GOOD: Geometry-guided Out-of-Distribution Modeling for Open-set Test-time Adaptation in Point Cloud Semantic Segmentation
Authors: Tianpei Zou
Guo Yu
Ya Wu
Fan Lu
Zhongcong Xu
Zhang Bo
Ziqiao Wang
Sanqing Qu
Guang Chen
Venue: ICLR 2026 Poster (2026)
Keywords: Open-set Semantic Segmentation
Online Domain Adaptation
Point Cloud Segmentation
Primary area: transfer learning, meta learning, and lifelong learning

Abstract: Open-set Test-time Adaptation (OSTTA) has been introduced to address the challenges of both online model optimization and open-set recognition. Despite the demonstrated success of OSTTA methodologies in 2D image recognition, their application to 3D point cloud semantic segmentation is still hindered by the complexities of point cloud data, particularly the imbalance between known (in-distribution, ID) and unknown (out-of-distribution, OOD) data, where known samples dominate and unknown instances are often sparse or even absent. In this paper, we propose a simple yet effective strategy, termed Geometry-guided Out-of-Distribution Modeling (GOOD), specifically designed to address OSTTA for 3D point cloud semantic segmentation. Technically, we first leverage geometric priors to cluster the point cloud into superpoints, thereby mitigating the numerical disparity between individual points and providing a more structured data representation. Then, we introduce a novel confidence metric to effectively distinguish between known and unknown superpoints. Additionally, prototype-based representations are integrated to enhance the discrimination between ID and OOD regions, facilitating robust segmentation. We validate the efficacy of GOOD across four benchmark datasets. Remarkably, on the Synth4D to SemanticKITTI task, GOOD outperforms HGL by 1.93%, 8.99%, and 7.91% in mIoU, AUROC, and FPR95, respectively.

Published as a conference paper at ICLR 2026
GOOD: GEOMETRY-GUIDED OUT-OF--ISTTIBUTION MODELING FOR OPEN-SET TEST-TIME ADAPTATION IN POINT CLOUD SEMANTIC SEGMENTATION
Tianpei Zou Guo Yu!. Ya Wu', Fan Lu', Zhongcong Xu²,
Zhang Bo², Ziqiao Wang', Sanqing Qu Guang C.
1 Tongji University Shanghai Innovation Institution "Shanghai Westwell Technology Co., Ltd.
4 CNNC Equipment Technology Research (Shanghai) Co., Ltd. ByteDance Seed Corresponding author
ABSTRACT
Open-set Test-time Adaptation (OSTTA) has been introduced to address the chal- lenges of both online model optimization and open-set recognition. Despite the demonstrated success of OSTTA methodologies in 2D image recognition, their application to 3D point cloud semantic segmentation is still hindered by the complexities of point cloud data, particularly the imbalance between known OOD) data, where known samples dominate and unknown instances are often sparse even absent. In this paper, we propose simple yet effective strategy, termed Geometry-guinn of-Distribution Modeling (GOOD), specifically designed to address OSTTA for 3D point cloud semantic segmentation. Technically, we first leverage geometric priors cluster the point cloud into superpoints, thereby mitigating the numerical disparity between individual points and providing a more structured data repre- sentation. Then, introduce novel confidence metric to effectively distinguish between known and unknown superpoints. Additionally, prototype-baset repre- sentations are integrated to enhance the discrimination between ID and OOD re- gions, facilitating robust segmentation. We validate the efficacy GOOD across four benchmark datasets. Remarkably, on the Synth4D to SemanticKITTI task, GOOD outperforms HGL by 1.93%, 8.99% and 7.91 % in mloU, AUROC, and FPR95, respectively.
(in-distribution, ID) and unknown cout-of-distributin
Out-
INTRODUCTION
3D point cloud semantic segmentation crucial for scene understanding in many applications like autonomous driving (Wu et al, 2018 Choy et al.) 2019) and robotics (Balsiger et al. [2019] Liu et al.)
2023). However, deep neural network (DNN)-based) models often experience performance degrada- tion when exposed to unseen or different target distribution than the source training data (Li et al.,
2023a Duan al. 2024). Test-time Adaptation (TTA) has emerged as promising technique to mit- igate these challenges by leveraging unlabeled data to update models dynamically for deployment scenarios. Yet, most TTA methods focus primarily on addressing covariate shifts (distributional differences in known classes), often neglecting semantic shift scenarios in which the target data in- cludes novel categories absent from the training data. This oversight may pose significant risks in security-critical applications, as models may misinterpret or fail to recognize unknown objects, as illustrated in Fig. a) and b).
Recent efforts in 2D image recognition Li et al.) 2023b; Lee et al., 2023 Gao et al. 2024 2023
Yu et al. 2024| [Schlachter & Yang 2024) have introduced Open-set Test-time Adaptation (OSTTA) methods for online model optimization and open-set detection in generalized cases involving out-of- distribution (OOD) categories. However, directly adapting these OSTTA methods to 3D point cloud semantic segmentation suboptimal and challenging. As shown Fig [c) and g), combining TTA methods designed for 3D point cloud segmentation (TTA-3DSeg), such as GIPSO (Saltori et al.
Project leader, supervised the work and defined the conceptualization.

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Published as a conference paper at ICLR 2026
Sueeoli duste
A Known Unknown
GOOD
Caronnd Trath
bJ HGL
1K2. Vudaa 18%
IXL.
GEso-Teens Plers
cioli Impro (51) gi Perfoemance Compassen
dI GUOD
4) Pain--leval methods
f) Siperpometleel GOOD
Figure 1: a) Black pixels indicate the OOD points in the target domain, such as the bicycle and bicyclist. b) The TTA-3DSeg model's (HGL Zou et al. 2024)) overconfidence leads erroneous predictions of dynamic OOD points as stationary vegetation, which could lead to serious safety incidents. c) Directly adapting the existing OSTTA method (UniEnt (Gao et al. 2024 point cloud semantic segmentation would lead to the misclassification of dominant ID points as OOD. d) Our GOOD effectively mitigates the imbalance between ID and OOD points, achieving satisfactory performance in both open-set and closed-set. e) Due to the predominance of ID points, point-level OSTTA methods would misclassify large number of ID points OOD, undermining segmentation performance. f) GOOD addresses this by grouping individual points into geometrically connected superpoints. Darker colors indicate higher uncertainty. g) We compare GOOD with GIPSO (Saltori ct al. 2022), HGL (Zou et al. 2024). + UniEEt (Gao et al. 2024). WCEM (Lee et al. (023).
2022) and HGL (Zou et al. 2024), with 2D OSTTA approaches even results in reduced OOD detec- tion performance compared to using these TTA methods alone. We speculate that this performance drop stems from the reliance of existing OSTTA methods on image instance-level processing; when transferred to 3D point clouds, such methods often fail to capture the geometric priors and spatial interdependencies essential for effective 3D discrimination, leading to limited OOD detection capa- bilities. Furthermore, these methods typically assume the availability of abundant OOD samples to assist in discerning discrepancies between in-distribution (ID) and OOD data. This assumption may not hold in 3D point cloud segmentation, where OOD samples could be very sparse.
In this paper, we delve into the open-set test-time adaptation for 3D point cloud semantic segmen- tation (OSTTA-3DSeg). As shown in Fig. e)) and f), rather than directly identifying imbalance individual points. we propose focusing on geometrically connected superpoints, which can better differentiate ID and OOD objects to relieve the imbalance in the quantity of ID and OOD points. Dut-of-Distribution
To implement this approach, we develop method termed Geometry-guidest (GOOD) modeling. Specifically, GOOD begins by clustering the point cloud into superpoints, ef- fectively transforming the identification task from point-level to superpoint-level. Unlike existing OSTTA methods that purely rely on Gaussian Mixture Modeling (GMM) for pseudo-identifictton of ID and OOD data, we introduce a superpoint ID prototype strategy. This mechanism enhances the identification of ID superpoints, addressing false identifications by GMM, especially when OOD samples are sparse or absent. Furthermore, we incorporate temporal consistency to generate reli- able pseudo-labels for stable and efficient online optimization. Similar to most existing TTA meth- ods (Yuan et al. 2023 Wang et al. 2022). GOOD operates under an exponential moving average (EMA) framework. We validate GOOD across extensive experiments on four benchmarks with varying distribution-shh scenarios, showing that GOOD achieves new state-of-the-as performance.
Our contributions can be summarized as follows:
To the best of our knowledge, we are the first to address open-set test-time adaptation for 3D point cloud semantic segmentation (OSTTA-3DSeg).
We propose Geometry -uiided -iitt---iitiiittionn Modeling (GOOD), an effective method that shifts from peint-level to superpoint-level identification, leading to more robust and coherent segmentation results.
GOOD is designed to be flexibly integrated with existing TTA-3Dseg methods, substan- tially enhancing performance in open-set scenarios.
2 RELATED WORK
Test-time Adaptation: Test-time adaptation (TTA) has gained considerable attention due to its ability to adapt models target data distributions without requiring access source domain data during deployment. Extensive research has explored TTA in the image domain across tasks such
as recognition (Wang et al. 2020 Niu et al. 2022 Chen al. 2022). detection (Kim et al. 2022
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Published as a conference paper at ICLR 2026
VS et al. 2023 Veksler 2023), and segmentation Wann al. 2023 Zhang al. 2022 Recently,
there have been some ettorts (Saltori et al. 2022 Zou et al. 2024; Weijler et al. 2024 Wang al.
2024) made to TTA-3DSeg. GIPSO (Saltori et al. 2022) is the first method tailored for TTA-
generating pseudo-labels for each eategory based on confidence-rankee
proportions and employing additional networks for pseudo-label propagation. HGI. (Zou et al. 2024) introduces local-global pseudo-label strategy to balance pseudo-labe accuracy with computational efficiency. Despite their effectiveness in handling covariate shifts, these methods largely overlook semantic shifts, where novel, unseen categories emerge the target data. Addressing semantic shifts is critical, particularly in safety-critical applications such as autonomous driving, where robust recognition and response to novel objects are essential for reliable performance in the real-world.
