models often exploit surface correlations (e.g., \"presence of circular support\" → \"stool\"), leading to erroneous predictions. The right panel demonstrates causality-aware learning. By identifying the true causal feature $L$ (4 legs), the model performs causal inference and correctly classifies novel instances.\n\nIn causal representation prototype learning, adversarial training aims to eliminate the influence of $U$, forcing the model to focus on $L$ for robust classification. We will supplement the final version with a detailed mathematical characterization of $U$ and intuitive explanations to enhance clarity.\n\n**2.Further Analysis of the Impact of Individual Causal Constraints (Pruning, Direction Consistency)**\n\nWe supplement the following analysis based on our experimental framework, results are averaged across SemanticPOSS splits. \"W/O\" denotes \"without\".\n\n|**Method**|**Novel Class mIoU (%)**|\n|-|-|\n| Full Model (with both constraints) | 32.5 |\n| W/O Causal Pruning | 32.1 |\n| W/O Direction Consistency | 31.8|\n| W/O Both Constraints| 31.4 |\n\nRemoving the causal pruning constraint resulted in a 0.4% performance drop, demonstrating that causal pruning effectively reduces irrelevant information. This technique filters out low-weight causal connections between base and novel classes, allowing the model to maintain focus on valid causal relationships. When the inference direction consistency constraint was removed, mIoU dropped by 0.7%, highlighting the importance of this constraint in ensuring correct information flow from the base to the novel class (B→N) and preventing reverse information leakage. When both constraints were removed, the mIoU decreased by 1.1%, demonstrating their synergistic effect in optimizing the causal graph. Causal pruning narrows the search space for the direction consistency constraint, which ensures proper information flow along retained edges. Together, they more effectively capture reliable causal relationships. We will add this ablation experiment in the final version of the paper.\n\n**3.Robustness of Causal Reasoning under Extreme Novel-Base Dissimilarity**\n\nThe question you raised is extremely crucial. In response to this, we provide answers from the following two aspects:\n\nOn the one hand, we construct a graph model that incorporates causal reasoning to make explicit the high-level semantic causal relationships between base classes and novel classes, enabling novel classes to obtain indirect causal support by associating with multiple base classes. Even though novel classes may appear drastically different from base classes, there often exist some underlying causal factors or interaction relationships in their generation processes (for example, planes, arcs, and cubes, and the relative positions between components), and these geometric units are widely present in different shapes and are more amenable to generalization. In the GCN, base class nodes capture and transmit these underlying causal relationships while aggregating neighbor information, providing causal reasoning support across different classes, thus achieving robust semantic understanding.\n\nOn the other hand, we introduce a causal pruning constraint. When base and novel classes share minimal features, this mechanism prunes weak causal links to prevent interference from base class data. However, this does not indicate a failure of causal reasoning; rather, it reflects the model’s adaptive capability: it transmits reliable causal information when available, while automatically reducing the influence of base classes in the presence of significant interference.\n\nTo further demonstrate this, we propose a new partitioning strategy (Table 15 in the appendix), and split the SemanticPOSS into two splits with low similarity. **Table 16 shows that our method outperforms the next best approach by 15.2% and 14.3%, respectively.** This indicates that even in situations where novel classes are extremely different from base classes, the model is still capable of leveraging meaningful causal information and suppressing noise through the causal pruning mechanism, thus achieving excellent cross-class generalization. \n\nTo provide further clarification, we have included the discussion of this issue in **Section C.4** of the appendix, which you can refer to for additional details. In the camera-ready version, we will incorporate the discussion of this problem into the main text. Thank you again for your valuable comments.\n\n**4. Justification for Backbone Selection and Its Impact on Performance and Generality**\n\nWe chose MinkowskiUNet as the 3D backbone network mainly to ensure a fair comparison with existing SOTA methods in the 3D-NCD field, such as NOPS and DASL, which all use this architecture as the benchmark.  Using MinkowskiUNet, our method outperforms existing approaches on the SemanticPOSS and SemanticKITTI datasets, demonstrating the effectiveness of our causal framework, which is independent of the backbone network.\n\nOur causal learning framework is orthogonal to the backbone architecture. Its core components (causal representation prototype learning, graph-based causal reasoning, and GCN pseudo-label generation) are independent of MinkowskiUNet and can be easily plugged into modern architectures like Point Transformer V3.