OOD Detection: Traditional OOD detection usually enhances model reliability through advanced confidence metrics (Hendrycks & Gimpel 2016; Hendrycck et al. 2019 Hsu et al. 2020: Liang ct al. 2017 Liu et al.] (2020) and strategically integrating auxiliary OOD data (Hendrycks et al.
2018 Katz-Samueka et al.] [0022] Yang et al. 2021; Yu & Aizawa 2019 Zhang al. 2023). These
approaches optimize the model's 's discriminative capability, enabling more precise differentiation be- tween ID and OOD samples. More recently, several approaches (Li et al. [003bb Lee et al. [023 "aang
Gao et al. 2024 2023 Yu et al., 2024, Schlaccterr & Yang) (224) have emerged that optimize net- works using unlabeled target data to enhance OOD detection at test time. While promising, these methods are generally limited 2D images, which loses effectiveness in 3D point cloud segmen- tation, where the predominance of ID samples and the scarcity of OOD samples lead to frequent misclassification of ID points as OOD, compromising model robustness open-world applications. Our work delves into this limitation and targets for proposing dedicated framework for OSTTA- 3DSeg, specifically designed leverage geometric context for more robust segmentation in complex 3D environments.
3 METHODOLOGY
3.1 PROBLEM SETUP AND OVERVIEW
In this paper, we focus on open-set test-time adaptation for 3D point cloud semantic segmen- tation (OSTTA-3DSeg), which aims to adapt the source model Fs online the target model FT without access to the source data during model optimization. In this setting, the target do- main T and source domain S exhibit both covariate shifts (distributional differences in shared classes) and semantic shifts (presence of new, unseen classes in the target domain). Formally, let Ds = {(X;s Yg) No represent the source domain dataset with label space Ys = {I....,(s) and let Dr = {(X}, Y/D) represent the target domain dataset with label space Yr- {1..., CT), where Cs and Cr denote the number of classes in the source and target do- mains, respectively.
traditional closed-set TTA, Cş = Cr, whereas in OSTTA, Cs < Cr. The objective is to accurately segment points from the known (ID) classes C's while rejecting points from the unknown (OOD) classes in CT \ Cs, even amidst substantial ID-OOD imbalance.
Traditional 2D OOD detection methods often treat individual instances in isolation, but for 3D data, spatial coherence and contextual relationships are crucial for accurate detection. To address this, we introduce the Geometry- guided Out-of -fitrribbittttoinnnn Modeling (GOOD) approach, as depicted in afff-rranning strategy based on the mean-teacher EMA framework, where the teacher model Fritea generates pseudo-labels that the student model Fr,etu leverages for model optimization. To realize robust ID-OOD separation and accurate ID segmentation. GOOD incorpo- rates two key branches within the teacher model: the Superpoint Representation Branch for effec- tive detection of OOD points by grouping spatially coherent regions, and the Temporal Pseudo-label Branch to improve IDD category identification by enforeing consistency across sequential frames. These components enable GOOD to capitalize on both temporal and geometric patterns involved in 3D data, enhancing OOD detection and IID segmentation performance.
Fig. 2] GOOD utilizes
3.2 SUPERPOINT REPRESENTATION BRANCH
In applications like autonomous driving, point cloud segmentation models often encounter far fewer unknown (OOD) points compared to known (ID) points, leading significant class imbalance. This imbalance can undermine instance-based methods (Li et al. 2023b Gao et al.] [024] [Schlachter & Yang 2024), resulting in frequent misclassifications. To address this challenge, we propose the
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O
A Saown Clads C Caloowa Cless Superpoinl Cluslering
Dodsion Suusday
Supergoirt Repmsentation
Superpoint ID Proitype
Tertoooal Pseuio-Label
EMA
Sugerpolin
Calggon
Keown
Las Foo:
Temporil Regulorization C..
Edtrogy
Figure 2: Method overview. a) GOOD consists of two branches: the Superpoint Representation Branch and the Temporal Pseudo-label Branch. The Superpoint Representation Branch further in- cludes three key components Superpoont Clustering, Superpoint Confidence, and Superpoint II) Prototypes. Specifically. b) Superpoint Clustering generates superpoints by aggregating multiple frames of point clouds and applying clustering algorithm. Then, c) Superpoint Confidence in- troduces superpoint purity and superpoint entropy as metries to distinguish between ID and OOD superpoint preliminarily. Finally, to address the noise and potential absence of OOD points, we additionally introduce d) Superpoint ID Prototypes to further refine the classification.
Superpoint Representation Branch (SRB), as illustrated in Fig. 2 b), c), and d). The SRB trans- forms discrete individual points into spatially correlated superpoints to mitigate extreme ID-OOD imbalance. Specifically, we first aggregate multiple frames of point clouds and apply non-learning clustering method to generate superpoints, capturing spatial coherence across points. We then define superpoint purity and superpoint entropy to quantify confidence: purity measures the homogeneity of ID or OOD points within each superpoint, while entropy indicates confidence based on distri- butional uncertainty. Based on these metries, Gaussian Mixture Model (GMM) is then employed to differentiate ID and OOD superpoints. Finally, we establish superpoint ID prototypes to further refine the identification of ID superpoints, addressing potential misclassifications by GMM, partic- ularly in cases where OOD samples are sparse or absent.
Superpoint Clustering. We propose heuristic pre-segmentation to divide the point cloud into set of superpoints, with each superpoint demonstrating high purity and typically containing points from only one category. Outdoor LiDAR scans, unlike indoor scenes, often feature well-separated objects, especially after ground points are identified and removed. Inspired by this philosophy, we design an intuitive approach to pre-segment each LiDAR frame, including three steps: 1) Combine temporal frames. Using the ego poses, we align the current and previous frames to produce denser point cloud, which facilitates clustering by improving the continuity of object structures. 2) Remove ground points. To isolate objects from the ground, we apply RANSAC (Fischler & Bolles 1981) to estimate the ground surface. To account for variations in ground slope, we partition the point cloud into smaller sub-regions and apply RANSAC to each sub-region individually. 3) Cluster into superpoints. We apply DBSCAN (Ester et al., 1996) to the remaining point cloud to form superpoints. Its ability to identify clusters of arbitrary shapes without preset cluster number makes it ideal for complex outdoor LiDAR data and improves noise robustness.
Superpoint Confidence. Calculating prediction confidence and using this information for reliable OOD detection is essential in OSTTA. With the intuition that points within known (ID) superpoints exhibit greater geometric and temporal consistency than those within unknown (OOD) superpoints (confirmed in Fig. c) right and Appendix[A.33. Cour to evaluate the internal consisteney of each superpoint. Formally, given set of superpoints S S= {sx)]= and the corresponding softmax predictions P = ] we normalize the label 0 (the e-th element of the one-hot label for point j in superpoint sx) within each superpoint to obtain the probability distribution Pk.c. and then calculate the superpoint purity Charr as:
we first introduce the metric of superpoint purity
(sk, Pie pk +- (B,CAN((E.) NN
RNxxc
0j.c = 1e arg max (DA.eloEC)
.Pke= N JENz
1
Σ $3.00
(1)
C
ChTT= log
1
Σ
Pk.c log Pk.cr
where 1e(-) denotes the one-hot transformation, is the point number in superpoint, C is the num- ber of known classes, and Pr.c. denotes the softmax probability of point in superpoint 88 belonging
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Published as a conference paper at ICLR 2026
to the e-th class. However, superpoint purity alone may be insufficient in cases where the majority of points show only a slight preference for one class over others, making the metrie less reliable. To
address this, we further introduce the superpoint entropy Cent, a soft version of Eq. I that replaces the aarg max(.) term with max(() to better capture distributional uncertainty.
Accordingly, we obtain the final function of superpoint confidence C* Cx -hur, which is empirically validated in [p.endii(A.3.3je achieve a good trade-off' between closed-set and open-set performance. As shown in Fig. 2 c). we empirically observe that the bimodal distribution of C effectively distinguishes ID and OOD superpoints, with two distinct peaks corresponding to each type. Consequently, we model the distribution of Cx using two-component GMM, i.e. ((x). Here, the component with the larger mean represents the ID superpoints, while the component with the smaller mean corresponds to the OOD superpoints.
(xx) = π (x) (x (IDD oD) + (1 - [())) (x |OOD, rdo)):
where Taa) denotes the probability that Cruup belongs to the ID superpoint, MID, MID, o and
MOOD; doD denote the mean and variance of the II) and OOD superpoint, respectively. Although superpoints alleviate the extreme ID-OOD imbalance, IDD superpoints may still be misclassified as OOD. To address this, we discard mixed data in the middle, classifying superpoints below threshold FOOD OOD SOOD and above threshold PiD as ID STD.
Superpoint ID Prototypes. While the GMM based on superpoint confidence enhances ID and OOD discrimination, some limitations remain. Specifically, superpoint confidence relies solely on the classifier's ss logit output, overlooking embedding features of the superpoints. This omission can lead to mixed ID and OOD superpoints. Furthermore, in certain scenarios, OOD samples may be absent. Nevertheless, the G.MM-based partitioning method forces each point cloud frame to contain both ID and OOD superpoints, potentially resulting in the over-segmentation of OOD regions and degrading model performance. Morcover, we observe that while OOD superpoints may be incon- sistently identified, ID superpoints tend to be more stable and reliable, shown in Fig. ()) right. These observations motivate our development of a superpoint ID) prototype strategy to achieve more accurate and robust identification.
Concretely, at each timestep f, we construct a set of prototypes for each ID class based on superpoints SID. For each class we calculate the prototype Pe as the centroid of all ID superpoint embeddings, defined p' 1 EN zi. where N₂ is the number of superpoint of current class and 2 is the mean embedding feature of each superpoint. To stabilize training, we update the prototypes incrementally using EMA: P'. = a (1-x1))))) (1-')p' Empirically, we set EMA coefficient a = 0.99. Then we can evaluate the similarity between pseudo-labeled OOD superpoints and ID prototypes, identifying potential misclassifications among the OOD superpoints. This is formalized as:
OOD, if sim (2ks pt) < $ ID,
(3)
if sim (= (b) 2
where sim(a, b) measures the cosine similarity between and b, T is an empirical threshold. We define YOOD ExoOOD $4 OOD pscudo-label.