\n\nExperiments on the SemanticPOSS dataset with Point Transformer V3 as the backbone show that our causal framework consistently improves performance by **6.3%-7.8%** over baseline models without causal mechanisms, demonstrating the approach’s generalizability across different backbone networks. We will add this experiment in the final version of the paper.\n\n| **Backbone** | **Causal Framework** | **Novel Class mIoU (%)** |\n|- |-| - |\n| MinkowskiUNet        | ✗| 24.7 |\n| Point Transformer V3 | ✗   | 26.9 |\n| MinkowskiUNet        | ✓   | 32.5|\n| Point Transformer V3 | ✓ | 33.2 |\n\n**5.Theoretical Rationale for Adversarial Loss in Causal Learning and Insights from Recent Generative Models**\n\nThe adversarial loss used in this paper is based on the Independent Causal Mechanism (ICM) principle, which states that the causal mechanisms generating variables should be independent of other mechanisms (such as those generating confounders). To satisfy this principle, we need to learn a point cloud representation $Z$ that is independent of the confounder $U$ (i.e., $Z \\perp U$), which aligns with the goal of minimizing the mutual information $I(Z;U)$. Inspired by GANs, the adversarial process (the minimax game between the feature extractor $f_\\theta$ and the adversarial network $g_\\phi$) approximates this goal: $f_\\theta$ aims to extract causal features while suppressing signals related to $U$, and $g_\\phi$ attempts to recover $U$ from $Z$. If $g_\\phi$ cannot recover $U$, it indicates that $Z$ has effectively removed the interference from the confounder, adhering to the ICM principle. This process mirrors the logic of causal intervention (via the $do(·)$ operator), removing spurious correlations and separating the true causal relationship between features and classes. We will add detailed explanations in the final version of the paper.\n\nDiffusion models simulate the data generation process by gradually adding and removing noise. This fine-grained characterization of data distributions may offer advantages in separating causal factors from confounders. For instance, the denoising process can be likened to a progressive causal intervention. This intervention potentially enables a more detailed removal of confounding information, thereby yielding purer causal features. However, diffusion models typically require substantial computational resources and longer training times, which could pose efficiency challenges when applied to large-scale 3D point cloud data. Overall, recent generative models have provided new insights for causal learning, and we plan to explore this further in future work.\n\n**6. Response to Minors**\n\nThank you for your suggestions. We will spell out \"Directed Acyclic Graph (DAG)\" in full the first time it appears in the text and check other similar terms. Additionally, we will optimize the font style and layout of Figures 1 and 2, remove unnecessary italicized text from Figure 1, and enlarge the point cloud demonstration to improve readability and presentation."}, {"review_id": "O78MFAUydS", "reviewer": "Reviewer_wnpo", "summary": "This paper addresses the problem of Novel Class Discovery (NCD) for 3D point cloud segmentation. A causality-driven framework is proposed to jointly learn causal representations and reasoning paths. Specifically, it eliminates confounding factors via adversarial training to extract causal prototypes for base classes, and constructs a causal reasoning graph to infer novel class prototypes. A GCN is then used to propagate semantic information and generate pseudo-labels. The method achieves strong performance on 3D LiDAR point cloud benchmarks and 2D NCD semantic segmentation datasets.", "questions": "Please also refer to the weaknesses part for more details. My main questions include:\n\nCan the authors provide more interpretation of the confounding factor U to clarify its role in the model?\n\nWhether the base class prototypes are sufficiently diverse and well-separated to support reliable base-to-novel causal reasoning?\n\nHave the authors considered testing on indoor 3D benchmarks such as S3DIS or ScanNet? Given the 3D-specific nature of the approach, evaluating on diverse 3D scenarios seems more critical than demonstrating generalization to 2D tasks. Since indoor 3D point clouds are highly relevant for applications in robotic perception.", "limitations": "Yes", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\nCausal Learning for Zero-Shot 3D Segmentation. This paper introduces a new causal representation learning paradigm into the 3D point cloud semantic segmentation task, using structural causal modeling and adversarial deconfounding to extract semantically aligned prototypes and improve generalization from base to novel classes. The approach is built on solid theoretical foundations and is shown to be practically effective through well-integrated model design and empirical results.\n\nGood Transferability Across Modalities. The proposed method generalizes well to 2D novel class discovery tasks (e.g., PASCAL-5i, COCO-20i), demonstrating its strong transferability across modalities without major architectural changes.