In summary, the dedicated SRB reliably separates ID and OOD superpoints, effectively addressing extreme cases where OOD samples are minimal or absent.
3.3 TEMPORAL PSEUDO-LABEL BRANCH
Existing -TA-3DSeg methods (allori et al. 2022 Zou al., 2024) typically assign pseudo-labels for ID classes based solely on single-frame information, overlooking valuable context provided by consecutive frames. To overcome this limitation, we propose effective Temporal Pseudo-labels Branch (TPB) that fully leverages temporal information. Technically, we start by inputting the current point cloud X into the teacher model Fiee generating the softmax prediction p and
one-hot label Yt, Subsequently, we project both the current frame (Xt,'* and the previous frames (X*-1 pt-1) ..... (X* pt-w) into the global coordinate system. Following prior work (Saltori ct al. 2022 Zou et al. 2024), we assume access to reliable self-pose for temporal alignment; if this assumption is violated (i.e., the pose noisy), performance may degrade without targeted ad- justments. Further analyses of noise effects, alternative solutions, limitations of this assumption, and verification details are provided in the Appendix A.3.6 For each point of in the X', we then leverage K-NN to identify temporal neighborhoods Ne;" Nef-u from each preceding frame.
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Published as a conference paper at ICLR 2026
Table 1: Results of different methods on Synth4D to SemanticKITTI. indicates that larger values
better, and vice versa.
Pedestien
Sidewolk
Terrain
Monmade
Vogeladivn
roloU111
AUROCTI
FPHNEL1
Seanor
51.05
15:56
13.52
22:14
16.33
10.01
-0076
08.80
76.5
BN Nacoztali Nooa
-0.0
1.15
-5.3%
+6.35
+10.03
+2199
64. 16.16
79:00
-1.0
+1.6
Conjeptzee
15.76
130.30
11.30
GIPSO Salanre al 1022
+10.57
-6.22
-0.25
5.26
+3.36
-3.31
+2.03
+220
64.47
$0.40
+Ser7
-.54
-0.54
-3.4
+8.56
-3.0
+1.1
16.00
05.01
Daiba
Elar
+2.51
+3.9
+117
16000
HGL. 2ou stdl. (024
+80.13
-2.33
-0.00
-0.37
+3.50
-5.07
-2.7
+250
64.40
70.82
+S677
64.07
.790
-3.29
+0.30
+7.48
+1.43
-IKGMM
+12.34
+3.38
+6000
+13.5
0.02
-1.9
-0.4
45.23
+9.93
+5.55
+5.31
75.31
71.31
+0.01
Ne for 2 then calculated as the aggregated label from its neighboring The neighborhood label y points in previous frames:
st.Ne 1. (arg max
Σ
PrLeeC).
NENe Nf..Ne--"
Next, assign ID pseudo-labels YID only points where the predictions are consistent between the current pseudo-label and its temporal neighborhood pseudo-label:
v/ s.t. 11. = N...N.
N.Ne
(5)
Finally, we remove overlapping regions of FOOD from YID, resulting in a filtered ID pseudo-label.
Notably, the EMA-based teacher-student architecture in TPB not only explicitly enforces temporal consistency within the point cloud data but also implicitly promotes temporal stability within the model itself, thereby enhancing both accuracy and robustness. Additionally, it does not require extra threshold hyperparameters to balance precision and generalization.
3.4 TEMPORAL-BASED FEATURE REGULARIZATIO
Following (Chen & He 2021 Saltori et al. 2022 Zou et al, 2024), we also introduce tempo- ral regularization enforce consistency of projected features between corresponding points across consecutive frames:
z
1 D (g', 2"',2t-)== + D (g-
Di-ttw (d',z--)) (/==))==
- Freg
|1 112
where q' and --- are the projected feature of corresponding points in frames t and t w, generated by an additional encoder network h(-) and predictor head f(-). respectively. The temporal consis- tency loss Creg is defined the average bidirectional feature distance between frames. Please refer to Appendix A.2. for more details.
3.5 TEST-TIME MODEL ADAPTATION
Given the class imbalance in point clouds and to prevent confusion between unknown and known points, we adopt the Dice loss Ldice as the ID pscudo-label learning objective. Additionally, we introduce entropy-based OOD loss, Lood
Toooll ExeSoon H atr))) to supervise pseudo-labeled OOD superpoints, where H.)) denotes the entropy. The overall test-time optimiza- tion objective for the student model is defined as finat Ldioe + Lreg + XLood, where À is trade-off hyperparameter. During the iterative training process, the teacher model is updated indi- rectly by the student model using the EMA strategy.
4 EXPERIMENTS
4.1 SETUP
Source and Target Datasets: Following previous studies, we evaluate our method on the widely
used Lidar benchmark datasets: Synth4D (Saltori et al.] (022), SynLiDAR (Xiao et al., 2021).
SemanticKITTI (Behley et al. 2019) and nuScenes (Caesar et al. 2020 The source model is

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Published as a conference paper at ICLR 2026
Table 2: Results of different methods on Synth4D to nuScenes.
Model
Vehidl
Pedestrian
Road
Sidewalk
Terrain
Manmade
Vegetation
InleL111
AUROCITI
EPRMELL
08.00
10.09
76.97
2001
POAD
10.25
10.57
-0.1
-062
10.69
10.50
10.55
11.05
1966
61.11
76.71
Salam
+048
38.56
Mog
-1.55
-083
-3.00
-004
ICCEM
12 20220
12.43
-1.24
-002
+1.00
44:12
-091
11.01
$7.50
$5.51
+5000
4.15
-0.37
-0.44
+1.47
13.59
40.35
05.23
+3.67
62.95
73.25
+Nee7
-1.01
-010
0015
79.15
+DilEat
No0
-1.50
-460
-4.6
-0.74
+8.58
+3.00
+2.4
0.30
57.42
$1.00
-ICLM
12 2rerrh 20226
+2.83
-3.9
-1.99
-0.11
+5:30
-0.30
-243
+0.66
57.14
$4.77
+0000
-1.04
+0.11
-3.7
+1.85
43.53
+2.07
+2.62
62.30
G006
-0.1
-021
-308
+1.79
+3.35
-1.33
-5.3
+2.33
64.00
72.00
Table 3: Results of different methods on SynLiDAR to SemanticKITTL.
Siergr
55.13
382
16.77
17.40
16.33
31
74.14
10.53
002
3600
567
63.0
15.33
28.50
13,5
50
2655
56.00
$6667
11 1400
-2.61
+0.30
+2602
+2.17
1.33
4003
2.59
+0.0
-181
+162
$1.15
205
66.15
+1.55
+2.65
19.34
12.68
16.19
-1.30
1.99
3.11
3.99
+0.00
112.1
4.33
4862
1362
5.59
-33
+2.50
+1.38
00.11
$009
GUPS0
Saled
+4.44
4.23
-1.14
+2.55
-220
-.22
488
40.55
+564
+3.24
+042
-3.8
+1.70
+1.45
44.11
+4.00
+229
+3.00
+1.46
57.55
$290
06.51
4.00
+5.35
3.55
4.77
3.57
-510
+1
+005
-305
13
-1.1
+8.3
-1.1
+3.0
56.2
-NEEM
44.23
4.25
-1.33
+2.38
1260
482
4041
41.00
+5.99
+3.5
+040
-3.93
+1.00
+1.54
+306
45.33
+3.0
+422
+1.02
62.57
1060
-0010
+17 16
40.33
+4.34
+M
0.15
335
+5.0
66.65
+6.9
+3.1
407
+16.1
15
+6.35
+1.6
0.2
+5.27
71.00
71.66
HGL 2ew 2014
110.50
4.40
-.34
17.55
2.99
3.3
18.20
11:43
551
13:00
1000
1:3.2
12.50
11.0
16.04
14.55
15.75
12.54
41.50
02.91
75.1
10:85
1.16
-285
17.05
3.50
331
4.52
2.31
4437
13.2
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105 86
12.55
1445
14.4%
14.45
15.99
1414
11.08
00.10
1942
NCEM
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113.7
0.31
3.46
18,50
1.54
4.55
15.1
00.00
4.33
1412
1645
12.00
(44)
16.11
15.14
13.14
12.35
71.05
1060
-00010
+1.00
40.0
+341
+31.14
+1.0
+0.00
+55.3
+6.99
+195
4.32
+1.0
+0.3
72.00
6K3
GD0D
+17.7
4030
+3.3
+34.5
+1.59
2M
+6,87
-1.2
+3.05
4003
+37.81
+1.40
+6.35
+2.96
1131
+7.15
4033
73.33
69/96
pre-trained on virtual datasets (Synth4D or SynLiDAR) and adapted real-world datasets (Se- manticKITTI or nuScenes). See Appendix A.3.I for more details.
Label Mapping: Since no standard setup currently exists for OSTTA-3DSeg, we propose standard- ized training and evaluation settings to facilitate future research in this area. We design virtual- to-real experiments based on LiDAR beam configurations as: Synth4D-64
SemanticKITTI, Synth4D-32 to nuScenes, and SynLiDAR to SemanticKITTI. For the first two experiments, fol- lowing GIPSO (Saltori et al.] 2022), we remap the SemanticKITTI and nuScenes datasets to match Synth4D by selecting seven common classes and treating all remaining categories as OOD classes. In the SynLiDAR to SemanticKITTI experiment, we follow (Cen et al. 2022). designating other- vehicle as the OOD class, while all other categories are treated as ID classes.
Following recent researches (Saltori et al. 2022 Zou et al. 2024). we eval- uate the performance of TTA methods on a new incoming frame utilizing the model that has been adapted to the preceding frame. To evaluate the adapted model in an open-world setting, we con- sider both closed-set and open-set metrics. Specifically, the closed-set performance is measured by the improvement in mean intersection-over-mme performance is evaluated using the area under the receiver operating characteristic curve (AUROC) and the false positive rate of OOD samples at a 95% true positive rate for ID samples (FPR95)..
Evaluation Protocol:
(mioU) over the source model, while open-set
Baseline Methods: We mainly compare our method with TTA-3DSeg baselines, such as
GIPSO (Saltori et al. [2022) and HGL (Zou et al. 22024), and image- based OSTTA methods, in- introduce straightforward bascline based on hyperparameter thresholds searching to distinguish ID and OOD points, termed Searching Thresholds (SeaT). This strategy identifies OOD points by selecting points with the lowest confidence.
cluding UniEnt (Gao et al. 2024 and WCEM (Lee et al. (023). In addition,
Implementation Details: Following Saltori et al. 2022 Zou et al., 2024). we use the MinkowskiUNeet-15 (Choy et al., 2019) as our backbone and set the voxel size to 0.05. For online model adaptation, the batch size is set to 1 across all benchmark datasets. We configure hyperpa- rameters as 3, = 0.85, and À 0.1 for all datasets. For temporal K-NN, K is set to 10 for SemanticKITTI and 5 for nuScenes. Further details on DBSCAN configuration and additional implementation information can be found in the Appendix A.3.5
4.2 EXPERIMENT RESULTS
Synth4D to SemanticKITTI and nuScenes: We first evaluate our method on the Synth4D to Se- ManticKITTI and Synth4D to nuScenes tasks. As shown in Tables I [ GOOD significantly en- hances both closed-set ID segmentation and open-set OOD identification performance. Specifically,
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Gronn Truh
DRSCAN Cluster
W/O SIDP
With
Qualitative results of Superpoint ID Prototypes (SIDP) Synth4D to SemanticKITTI. Black pixels represent the open-set points in ground truth or unknown pseudo-label. The results indicate that SIDP effectively corrects ID misclassifications and handles absent OOD.
Figure 3:
Trpatt
GrundTThhh
Source
Ous
Figure 4: Qualitative results on Synth4D to SemanticKITTT Black pixels denote open-set points in the ground truth and prediction OOD points with Maximum Softmax Probability (MSP) above 0.6. Our method significantly enhances both ID and OOD segmentation.
on Synth4D to SemanticKITTI, GOOD achieves improvements in mloU, AUROC, and FPR95 of +4.83% 73.39%, and 71.91% %. respectively, marking gains of I. 1.93%, 8.99% and 7.91% over HGL. Similarly, on Synth4D to nuScenes, GOOD yields mloU, AUROC, and FPR95 values of +2.23%, 64.60%, and 72.60%, with respective improvements of 1. 1.72%, 6.93%, and 9.41% over HGL. Fur- thermore, GOOD can complement existing methods effectively. For example, integrating GOOD with HGL on Synth4D to SemanticKITTI enhances mloU from +2.90% to +5.31%, AUROC from 64.40% to 73.39%, and reduces FPR95 from 79.82% to 71.91%.
In comparison, the 2D image method UniEnt (Gao al. 2024) negatively impacts closed-set 3D segmentation mIoU and has limited effect on open-set AUROC and FPR95. This is primarily due to its inability to effectively differentiate between imbalanced ID and OOD points, resulting in the erroneous application of entropy maximization to many ID points and leading to suboptimal results. The addition of + WCEM (Li et al.] (023b) yields a noticeable improvement in closed-set perfor- mance but performs poorly on open-set metries. This is also due its difficulty in distinguishing ID from OOD points, which causes the loss function to degenerate into entropy minimization.
SynLiDAR to SemanticKITTI: We then conduct experiments SynLiDAR to SemanticKITTL. In this setup, the imbalance between known and unknown points is exacerbated, with many frames lacking any unknown points. As shown in Table 3 consistent with our previous findings, GOOD achieves superior performance. Specifically, GOOD yields mloU improvement, AUROC, and FPR95 scores of +5.03%. 73.23%, and 69.96%, marking gains of 3.13%, 10.32%, and 9.17% HGL, respectively. Additionally, the increased imbalance between ID and OOD points causes a notable decline in both closed-set and open-set performance for the +SaaT and +nniEnt methods.
The last column of Table 4 presents the adaptation times. Our method outperforms GIPSO in speed but is slightly less efficient than HGL, primarily due to the multi-frame superpoint clustering step, which takes about 0.7 seconds. Notably, under closed-set conditions without the Superpoint Representation Branch (SRB), our method achieves speed comparable to HGL while outperforming it by 1.71% & in mloU, as shown in Experiment I. More experiments and discussions regarding clustering algorithms and runtime can be found in the Appendix A.3.4 and A.3.5
Efficiency:
Fig. 4 shows the comparison between GOOD and the source model Synth4D to SemanticKITTI. demonstrating that GOOD consistently improves segmentation per- formance for both ID and OOD points.
Qualitative Results:
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AUROCIT)
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Figure 5: papee--arameter sensitivity analysis on Synth4D to SemanticKIITTI
Table 4: Ablation Study. TP, SC, and SIDP de- note Temporal Pseudo-label Superpoint Con- fidence and Superpoint ID Prototypes, respec- tively.
Table 5: Results under different sparsity levels and OOD class counts. Values presented (mloU(T), AUROC(1). F)))
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(47.66 26.65 76.7%)
4.3 ABLATION STUDY
We conduct an extensive ablation study to assess the contribution of each component. Unless other- wise specified, this study is conducted on Synth4D to SemanticKITTI.
As shown in Tab. we evaluate the effectiveness of the Temporal Pseudo- labels (TP), Superpoint Confidence (SC), and Superpoint ID Prototypes (SIDP). To assess the effec- tiveness of the proposed TP, conduct experiments using only TP, and the result from Experiment I indicates that the standalone TP model significantly outperforms the baseline methods. Experiments I and II demonstrate that introducing SC significantly enhances open-set performance, despite mi- nor decline in closed-set performance. The results of Experiments II and III demonstrate the critical role of the SIDP, as it effectively reduces the misclassification of known superpoints, while simul- taneously enhancing both open-set and closed-set performance. The visualization results in Fig. also qualitatively demonstrate the effectiveness of SIDP.
Component Analysis:
Hyper-parameter Sensitivity: We first examine the sensitivity of X in Fig. (a), where À takes values from {0.05, 0.1, 0.2, 0.3, 0.5}. The results indicate that larger À values improve open-set performance but may slightly reduce closed-set performance. For all benchmarks, we set X = 0.1. We then explore the effect of w in Fig. (b), with w ranging from {2. 3, 5, 7, 10]. The results show that our method is robust around the selected parameter w 3. We then evaluate the hyperparame- ter K in K-NN, with values ranging from 0 to 25. As shown in Fig. (c), K demonstrates stability around a value of 10. Finally, we assess r in Fig- (dd A smaller r value sets stricter criterion for identifying unknown superpoints, enhancing closed-set performance while reducing open-set per- formance, and vice versa. For all benchmarks, predefine 0.85 distinguish between ID and OOD superpoints.
Performance under Different Sparsity levels and OOD Class Counts: The sparsity level and OOD class counts represent the complexity of the open world. We examine the impact of different sparsity levels and unknown class counts in Tab. 5 For different sparsity levels, perform experi- ments on the SynLiDAR to SemanticKITTI and control the data ratio at 0.46%, 1.15%, 6.47%, and 11.90%, designating other-vehicle, trunk, car, and building different class counts, we sort the class proportions from smallest to largest, discarding all classes with proportions less than or equal to person, other-vehicle, fence, and car. The proportions of un- known classes are 0.46%, 1.30%, 6.44%, and 12.92%. corresponding to unknown class counts of 7, 10, 13, and 14, respectively. As shown in Tab. 5 our GOOD and SRB can effectively address varying sparsity levels and OOD class counts.
unknown class, respectively. For
Further ablation studies and additional experimental results are provided in the Appendix A.3.5
CONCLUSION
In this paper, we introduce the GOOD framework, a simple yel effective approach for open-set test- time adaptation in point cloud semantic segmentation. Existing methods, primarily developed for 2D)
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image recognition, face significant challenges when applied to point cloud segmentation due to the complexities of point cloud data, especially the imbalance between ID and OOD points. To address these issues, our framework leverages the unique characteristics of point clouds by using superpoints to mitigate data imbalance. Technically, we devise the Superpoint Clustering method to organize the disordered point cloud, then propose the Superpoint Confidence metric to preliminarily distinguish ID and OOD superpoints, and finally introduce the Superpoint ID Prototypes to further refine the pseudo-
identification of ambiguous OOD superpoints. Furthermore, we propose a temporal- label generation method that simultaneously leverages both data temporal information and model temporal consistency. Extensive experiments demonstrate that GOOD significantly outperforms existing TTA-3DSeg methods in open-set scenarios. Notably, on the Synth4D to SemanticKITTI task, GOOD surpasses HGL by 8.99% and 7.91% in AUROC and FPR95, respectively.
Acknowledgments: This work was supported by the National Natural Science Foundation of China (No. 62372329). in part by the National Key Research and Development Program of China (No. 2024YFE021 10000,, in part by the National Natural Science Foundation of China (No. 62506263, 62506264), in part by the Shanghai Scientific Innovation Foundation (No. 23DZ1203400), in part by the China Postdoctoral Science Foundation (No. BX20250383, GZB20250385, 2025M771530, (025M771539) in part by Tongji-Qoolo Autonomous Driving Commercial Vehicle Joint Lab Project, and in part by Xiaomi Young Talents Program.
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Yang Zhang. Zixiang Zhou, Philip David, Xiangyu Yue, Zerong Xi, Boqing Gong, and Hassan Foroosh. Polarnet: An improved grid representation for online lidar point clouds semantic seg- mentation. In Proceedings of the IEEE/CVF Conference Computer Vision and Pattern Recog- nition, pp. 9601-9610, 2020.
Yizhe Zhang, Shubhankar Borse, Hong Cai, and Fatih Porikli. Auxadapt: Stable and efficient test- time adaptation for temporally consistent video semantic segmentation. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 2339-2348, 2022.
Yin Zhou and Oncel Tuzel. Voxelnet: End-to-end learning for point cloud based 3d object detection. In Proceedings of the IEEE conference computer vision and pattern recognition, pp. 4490- 4499, 2018
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arXiv preprini arXiv:2407.12387 2024.