\n\nComprehensive Comparisons. The approach is extensively validated on both 3D (SemanticKITTI, SemanticPOSS) and 2D benchmarks, with detailed quantitative comparisons and clear visualizations showing superior zero-shot segmentation quality over SOTA baselines.\n\nWeaknesses:\n\nConfounder Representation U is Implicit. The proposed confounding factor U is handled through adversarial training strategy, but it lacks interpretability or visual validation. The role and nature of the confounder representation U remain abstract for me.\n\nLack of analysis on base class prototypes. Since the graph nodes are constructed from base class prototypes, a lack of diversity or clear separation among them would make it difficult to establish reliable causal paths from base to novel classes. Thus one underlying assumption of the method is that the base classes are sufficiently diverse and semantically distinct to support meaningful causal reasoning. This is not explicitly discussed or analyzed, but is critical for the effectiveness of the causal graph and prototype transfer.\n\nLimited Evaluation Scope. Although the method performs strongly on outdoor LiDAR datasets (SemanticKITTI, SemanticPOSS), its generalizability to indoor 3D point clouds (e.g., S3DIS, ScanNet) remains unexplored, which limits claims about broader applicability.", "was_revised": true, "final_justification": "As my concerns are addressed in authors' response, I raise my rating to 5 Accept.", "rebuttal": "We feel great thanks for your professional review work on our paper. The detailed responses are listed below.\n\n**1.Further Explanation of  “confounding factor” ($U$) :**\n\nWe define the confounding factor $U$ as a shortcut feature statistically correlated with the causal feature $L$. Formally, $U$ is a feature subspace satisfying:\n$$\nU := {u ∈ R^k | u = h(X), Cov(u,L) ≠ 0, I(u; Y|L) = 0}\n$$\nwhere:\n\n- $h: X → R^k$ represents a function mapping point cloud data $X$ to a $k$-dimensional space, capturing spurious correlations (i.e., shortcut features) in the data.\n- $Cov(u,L) ≠ 0$ indicates statistical dependence between the shortcut feature $U$ and the causal feature $L$ (non-zero covariance).\n- $I(·|·)$ denotes conditional mutual information. The condition$I(u; Y|L) = 0$ implies that, given the causal feature $L$, the shortcut feature $u$ contains no additional information about the target variable $Y$. All predictive information about $Y$ is already encoded in $L$, and $u$ provides no new insights.\n\nWe illustrate this with Figure 1 from the paper. The left panel shows how statistically driven models may misclassify novel class (e.g., distinguishing \"stool\" vs. \"chair\"). Here, the causal feature $L$ represents leg count (stools typically have 1 leg, chairs have 4), while the confounding factor $U$ corresponds to shared circular supports. During training, statistical models often exploit surface correlations (e.g., \"presence of circular support\" → \"stool\"), leading to erroneous predictions. The right panel demonstrates causality-aware learning. By identifying the true causal feature $L$ (4 legs), the model performs causal inference and correctly classifies novel instances.\n\nIn causal representation prototype learning, adversarial training aims to eliminate the influence of $U$, forcing the model to focus on $L$ for robust classification. We will supplement the final version with a detailed mathematical characterization of $U$ and intuitive explanations to enhance clarity.\n\n**2.Separability and Diversity of Base Class Prototypes**\n\nThank you for the valuable reviews regarding the diversity and separability of the prototype classes. This is crucial for the reliability of causal paths in our graphical model.\n\n**(1) Separability**: First, the experimental results in Table 6 (Effect of causal representation prototype number on model performance) provide evidence for the strong separability of class prototypes. The table shows that when each class corresponds to a single prototype, the model achieves the highest average mIoU across splits (45.3). Increasing the number of prototypes per class leads to a continuous decline in performance. As mentioned in lines 506-507 of the original text, “Multiple prototypes increase model complexity, causing clustering distributions in feature space to become less tight, thereby reducing performance.” This suggests that one prototype per class is sufficient to capture its essential characteristics, and the classes themselves have enough semantic distinction to ensure clear separation between prototypes.\n\nTo further visually demonstrate the separability of class prototypes, we have added t-SNE visualizations of the prototypes. These visualizations show clear clustering with good separation between prototypes from different classes in feature space, confirming their diversity and separability. We will add the t-SNE results in the final version of the paper.