A APPENDIX
A. MORE RELATED WORK
Point Cloud Semantic Segmentation: Point cloud semantic segmentation is a fundamental com- puter vision task that assigns semantic categories to each point within a point cloud. Depending on the specific methodology used, approaches to point cloud segmentation can be broadly categorized into point-based methods (Qi et al. 2017a b. Hu et al. 2020). range-map-based methods (Wu et al.
2018 Cortinhal et al. 2020
Milioto et al. 2019 sparse voxel-based methods (Choy et al. 2019
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Graham et al. 2018 Graham & Van der Maaten 201 Zhou & Tuzel 2018), and other specialized approaches such as hybrid point-voxel (Tang et al., (020), polar coordinate (Zhagg et al. 2020). and cylindrical coordinate systems (Zhu et al. 2021). Although these methods achieve strong per- formance under closed-set conditions, they remain sensitive to distribution shifts and struggle to handle OOD objects absent from training data. Beyond traditional closed-set segmentation, recent advances open-vocabulary 3D scene understanding (Peng et al. 2023). few-shot 3D segmenta- tion (An et al.) 2024b a). and generalized segmentation with vision-language models (An et al.) 2025) aim to improve flexibility and reduce reliance on large labeled datasets. However, these ap- proaches are typically developed and evaluated on single datasets, and their generalization across di- verse domains or unseen categories remains limited. This further motivates the need for a framework specifically designed to enhance both robustness and generalization in open-world 3D environments.
A.2
MORE DETAILS ABOUT METHODOLOGY
A.2.1
TEMPORAL-BASED FEATURE REGULARIZATION
We employ the widely used Temporal-based Feature Regularization module (Saltori et al. 2022; Chen & He 2021) to mitigate negative transfer within the model. This process begins with calculat- ing the temporal correspondences between feature points in the frames X1- and X1 by applying rigid transformation Th- derived from the odometry data. Specifically, the temporal corre- sponding points (-- are defined as:
O iffa² - E Xx x'- x : (T (I--w-i x/ x4) x'- 11
Here, x² represents the nearest neighbor (NN(.)) point of X2 the Tt- and 7' distance threshold that filters out pairs with distances above this value. Following SimSiam (Chen & He 2021), we introduce an encoder network h(.) and a predictor head f()) the target model Frr. These components minimize the negative cosine similarity between the semantic representations of temporally corresponding points. The negative cosine similarity can be formulated
f-w
Di-eew (t = 11 1|t--=1 1
(8)
where z' h(x*)) is the encoder features, and 9t f(h(x*)) represents the predictor features. To enforce temporal consistency, we define the final temporal consistency loss as:
Creg = 1 D
1 -- 2t + De- (g-
(9)
where stop-gras operator is applied 2t and t"
A.2.2 FINAL ID PSEUDO-LABEL GENERATION
Given the ID pseudo-label ÝID generated from the Temporal Pseudo-label Branch with the OOD pseudo-label YooD derived from the Superpoint Representation Branch, we can refine YID using formation from YOOD and superpoints. We explore two optimization strategies: 1) Direct Filtering: We remove overlapping regions of YooD from YID, resulting in a filtered ID pseudo-label, denoted as YID/YOOD. 2) Superpoint-Band Based Refinement: Based on the filtered pseudo-label ÝID/ / we incorporate superpoints to further refine the pseudo-labels (+ (+Super). Specifically, we assess each superpoint's ID pseudo-label composition. If a superpoint exclusively contains a single class of ID pseudo-labels, we assign all points within that superpoint to this class. If superpoint contains multiple classes, is excluded from further consideration.
As shown in Tab. 6 both strategies enhance open-set performance relative to the initial ID pseudo- label. Although Strategy Two yields slight performance improvement over Strategy One, risks introducing a substantial number of incorrect pseudo-labels, compromising its stability. Therefore, select the simpler and more stable Strategy One.
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A.3
MORE EXPERIMENT DETAILS
A.3.1 SET UP
Following previous studies, we evaluate our method on the widely used Lidar datasets: Synth4D (Saltori et al., 2022). SynL .iDAR (Xiao et al.] (021). Se- manticKITTI (Behley et al. 2019) and nuScenes (Caesar et al., 2020). Synth4D (Saltori et al.) 2022] is synthesized by the CARLA simulator (Dosovitskiy et al. 2017) and consists of semantic classes, available in two configurations: 64-beams and 32-beams to simulate either SemantiKlTTI or nuScenes. SynLiDAR (Xiao et al. 202T) synthesized by the Unreal Engine 4 platform and contains 19 semantic classes to simulate SemantiKITTI. SemanticKITTI (Behley et al., 2019) is large-scale real-world dataset for LiDAR point-cloud semantic segmentation, which contains 19 semantic classes. nuScenes (Caesar et al., (020 has 32 class labels and 40K LiDAR frames anno- tated with per-point semantic labels from IK sequences. Following the official sequence split, we use scene 08 in SemanticKITTI and 150 sequences in nuScenes for validation.
Source and Target Datasets:
Baseline Methods: Due to the limitations of image-based TTA methods when applied to point clouds (Zou et al.] 2024). we mainly compare our method with specific TTA-3DSeg baselines, such as GIPSO (Saltori et al. 2022) and HGL (Zou et al. 2024). Considering that the original TTA-3DSeg baselines are inadequate for addressing open-world scenarios, we incorporate several representative image-based OSTTA methods as an additional complement. UniEnt (Gao et al.) 2024) adopts sample-based GMM to distinguish ID and OOD samples, followed by entropy mini- mization on the pseudo-ID ID data and entropy maximization on the pseudo-OOD data. OSTTA (Lee) al. (022) uses the wisdom of crowds to filter out the samples with lower confidence values in the adapted model compared to the original model. In addition, we develop straightforward method based on hyperparameter thresholds searching to distinguish between ID and OOD, termed Search- ing Thresholds (SeaT). This method targets high-uncertaing points in the pseudo-labe generation process and classifies them as OOD samples.
For the source model training, we use the Adam optimizer with an initial learning rate of 0.01, applying exponential decay, batch size of 24, and weight decay of 10-5, Training is conducted uniformly over 100 epochs across all source domain data, with the final model weights from the last epoch used as the definitive model. To avoid confusion between ID and OOD points, we employed the Dice loss instead of the soft Dice loss in GIPSO (Saltori et al. 2022). For online adaptation,
Implementation Details:
also apply the Adam optimizer with weight decay 10- and set the learning rate to 10-3 3 for all datasets. We avoid using learning rate schedulers to eliminate the need for prior knowledge about the length of the data stream. All experiments are conducted on L40 GPU with PyTorch-1.9.
To ensure full reproducibility of preprocessing, each LiDAR scan is divided into 10x10 sub-regions to reduce the effect of uneven ground surfaces and allow RANSAC to operate on locally consistent regions, improving plane estimation accuracy. Candidate ground points are defined as those with height below -1 relative the ego-vehicle coordinate system, which works effectively across datasets due to the similar mounting height of vehicle LiDAR sensors. For RANSAC, the distance threshold is set 0.1, the minimum number of points for model fitting is 3, and the number of iterations is 1000.
More Detail About RANSAC:
A.3.2 INTEGRATION INTO EXISTING METHODS
As claborated in the main text of our study, the GOOD framework introduces an innovative approach to OSTTA-3DSeg through superpoint representation and temporal lseudo-label. Our approach can serve as a valuable complement to cxisting methods. To validate this merit, we have integrated GOOD with representative methods, specifically GIPSO (Saltori et al. 2022) and HGL (Zou et al.) 2024]. In light of the unsupervised nature of the OSTTA-3DSes task, this integration is realized by aligning the optimization objectives of GOOD with those of the baseline methods. Specially, the integrated optimization objective is presented as follows:
Loverall = LGOOD + (1 - Loaseline
(10)
where 7 is a trade-off paraeepar,meter, generally set to 0.5. We can also combine the ID pseudo- labels from GOOD and baselines to obtain final pseudo-labels, which could yield similar results.
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AUSMOC
dtel
AU00C
FRR85
m 3
22
22
035
Spalon
Figure 6: Hyper-parameter sensitivity analysis of DBSCAN Synth4D SemanticKITTI.
Figure 7: Hyper-parameler sensitivity analysis of DBSCAN on nuScenes SemanticKITTL.
114
135
44
11
721
117
94
11
115
400
Figure 8: Highly coupled hyperparameters sensitivity analysis on Synth4D to SemanticKITTL
A.3.3 EXPERIMENTS ON INDOOR DATASETS
Our method is also applicable to indoor datasets. We conducted additional experiments on Scan- Net (Dai et al. 2017) using MinkUNet34C as the backbone. We use the standard ScanNet20 (Dai ct al. 2017) 7) classes as ID categories and treat all other classes in ScanNet200 (Rozenberszki et al. 2022 OOD. The source model achieve mloU 65.01% %. AUROC 66.37%, and FPR95 88.93% After introducing GOOD, the performance mloU 61.58%, AUROC 70.23%, and FPR95 85.93%
It can be observed that while the ID performance decreased, the OOD performance improved. The decline in ID performance primarily due due the additional OOD loss, phenomenon sim-
ilarly reported in other 3D OS semantic segmentation studies (Cen et al. 2022 Li & Dong 2023
Xu et al. 2023
A.3.4 RUNTIME ANALYSIS
We provide a detailed analysis of the runtime for Superpoint Clustering. In Superpoint Clustering, RANSAC takes approximately 0.55s on the CPU, while DBSACN clustering takes around O. 0.15s on the GPU and 0.25s the CPU. Our method is slightly less efficient than HGL (0.65s) in speed but outperforms GIPSO (4.03s). For practical deployment, we offer two solutions to reduce runtime:
Deploy Superpoint Clustering only on the CPU. CPU-based clustering takes about 0.8s, while TTA takes about 0.7s. In practice, clustering for the next frame can run concurrently with TTA.
Replace RANSAC with height threshold (e.g.,-I, -1.4m (Geiger al. 2012)), sacrificing small amount of perception accuracy (mloU(t) from +4.83% to +4.65% AUROC(T) from 73.39% to 71.50% .50%, FPR95()) from 71.91% % to 77.39%) but achieving runtime comparable to (06s vs 0.65s).