\n\nAdditionally, our causal representation learning mechanism inherently enhances the separability of class prototypes. By incorporating Structural Causal Models (SCM) and causal prototype learning, we eliminate confounding factors (such as shortcut features), extracting the pure causal representation of each class. This causal mechanism ensures that prototypes accurately reflect the intrinsic features of each class and amplify the differences between prototypes from different classes, thus enhancing their separability. Even when classes are initially ambiguous, the causal representation learning process effectively decouples their inherent features, ensuring reliable causal inference from classes to new ones.\n\n**(2) Diversity**: From a methodological perspective, our causal representation prototype learning eliminates confounding factors and focuses on capturing the intrinsic causal features of each class. These causal features are directly related to the core semantics of the classes, and the core semantics of different classes naturally exhibit diversity (e.g., the fundamental structural and functional differences between “car” and “pedestrian”). Through adversarial training and prototype matching loss, the model actively amplifies the differences in causal features between classes, ensuring that each class prototype focuses on its unique intrinsic attributes, thereby forming diverse representations.\n\nFrom the experimental results, the “single prototype optimal” phenomenon in Table 6 indirectly reflects the diversity of class prototypes. If the class prototypes lack diversity (i.e., prototypes of different classes share similar features), increasing the number of prototypes could have improved performance by optimizing the features. However, the actual result is a decline in performance. This suggests that a single prototype sufficiently represents the uniqueness of each class, and the prototypes of different classes cover distinct regions in feature space, without feature overlap leading to “homogenization.” This “distinct focus for each prototype” in feature distribution is a direct manifestation of diversity.\n\nWe will update the above discussion and analysis in the camera-ready version of the paper. Again, we appreciate your valuable feedback.\n\n**3.Test on indoor 3D benchmarks**\n\nWe acknowledge the importance of indoor 3D point cloud datasets (e.g., S3DIS, ScanNet) in fields like robot perception. Our current work focuses on outdoor LiDAR datasets (e.g., SemanticKITTI, SemanticPOSS), as the initial motivation of our method was geared towards addressing the novel class discovery requirements in outdoor autonomous driving scenarios. However, we fully agree with the reviewer that the generalizability of 3D methods needs cross-scenario validation, as the unique characteristics of indoor environments indeed present different challenges for causal representation learning and inference.\n\nTherefore, we conducted experiments on S3DIS following SNOPS[1] with the same dataset split. The results show that our method performs comparably to SNOPS in indoor scenes, with performance improvements in most splits. This demonstrates that, even in the complex environments unique to indoor scenes, our causal representation prototype learning effectively eliminates confounding factors. Causal reasoning graphs also model the intrinsic relationships between base and novel classes, supporting reliable novel class discovery. Detailed results will be presented in the final version.\n\n|  Split   | Method | Novel | Known |  All  |\n| :------: | :----: | :---: | :---: | :---: |\n| S3DIS-4⁰ | SNOPS  | 55.94 | 33.12 | 40.14 |\n|          |  Ours  | 54.46 | 34.05 | 40.33 |\n| S3DIS-3¹ | SNOPS  | 53.49 | 43.49 | 45.79 |\n|          |  Ours  | 53.78 | 44.27 | 46.46 |\n| S3DIS-3² | SNOPS  | 15.51 | 50.99 | 42.80 |\n|          |  Ours  | 14.04 | 51.36 | 42.75 |\n| S3DIS-3³ | SNOPS  | 11.24 | 55.23 | 45.08 |\n|          |  Ours  | 12.87 | 57.09 | 46.89 |\n\n[1]Riz, L., Saltori, C., Wang, Y. *et al.* Novel Class Discovery Meets Foundation Models for 3D Semantic Segmentation. *Int J Comput Vis* **133**, 527–548 (2025). https://doi.org/10.1007/s11263-024-02180-x"}, {"review_id": "7KCZpesXAI", "reviewer": "Reviewer_DFpS", "summary": "This paper addresses novel class discovery (NCD) for 3D point cloud segmentation. The authors introduce a causality-based framework that jointly learns causal representations by removing confounding factors and performs causal reasoning to infer novel classes. The method involves causal prototype learning and a causal reasoning graph. The approach shows strong performance on both 3D and 2D benchmarks.", "questions": "1. Providing visualizations and analysis of failure cases will further improve the paper’s quality and offer insights for future research directions.\n2. Please refer to the weakness part and address the concerns there.", "limitations": "yes", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\n+ The paper is well-written and well-organized, with clear motivations and effective presentation of the overall framework.\n+ The presented framework is the first method to incorporate structural causal modeling into 3D NCD, offering new insights to the domain. The proposed modules are reasonable.