A.3.5 MORE ABLATION ANALYSIS
We conduct a more comprehensive analysis in GOOD. Unless otherwise specified, the ablation analysis is based on Synth4D to SemanticKITTI
Different Superroint Clustering: We also experimented with clustering algorithms other than DB- SCAN, including K-Means and the
MiotoornKKoop--baae optimal transport algorithm, as shown in Tab. 7 Compared to DBSCAN, K-Means and Sinkhom-KKoop require manual setting of the num- ber of clusters, which limits their adaptability. Moreover, they cluster all points without filtering, resulting in the inclusion of large number of outliers. Due to these two factors, their performance is inferior. It is also worth noting that unlike CPCR's s online feature clustering (Feng et al. 2023), the OT offline coordinate clustering significantly slower than the DBSCAN algorithm (50.30s vs 0.25s). While DBSCAN has time complexity of O(N2), OT's worst-case complexity is approx- imately O(TN where T, N, M, and denote the number of center iterations, points, clusters, and Sinkhorn iterations, respectively. Given T = 100, N 100000, M = 100 and 1000, OT is up to 100 times slower than DBSCAN.
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Table 6: Results on different ID pseudo-label
Table 7: Results on different superpoint clus- tering.
strategy.
Model
Vie
Yso/YooD
(No//Yoo)] Super
mlol)(%)
AUROC(S)
FPRO5(55)
Rentime)))
mlaU(5)
+4,95 0007
+4.33 + D.06
+4.7 +
Kmeans
+4.30
70.75
74.47
0.18
AUROC(%))
72.49
73.39 0.37
7344+0042 0.43
Sinkhoen Knopp
+4.26
69.25
74.60
50,30
FPR95(3)
1
71.91 1 1,09
72.50 1.57
DBSCAN
+1,83
73.39
71.11
0.25
Table 8: Ablation Study. TP: Temporal Pseudo-label. perpoint Confidence. SIDP: Superpoint ID Prototypes. EMA: Exponential Moving Average. *;
TR: Temporal Regularization. SC: Su-
without Temporal Regularization.
ID Scunce
TP
TR
sc:
SIDP
EMA
mloU(())
AUROCCS))
FPR35/3)
Runtiaae(s)
40
65.80
78.39
GIPSO
Saheri el 2002
+2.05
64.17
$0.40
4,00
GIPSU
103000 C4 PUPH
-0.25
03.30
911.73
3,85
HGL
2010 2020
+2.90
64.40
79.82
0.03
HGB
7anrer
+0.51
63.01
88.28
0.46
+2.64
65.36
87.00
0.52
II
+3.36
67.09
80.47
0.70
+3.56
73.96
1.38
+3.07
74.28
1.30
VI
3
VII
+4.54
71.47
76.82
1.42
VIII
44.83
73.39
71.90
1.45
More Detail About DBSCAN: DBSCAN is a popular density- clustering algorithm that iden- tifies clusters by grouping points within proximity and designating points in low-density areas as outliers. The algorithm relies on two key parameters: epsilon and min-samples. Epsilon defines the maximum distance between two points for them to be considered part of the same neighborhood, while min-samples the minimum number of points required to form a cluster. In our experiments, we set epsilon as 0.25 and set min-samples as 50 for SemanticKITTT and 25 for nuScenes.
We first examine the effect of min-sample, with epsilon taking values from {10, 25, 50, 75, 100) and epsilon holds constant. The experimental results in the left side of Fig. show that performance remains stable around the chosen parameter value of min-samples impact of varying epsilon on OOD superpoint detection, holding min-samples constant. We test ep- silon values from {0.15, 0.25, 0.50, 0.75, 1.00), as illustrated on the right side of Fig. 6 The results reveal that epsilon is more sensitive than min-samples. Notably, open-set performance decreases as epsilon increases, while closed-set performance initially improves and subsequently declines. This behavior occurs because a low epsilon value leads to more discrete and smaller superpoints, enhanc- ing the distinction between ID and OOD suerpoints. However, this also makes it more challenging for ID pseudo-labels to benefit from the OOD pscudo-labels. In contrast, when epsilon is higher, the superpoints expand and may encompass multiple classes, which can increase misclassification rates. Therefore, moderate epsilon value, such as 0.25, balances the trade-off between superpoint granu- larity and classification accuracy, yielding satisfactory performance in both open-set and closed-set scenarios. In addition to the sim-to-real experiments presented in Fig. 6 we further conduct real- The results consistently demonstrate the effectiveness and
50. Next, we investigate the
to-real experiments as shown in Fig. 7 stability of the proposed hyperparameters.
More Hyper-parameter Sensitivity Analysis: We perform detailed sensitivity analyses on two strongly coupled hyperparameter groups: (1) K in K-NN and the w in temporal aggregation for temporal information, and (2) confidence threshold T and the loss weight for OOD separation. Fig. shows stable performance within selected ranges. We further conduct sensitivity analysis on the EMA hyperparameter a, as shown in Tab. 9 In experiments on the Synth4D-to Semantie setting, GOOD shows low sensitivity to this hyperparameter. In highly dynamic scenarios with large domain shifts, a smaller a can help maintain stability.
As shown in Tab. 8, we conduct more comprehensive analysis of the components in GOOD, where components Temporal Pseudo-label (TP), Superpoint Confidence (SC), and Superpoint ID Prototypes (SIDP) have been previously discussed in the main text. Based on the results of experiments IV and VIII, we observe that incorporating Exponential Moving Av- erage (EMA) significantly enhances closed-set performance, but slightly reduces open-set perfor- mance. We hypothesize that this trade-off arises because EMA improves ID pseudo-label accu- racy through consistency, which may inadvertently propagate the prior overconfidence of the source model regarding OOD points, thereby negatively impacting the OOD pseudo-label. Experiments I1, V, and VIII demonstrate that Temporal Regularization (TR) effectively improves both the effi- ciency and stability of the model's closed-set performance, also reported in (Saltori al. 2022 Zou
More Component Analysis:
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Table 9: Haper-parameter sensitivity analysis of the EMA coefficient
Table 10: Results w/ and w/o SIDP under vary- ing OOD ratios.
0,9999
OOD ratios
Souice
nloUkth AUROCITI, FPRS5C))
GOOD SIDP
AUROCIO
()ROS())
74.62
+6.70
22.42
72.40
71.91
7587
71.16
+4.06
70.50
~: 12.99
0.11%
6.44
(46 48, 64.00, (1-47) (+7.00 74-40 76 76.07)
(24.92, 66.25, 70.18)
016 09142, 96.54
(+5877 56.66 27
(43.44, 70.44, 60 25)
6ZAR 91.39) (43.00
(+488
71.90 65.75) (+2A8, 74.64 76.33
Table 11: Results on different superpoint con-
Table 12: mloU (%) results close-set
fidence.
Confidence
MSP
Margin
Purity
Eatropy
Purity-Furrop
Model
Synth4D2KITTI
Syuth442ouSc
SynlidarKIITTI
mloU(%)
+4.89
+5.05
+5.21
+4.00
+4.83
Source
35,93
30.75
40.19
AUROC/S)
69.79
HGI.
+6.40
+1.87
+6.72
FPR95(3)
77.22
73.13
75.43
69.74
71.91
Ours
+7.05
+2.22
+6.54
2024). Additionally, compared to previous pseudo-label generation methods (Saltori et al. Zou et al. (004)), our temporal pseudo-label approach proves to be more robust to regulariza- tion due its incorporation of temporal consistency, which effectively mitigates negative transfer during optimization.
al.
2022
Prototype Stability and Safeguards: In our framework, prototypes are updated via EMA using pseudo-labeled superpoints, which raises the question of whether early-stage noise may cause accu- mulated drift. To investigate this, compared several safeguard mechanisms, including weighted prototypes (WP) Manna et al.] (2024), threshold- center prototypes (MP) et al. (202)). The results in Tab. 13 show that WP achieves performance comparable to the original approach, TP performs slightly worse, likely due to strict filtering of valuable samples, and MP shows mixed effects, improving some metries while hurting others. The limited benefit of TP and MP can be attributed to the prototype construction process: superpoints are aggregated, and a GMM selects only high-confidence samples, mitigating the influence of noisy inputs at an early stage. Additional safeguards introduce extra hyperparameters without clear gains, so the original prototype formulation is retained for both simplicity and effectiveness. To further assess temporal stability, classification aceuracy for known and unknown categories was monitored before and after prototype updates, aggregated every 500 training steps, yielding values [1.61 %, 1.85%, 2.57%, 2.37%, 3.83%, 1.41%, 2.52%, J. These results show an overall upward trend, indicating that prototype updates stabilize over time and reduce category assignment errors rather than amplifying early-stage noise. Together, these analyses confirm that the baseline prototype mechanism is robust and temporally stable.
prototypes (TP) [Su et al. (2024), and multi-
Loss Function Analysis: We compared multiple loss functions for ID pseudo-label learning, in- cluding Dice loss, Soft Dice loss, SCE loss, and CE loss. The results are summarized in Tab. [4 Soft Dice loss leads to severe degradation in open-set detection capability, reflected in lower AU- ROC and higher FPR95, while SCE and CE losses achieve strong open-set results but insufficient closed-set segmentation performance, indicating that these losses overemphasize separability at the cost of reconstruction quality. Dice loss provides the best balance across both closed-set and open- set tasks. Its effectiveness for small objects and boundary regions, along with inherent normalization that mitigates class imbalance, makes it particularly suitable for superpoint aggregation and proto- type construction, supporting both stable pseudo-label refinement and consistent adaptation across domains.
Alternative Strategies to GMM: We also investigated threshold-based strategies that separate known and unknown categories using manually selected thresholds purity and entropy. Two variants were evaluated: threshold-open. which favors open-set performance, and threshold-closed, which favors closed-set performance. Although these approaches achieve reasonable results, they require additional hyperparameter tuning, and optimal thresholds vary across datasets, making sta- ble performance difficult without dataset-specific adjustment. In contrast, the GMM-based method adapts automatically to the score distribution of each domain and does not require manual thresholds. Empirical results, summarized in Tab. [5] show that while threshold- strategies can achieve competitive metrics, the GMseb method provides a more robust and indeeeeend solu- tion, motivating its use as the default.
Different Backbone Architectures: To assess the architectural generality of our framework, we evaluate its performance on a diverse set of backbone models, including MinkowskiUNedd Choy
al. 2019), MinkowskiUNNet- Choy et al. (2019), MinkowskiUNett-34 Choy al. (2019). Point- Net++ + Qi et al. (20175), and PTV2 et al. (2022), covering voxel-based, baset- and hybrid designs. As shown in Tab. 16] the method consistently improves both closed-set mloU and open-
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Target Irgar
Gronn Trit
Soure
Outs
Figure 9: Qualitative results in SynLiDAR to SemanticKITTL. Black pixels indicate the open-set points, and we consistently select points with Maximum Softmax Probability (MSP) greater than 0.6 as OOD points.
Tarss Input
Grund Truth
Source
Our
Figure 10:
Qualitative results in Synth4D to nuScenes.
set AUROC/FPR95 for all backbones, demonstrating that the proposed approach is architecture- agnostic and broadly compatible with different 3D semantic segmentation models.
Different Superpoint Confidence:
We experiment with different confidence metrics in the super- point representation, including Maximum Softmax Probability (MSP) (Hendrycks & Gimpel| 2016), Margin (Roth & Small, 2006). Purity (Zou et al. 2024). and Entropy (Shannon) 1948). As shown Tab. I1 Purity outperforms MSP and Margin, though all three demonstrate suboptimal open-set performance. While Entropy shows limited efficacy in the closed-set setting, it achieves outstanding results in open-set scenarios. Based these observations, we select Entropy to complement Purity, yielding robust performance in both open and closed sets. With GT labels, we also quantitatively report the average confidence scores of Purity+Entropy for ID and OOD superpoints 0.57 and 0,81, respectively, validating the effectiveness of our intuition in Sec. 3.2
Performance under Close-set: Although our method is designed for open-world scenarios, previ- ous experiments have indicated that the ID pscudo-labels generated by the Temporal Pseudo-labels Branch exhibit high quality, while the OOD pscudo-labels produced by the Superpoint Representa- tion Branch can be regarded as an additional filter for the pseudo-labeks Consequently, we attempt to evaluate our method in purely close-set. To accommodate the closed-set configuration, we re- moved LooD and substituted Ldice with L diee to ensure a fair comparison. As shown in Tab, our method achieves performance comparable to the state-of-the-an approach in Synth4D2KITTI and attains state-of-the results in Synth4D2nuSe and Synlidar2KITTI. These results demonstrate that GOOD exhibits strong generalizability and is effective in closed sets.
More Sparse OOD Scenario: We also conducted experiments the SynLiDAR SemanticKITTI dataset, controlling the data ratio at 0. %, with person designated as an unknown class, representing more sparse OOD scenario. We observe AUROC improvement from 60.42% to 62.65% and
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b
Target Irpat
Ground Truth
Source
Ouss
Figure 11: Failure cases in Synth4D to SemanticKITTL Black pixels indicate the open-set points, and we consistently select points with Maximum Softmax Probability (MSP) greater than 0.6 as OOD points.
Table 13: Results on different prototype safe- guard mechanisms
Table 14: Results on different ID pseudo-label learning losses.
Original
WP
TP
MP
Source
Dice(GOOD)
SoftDiee
SCE
CE
mloU (5)
+4.83
+4.81
+4.57
+4.94
mloU (5)
40.26
+4.83
+4.81
+3.33
+3.53
AUROC (%)
73.39
74.01
72.87
72.72
AUROC (%)
65.80
73.39
68.62
76.70
75.70
FPR95 (%)
71.91
71.38
75.76
76.40
FPR95 (%)
78.39
71.91
84.87
63.26
64.07
FRP95 improvement from 94.74% to 91.39%, where SIDP plays pivotal role. Without SIDP, both the AUROC and FPR95 exhibit a decline in performance (from 60.42% to 60.26% and 94.74% to 95.77%). Furthermore, we show that SC may fail under extreme imbalance where OOD points are extremely sparse or entirely absent, whereas our SIDP strategy effectively alleviates this issue. The Tab. [0 presents the results with and without the SIDP strategy across range of OOD ratios setting, demonstrating the effectiveness of SIDP in mitigating GMM's partitaatiio behavior.
in the
A.3.6 DISCUSSION
Combine with Open-set Segmentation: In addition to combining the existing TTA-3DSes and OSTTA-2D, we can also directly use 3D OS semantic segmentation (3D-OSSS) as a baseline. Con- sidering code reproduction and feasibility, we use LiON (Xu et al. 2023) as the baseline. Compared the normal source model, which yields mloU of 26.95%, AUROC of 54.90%, and FPR95 of 86.67% on SynLiDAR to SemanticKITTI. LiON produces an mloU of 25.44%, AUROC of 65.47%, and FPR95 of 75. 19%. Although 3D-OSSS performs better,
important note that 3D-OSSS (Cen et al. 2022 Li & Dong 2023 Xu et al. 2023) alters the source model's network structure, training strategy, and even introduces additional OOD datasets, which contradicts the goal of TTA methods to avoid retraining the original model. Therefore, we still choose the combination of TTA-3Segg and OSTTA-21 2D as the baseline model to enhance generalizability. Additionally, 3D- OSSS and our method are not in competition but are complementary. The GOOD approach can also be applied to the source model obtained through DD-OSSS. After applying GOOD, the LiON results improved to an mloU of 30.53%, AUROC of 77.66%. and FPR95 of 66.85%.
Ego-Pose Assumption and Robustness Analysis: Following standard practice in prior works (Saltori et al.) [022]] Zou et al.) (2044), our framework assumes access to reliable ego poses for temporal alignment; however, we acknowledge that pose quality may vary in real-world appli- cations, and therefore we provide extensive analyses to evaluate robustness under imperfect con- ditions. To verify whether GOOD critically relies on perfect poses, we replace the provided ego poses with estimates obtained through GICP SSgal et (2009). fast and accurate registration- based method for dense geometric alignment. The results in Tab. 17 show minimal performance differences compared with using perfect poses, demonstrating that temporal aggregation remains effective with registration-derived alignment. To further quantify sensitivity, we inject controlled translation and rotation perturbations into the poses. As shown in Tab. 19 we find that while trans-
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Table 15: Comparison of GMM and threshold- based strategies
Table 16: Results on different backbone archi- tectures.
Source
GMM
Threshold-ppen
Threshuld-cfosed
PoinNere+
MK-14
Mi-15
Mk: 34
PTV2 Source Oues
Sourse
Ours
Sounce
Ous
Swence
Ours
Soure
Our
AUROC (8)
65,80
73.39
AUROC
66.80
13.39
+4.37
78.35
FPR95 (%)
78.39
71.91
70.55
73.01
FPRU5
95.47
51.16
83.30
76.8
78,39
71.91
67.70
7966
85.21
70.02
Table 17: Performance comparison under dif- ferent pose settings.
Table 18: Performance comparison of GOOD- single and different models.
Source
Perfect Pose
GICP Pose
Source
GOOD
GOOD-single
HGL
GIPSO
mloU (18)
40.26
+4.83
+4.80
mloU (%)
40.26
+4.83
+2.37
+2.90
+2.08
AUROC (%)
65.80
73.39
74.29
AUROC (%)
65.80
73.39
73.23
64.40
64.47
FPR95 (%%)
78.39
71.91
FPR95 (%)
75.04
Table 19; Results under different transformations and rotations perturbations. Translation noise is denoted as Terr, where the noise is uniformly sampled within X, X] meters, and rotation noise is denoted as Rerr, sampled within [-Y, -Y.Y YJ degrees.
Source
Perfect Pose
Ro
To
T2
Too-Rs
Too-Ro To.o-Be
Tb.
Tio-Boo
Th
mloU (%)
40.26
+4.83
+4.54
+4.29
+2.86
+1.75
+0.91
+0.51
+0.98
+0.55
-0.08
AUROC (8)
65.80
73,39
73.28
73.94
74.53
74.43
72.27
72.76
73.31
72.41
70.35
FPR95 (9)
78.39
71.91
73.55
74.92
72.39
68.95
77.52
74.21
75.11
78.05
77.84
lation noise has limited influence, rotation noise leads to more noticeable degradation, and very large combined perturbations (c.g., and 20%) cause temporal correspondences to collapse, eliminat- ing the gains from temporal consistency, which is expected given the correspondence-drivee nature of TPB. To address scenarios where pose estimates are unreliable entirely unavailable, ditionally evaluate a single-frame variant of GOOD that removes temporal consistency and relies solely intra-frame superpoint aggregation and pseudo-labeling. As shown in Tab. [8] although its performance is lower than the full GOOD pipeline, it still surpasses GIPSO in closed-set accu- racy and remains competitive superior in open-set metries, indicating that meaningful adaptation achievable even without temporal aligmment. Collectively, these results confirm that GOOD is not strongly dependent on perfect ego poses: temporal cues improve performance when available, registration-baser alignment provides practical fallback, and the single-frame variant remains a robust solution in the absence of reliable pose information.
ad-
The reason for removing ground points that, generally, flat surfaces like roads or sidewalks are not considered OOD, as nearly all virtual source data contains these cate- gories. Our primary focus on rare OOD objects above the ground, which could pose significant safety risks (e.g., skateboards, wheelchairs).
Removal of Ground:
We present typical failure cases in Fig. 11 (1) Due to errors in the source model's predictions, our model's correction attempts result in lower confidence scores. Additionally, some edge points also exhibit low confidence. As shown in the red circle in Fig. a)a
Failure Cases:
a), the source model
incorrectly predicts manmade as vegetation, and GOOD misclassifies it as OOD while attempting to correct this error. (2) Due to pose prediction, some points within the same OOD object may be classified as ID, causing confusion, as shown in Fig. b)) (3) Some ID objects exhibit significant variation, leading to misclassification OOD. As shown in Fig. I c), vehicle is misclassified due its similarity to the unknown truck. In future work, we can explore methods such as edge loss and introducing a memory bank to mitigate these cases.