\n+ The proposed method achieves strong performance compared to prior approaches, as demonstrated on multiple 3D and 2D NCD benchmarks. And the ablation studies are also sufficient and detailed. \n\nWeaknesses:\n- The effectiveness of the causal reasoning graph relies on the existence of meaningful causal relationships between base and novel classes. However, such assumptions may not hold for arbitrary class splits. A discussion or analysis on how sensitive the method is to such assumptions would strengthen the paper. \n- Missing related work. Several recent works on 3D segmentation are highly relevant and should be cited:\n   - Graph Regulation Network for Point Cloud Segmentation (TPAMI 2024)\n   - Multimodality Helps Few-shot 3D Point Cloud Semantic Segmentation (ICLR 2025)", "was_revised": true, "final_justification": "My concerns have been addressed. I would increase my current rating accordingly.", "rebuttal": "We feel great thanks for your professional review work on our paper. The detailed responses are listed below.\n\n**1.Robustness of Causal Reasoning under Extreme Novel-Base Dissimilarity**\n\nThe question you raised is extremely crucial. In response to this, we provide answers from the following two aspects:\n\nOn the one hand, we construct a graph model that incorporates causal reasoning to make explicit the high-level semantic causal relationships between base classes and novel classes, enabling novel classes to obtain indirect causal support by associating with multiple base classes. Even though novel classes may appear drastically different from base classes, there often exist some underlying causal factors or interaction relationships in their generation processes (for example, planes, arcs, and cubes, and the relative positions between components), and these geometric units are widely present in different shapes and are more amenable to generalization. In the GCN, base class nodes capture and transmit these underlying causal relationships while aggregating neighbor information, providing causal reasoning support across different classes, thus achieving robust semantic understanding.\n\nOn the other hand, we introduce a causal pruning constraint. When base and novel classes share minimal features, this mechanism prunes weak causal links to prevent interference from base class data. However, this does not indicate a failure of causal reasoning; rather, it reflects the model’s adaptive capability: it transmits reliable causal information when available, while automatically reducing the influence of base classes in the presence of significant interference.\n\nTo further demonstrate this, we propose a new partitioning strategy (Table 15 in the appendix), and split the SemanticPOSS into two splits with low similarity. **Table 16 shows that our method outperforms the next best approach by 15.2% and 14.3%, respectively.** This indicates that even in situations where novel classes are extremely different from base classes, the model is still capable of leveraging meaningful causal information and suppressing noise through the causal pruning mechanism, thus achieving excellent cross-class generalization.\n\nTo provide further clarification, we have included the discussion of this issue in **Section C.4** of the appendix, which you can refer to for additional details. In the camera-ready version, we will incorporate the discussion of this problem into the main text. Thank you again for your valuable comments. \n\n**2.Missing Related Work**\n\nThank you for pointing out the omission in the related work. We take this comment seriously and have carefully reviewed the two papers you mentioned. In the revised version, we will add citations to \"Graph Regulation Network for Point Cloud Segmentation\" (TPAMI 2024) and \"Multimodality Helps Few-shot 3D Point Cloud Semantic Segmentation\" (ICLR 2025), and discuss them in the \"Related Work\" section.\n\n**3. Visualizations and Analysis of Failure Cases**\n\nThank you for the valuable suggestions from the reviewers. We fully agree with the importance of analyzing failure cases and adding visualization results to improve the quality of the paper and provide direction for future research.\n\nIn the revised version, we will add a section analyzing failure cases, specifically including typical samples where the model performs poorly in the SemanticKITTI, SemanticPOSS, and S3DIS datasets (such as semantic ambiguity in new-class and base-class boundary areas, point cloud scenes with severe occlusion, etc.). Through visual comparison of the model's predicted results and the ground truth labels, combined with base-class prototype feature distributions and the edge weight heatmaps of causal inference graphs, we will analyze the reasons for failure — for example, when the causal relationship between new and base classes is weak and there are highly similar confounding features, causal pruning may mistakenly remove valid edges, or prototype matching may be biased.\n\nThese analyses will help clarify the limitations of the method in complex scenarios, such as insufficient ability to distinguish between extremely similar categories, and provide specific directions for future research. Relevant visualization results and detailed analyses will be included in the “Experiments” section of the revised version."