A.4 VISUALIZATION RESULTS
In Fig. and 10 we report additional adaptation visualization results of the GOOD, source, and ground truth in SynthLiDAR to SemanickIITI and Synth4D to nuScenes. We observe that our method significantly improves both close-set and open-set performances.
A.5 CLASS MAPPING
Due to the different annotations across datasets, follow GIPSO (aaloori et al. 2022 to remap semantic categories of SemanticKITTI (Behley et al.] 2019) and nuScenes (Caesar et al.
2020) into
seven standardized categories in Synth4D (Saltori 2l.22 to ensure consistent category defini-
22

---

Published as a conference paper at ICLR 2026
Table 20: Class mapping from SemanticKITTI to Synth4D.
Table 21: Class mapping from nuScenes to Synth4D.
SemoniicK111-11
SemanteKiVl-Nimm
SymthlD-Namm
SymtHlD-DD
MuSoenes-ID
euSoenei-Name
Symb4D- Name
Synth4D-ID
0
unlabelled
bicycle
car
unlsbelled
unlabelled
vehocle
unlahelled bicycle
unlabelled
milaseleed
monorcycle
unlabelled
mnlabelled
truck.
uniabelled
bus
mnlahelled
other vebicle
unlabelled
vehicle
person
pedestraa
comstrrationn-eehee
unlabelled
monoscyclli
bicycliu
unlabelled
matoreyele
pedestrian
pedestrian
unlahelled
porking
road
trallic-come
unlabelled
traillr
unlabelled
12
unlekelled
10
truck
unlabelled
13
builling
drivaalle-urrin
14
fence
manmade
unlabelled
15
vegetation
vegetation
16
tnni
vegetation
terrain
lerrain
18
19
17
termin pole tratfc sign
monmade
monmade
tormin
15
16
vegetation
manmadee
vegetation
manmade
tions. Tab. 20 and 21 show the class mapping from SemanticKITTI to Synth4D and nuScenes to Synth4D, respectively. Categories that do intersect with other datasets are considered unlabeled.
A.6 LARGE LANGUAGE MODELS ACKNOWLEDGMENT
We employed Large Language Models (LLMs) during the manuscript writing process to assist with and refine the writing. Specifically, LLMs were used to optimize the fluency and phrasing of the text, enhancing both linguistic accuracy and readability. Additionally, the models were leveraged to improve the structure of certain sections, contributing clearer overall logical flow of the paper.
23


Reviews:
[{"review_id": "Jh0PuGISYu", "reviewer": "Reviewer_owHE", "summary": "This paper addresses the challenging problem of open-set test-time adaptation (OSTTA) in 3D point cloud semantic segmentation, where models must adapt to unseen target domains containing both known and unknown categories. The authors identify a key limitation in existing OSTTA methods—most were designed for 2D image data and perform poorly on 3D point clouds due to geometric complexity and severe imbalance between in-distribution (ID) and out-of-distribution (OOD) samples. To address this, the paper proposes GOOD (Geometry-guided Out-of-Distribution Modeling), a novel framework that leverages geometric priors to cluster point clouds into superpoints, thereby transforming point-level imbalance into a structured representation. GOOD introduces (1) a Superpoint Representation Branch (SRB) with a Gaussian Mixture Model and prototype learning to distinguish ID and OOD superpoints, and (2) a Temporal Pseudo-label Branch (TPB) that enforces temporal consistency for reliable pseudo-label generation. The method is evaluated on four benchmarks, showing certain improvements over existing TTA and OSTTA baselines.", "questions": "1. The SRB uses a GMM on purity and entropy. Have you experimented with other methods to avoid the Gaussian assumption? If so, why was GMM chosen?\n\n2. The paper notes that prototypes are updated via EMA using pseudo-labeled superpoints, but does not quantify how prototype drift (caused by noisy initial pseudo-labels) affects long-term ID/OOD discrimination performance. Have you conducted experiments to measure prototype stability over consecutive test frames? Additionally, do you have strategies to mitigate drift, such as re-weighting pseudo-labels by superpoint confidence or removing outlier pseudo-labels before prototype update?\n\n3. The TPB relies on multi-frame alignment via ego poses to build temporal neighborhoods, but real-world pose noise may corrupt this alignment. Have you quantified GOOD’s sensitivity to pose error (e.g., testing with artificial pose noise added to frames)? Specifically, how does pose noise impact the accuracy of temporal pseudo-labels and final segmentation performance?\n\n4. Fig. 3 does not seem to be cited or mentioned in the main text. Perhaps a brief explanation can be given for it.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The paper presents a clear and well-motivated method that effectively extends OSTTA to 3D point clouds, with experimental validation and meaningful performance improvements.", "weaknesses": "1. The paper assumes access to reliable ego poses for temporal alignment, which might not hold in real-world scenarios—this assumption and its limitations are not discussed.\n\n2. Prototypes are updated via EMA using pseudo-labeled superpoints. If the initial pseudo-labels are noisy, prototypes may drift toward incorrect centroids and subsequently reinforce wrong ID/OOD assignments. The paper does not analyze prototype stability over time or propose safeguards (re-weighting, outlier removal, confidence gating).\n\n3. TPB depends on aggregating multiple frames via ego-pose alignment for K-NN temporal neighborhoods. In practice, pose noise (e.g., localization drift) or missing pose data will break temporal correspondence. The paper does not quantify sensitivity to alignment error nor propose how the method behaves when only single-frame inputs are available.\n\n4. Several design choices (EMA coefficient α, cosine similarity threshold τ, use of Dice loss for ID pseudo-label learning) are empirically reasonable but not theoretically motivated. This weakens understanding of when and why the method will succeed or fail.\n\n5. The paper mentions partitioning the point cloud into sub-regions and applying RANSAC to each, but does not specify sub-region division criteria or RANSAC hyperparameters. Only the original RANSAC literature is cited, and no task-specific settings for 3D point clouds are provided. This ambiguity makes the step impossible to replicate, which undermines the method’s practicality.\n\n6. Appendix A.3.5 details the performance of different superpoint confidence metrics via Table 11 and concludes that \"Purity+Entropy balances closed-set and open-set performance\", but the main text (Section 3.2.2) only defines the confidence formula \"C_k^sup=C_k^ent∙C_k^pur\" and does not cite the appendix’s comparative results to support why \"product\" is better than other combinations. This makes the main text’s formula choice lack empirical support, and readers need to flip to the appendix to understand the rationale, disrupting the logical flow of the argument.\n\n7. Eq.3 uses \"z_i^t\" to represent superpoint embedding, but the preceding text clearly defines \"z_k^t\" as the mean embedding of superpoint k—this confusion between \"i\" (point-level) and \"k\" (superpoint-level) makes readers unsure whether similarity is calculated per point or per superpoint.", "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": "leYoL1TieC", "reviewer": "Reviewer_sTXL", "summary": "This paper proposes to more effectively distinguish ID and OOD samples through indicators such as superpoint confidence and purity, as well as ID superpoint prototypes. Furthermore, it designs a temporal pseudo-label method to generate more accurate pseudo-labels. Experimental results show that it achieves optimal performance under the open-set TTA setting, which demonstrates the effectiveness of its method.", "questions": "1.Is it possible to add more experiments with different backbones to demonstrate the generality of the method?\n\n2.In Table 4, the increase in mIOU metric when adding more components is not significant. Could the reasons for this be analyzed?\n\n3.The impact of noisy samples be considered when constructing prototypes? This is quite common in TTA methods for 2D images.\n\n4.In Table 4, does \" superpoint representation (SR)\" refer to the use of superpoint purity and confidence in GMM? It should be explained in more detail.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1.This paper achieves a better distinction between ID and OOD samples through superpoint clustering and the utilization of superpoints, which is well demonstrated by visualization. Therefore, it exhibits strong innovation and rationality.\n\n2. \nThe paper's method is simple and efficient, and can be applied to many point cloud segmentation methods.\n\n\n\n3.The paper's experiments and visualizations are comprehensive, and the results all demonstrate the effectiveness of its method.", "weaknesses": "1.The paper reports the running time but fails to report the computational overhead such as GFLOPs.\n\n2.The definition of symbols in the formulas is rather confusing. For example, the definition of the subscript of y$_{j,c}$  in Eq. 1 is not clearly explained.", "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": "tEeVigP9z1", "reviewer": "Reviewer_XJDX", "summary": "This paper proposes GOOD, a framework for open-set test-time adaptation (OSTTA) in 3D point cloud semantic segmentation. GOOD addresses the imbalance between in-distribution (ID) and out-of-distribution (OOD) points by introducing two key branches: the Superpoint Representation Branch (SRB) and the Temporal Pseudo-label Branch (TPB). Experiments demonstrate that GOOD achieves superior results compared to existing methods.", "questions": "Please refer to the weakness part and address the concerns there.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper is clearly written, and the figures are effective in illustrating the technical flow.\n2. The design of SRB and TPB is technically sound: SRB clusters point clouds into superpoints and defines confidence metrics to better distinguish ID and OOD regions, while TPB enforces temporal consistency across sequential frames to produce more stable pseudo-labels.\n3. The experimental evaluation is extensive, providing strong evidence of the method’s effectiveness.", "weaknesses": "1. For 3D open-set segmentation, there are models such as OpenScene that leverage open-vocabulary queries to achieve open-set segmentation. A comparison with such models would be valuable. In particular, how would be the performance difference, and what are the differences and benefits of adopting an open-set test-time adaptation approach compared to the open-vocabulary segmentation paradigm?\n2. Missing related work. Several recent works on 3D segmentation are relevant and should be cited:\n   - OpenScene: 3D Scene Understanding with Open Vocabularies (CVPR 2023)\n   - Rethinking few-shot 3d point cloud semantic segmentation (CVPR 2024)\n   - Multimodality Helps Few-shot 3D Point Cloud Semantic Segmentation (ICLR 2025)\n   - Generalized Few-shot 3D Point Cloud Segmentation with Vision-Language Model (CVPR 2025)", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)