}, {"review_id": "lFDFIFFBC0", "reviewer": "Reviewer_T4LH", "summary": "This paper introduces a novel approach for Novel Class Discovery (NCD) in 3D point cloud segmentation. The proposed method uses adversarial training to eliminate confounding factors (shortcut features) in base class representations, extracting causal prototypes that generalize better to novel classes. The causal relationships between base and novel classes are modeled via a graph structure. Causal pruning and direction consistency constraints are applied to refine pseudo-label generation. Experiments on SemanticKITTI and SemanticPOSS show that the proposed method achieves state-of-the-art performance, particularly in handling long-tailed distributions and low-similarity novel classes. The method also generalizes to 2D-NCD tasks.", "questions": "Are the inferred causal relationships (e.g., B→N) always valid, or could they introduce bias?", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1. This is the first attempt to integrate structural causal models (SCMs) into 3D-NCD, addressing spurious correlations in point cloud data.\n2. The proposed method outperforms comparative methods in imbalanced and low-similarity scenarios.\n3. The proposed method can be extended to 2D-NCD and demonstrate broad applicability.\n\nWeaknesses:\n1. The proposed method requires predefining the number of novel classes, limiting adaptability in dynamic open-world scenarios.\n2. This paper lacks a detailed analysis of experimental results. For example, in split 2 of Table 1, SNOPS outperforms the proposed method on novel-class segmentation accuracy by a large margin.", "was_revised": true, "final_justification": "The authors have addressed my concerns in rebuttal.", "rebuttal": "We feel great thanks for your professional review work on our paper. The detailed responses are listed below.\n\n**1.Response to the Comment on the Limitation of Predefining the Number of Novel Classes**\n\nThank you for pointing out this limitation. We fully agree that predefining the number of novel classes does restrict the method's adaptability in dynamic open-world scenarios, which is an important direction for our future improvements.\n\nAs mentioned in the \"Limitation\" section of the paper, novel classes in real-world scenarios often emerge dynamically, making it difficult to predetermine their quantity. To address this, we plan to integrate incremental learning strategies with the existing causal framework (similar to the approach proposed by Roy et al. in 2D NCD). This will enable the model to update causal representation prototypes and the structure of the reasoning graph in real-time when novel classes appear, without the need to predefine the number of classes. Specifically, potential new classes can be automatically identified by dynamically detecting new clusters in the feature space, and temporary prototypes can be assigned to them based on causal reasoning mechanisms. These prototypes will then be gradually optimized through online learning, thereby enhancing the ability to quickly adapt to unknown classes. This improvement aims to build a more flexible and robust novel class discovery framework for autonomous driving and embodied intelligent systems, and relevant explorations will be further carried out and validated in subsequent research.\n\n**2.A Detailed Analysis of Experimental Results**\n\nThank you for your attention to the detailed analysis of the experimental results, particularly for pointing out the comparison of novel-class segmentation accuracy in Split 2 of Table 1. Based on the specific data of Split 2 in the SemanticPOSS dataset, we supplement the following analysis:\n\nAs shown in Table 1, in Split 2, SNOPS leads temporarily with a score of 16.9% for the 'Novel' metric, while our method scores 13.6%. This difference is mainly related to the characteristics of specific novel classes in Split 2: the novel classes in Split 2 include \"traffic-sign\" and \"trunk\", which have small sample sizes. Among them, \"traffic-sign\" in point cloud data often has sparse features due to its small scale and variable morphology. In such scenarios, SNOPS, which is based on statistical clustering, may gain short-term advantages by capturing local appearance similarities (e.g., the common rod-like structure between traffic-sign and the base class \"pole\").\n\nHowever, from the overall performance, our method still outperforms SNOPS in the \"Known\" and \"All\" metrics (**56.1% vs 44.5% for known classes, 46.3% vs 38.1% for all classes**). \nEspecially for base classes such as \"car\" and \"plants\", through causal representation learning on base classes, we eliminate confounding factors like light/shadow and occlusion, leading to significantly improved segmentation accuracy. For example, the 'car' class achieves a score of 52.6%, which is significantly higher than SNOPS's score of 28.0%.\nThis indicates that our method has greater advantages in modeling the essential features of base classes and supporting the stability of overall segmentation.\n\nThe relatively weaker performance on novel classes such as \"traffic-sign\" and \"trunk\" reflects that there is still room for optimization in our method regarding causal feature extraction for extremely sparse samples. In future work, we will introduce domain adaptation techniques to transfer knowledge to target small-sample novel classes, thereby enhancing the adaptability to small-sample novel classes. The above analysis will be integrated into the \"Experimental Results and Discussion\" section of the revised manuscript, and we will also add detailed analyses of other experimental parts.\n\n**3. Are the inferred causal relationships (e.g., B→N) always valid, or could they introduce bias?**\n\nWe appreciate the reviewer for raising this important question. Our causal reasoning ($B \\to N$) leverages the semantic similarity between base classes and novel classes to enhance the model's ability to discover novel classes. The inferred causal relationship ($B \\to N$) is not absolutely valid in all scenarios (e.g., when novel classes are highly dissimilar from base classes). However, we have ensured its reliability through the following mechanisms and also taken corresponding measures to mitigate potential biases:\n\nOn the one hand, we construct a graph model that incorporates causal reasoning to make explicit the high-level semantic causal relationships between base and novel classes, enabling novel classes to obtain indirect causal support by associating with multiple base classes. Even though novel classes may appear drastically different from base classes, there often exist some underlying causal factors or interaction relationships in their generation processes (for example, planes, arcs, and cubes, and the relative positions between components), and these geometric units are widely present in different shapes and are more amenable to generalization. In the GCN, base class nodes capture and transmit these underlying causal relationships while aggregating neighbor information, providing causal reasoning support across different classes, thus achieving robust semantic understanding.\n\nOn the other hand, we introduce a causal pruning constraint. When base and novel classes share minimal features, this mechanism prunes weak causal links to prevent interference from base class data. However, this does not indicate a failure of causal reasoning; rather, it reflects the model’s adaptive capability: it transmits reliable causal information when available, while automatically reducing the influence of base classes in the \n\nNevertheless, in extreme cases, the inference of causal relationships may introduce certain biases. To address this, we verified the robustness of the model in low-similarity scenarios through experiments. We propose a new partitioning strategy (Table 15 in the appendix) and split SemanticPOSS into two subsets with low similarity. **As shown in Table 16, our method improved the mIoU for novel classes by 15.2% and 14.3% respectively compared to the next best approach.** This indicates that even when novel classes are extremely different from base classes, the model can still leverage meaningful causal information and suppress noise through the causal pruning mechanism, thereby keeping the bias at a low level.\n\nTo provide further clarification, we have included the discussion of this issue in **Section C.4** of the appendix, which you can refer to for additional details. In the camera-ready version, we will incorporate the discussion of this problem into the main text. Thank you again for your valuable comments."}]

Author rebuttal:

Meta-review: This paper introduces a novel causal learning framework for 3D Novel Class Discovery (NCD) in point cloud segmentation, integrating structural causal modeling (SCM) and adversarial deconfounding to extract causal prototypes and reason about novel classes. Reviewers found the approach theoretically grounded, methodologically sound, and empirically strong, demonstrating state-of-the-art performance on both 3D (e.g., SemanticKITTI, SemanticPOSS, S3DIS) and 2D benchmarks. The authors’ rebuttal effectively addressed key concerns, including: Providing additional ablation studies and visualizations (e.g., t-SNE plots) to demonstrate prototype separability and the impact of causal constraints; Extending evaluation to indoor 3D datasets (S3DIS) to strengthen claims of generalizability; Addressing limitations such as the need to predefine novel class count and potential bias in causal reasoning.

All four reviewers raised their ratings to Accept following the rebuttal, acknowledging the authors' thorough responses and commitments to enhance the final version. The work is recognized as the first to integrate SCM into 3D-NCD, offering both theoretical and practical contributions to the field. Minor revisions—such as improved figure readability, expanded related work, and failure case analysis—are expected in the camera-ready version.

Based on the reviews, rebuttal, and discussions among the reviewers and ACs, the meta-reviewer recommends an Accept for this submission. This paper represents a timely and impactful contribution to causal representation learning in 3D vision, with clear potential for broader applicability.
Decision: Accept (poster)