SMART-PC: Skeletal Model Adaptation for Robust Test-Time Training in Point Clouds
Authors: Ali Bahri
Moslem Yazdanpanah
Sahar Dastani
Mehrdad Noori
Gustavo Adolfo Vargas Hakim
David OSOWIECHI
Farzad Beizaee
Ismail Ben Ayed
Christian Desrosiers
Venue: ICML 2025 poster (2025)
Keywords: Point Cloud
Test Time Training
Skeletal Points
Primary area: deep_learning->robustness
TL;DR: SMART-PC enhances 3D point cloud classification under distribution shifts by learning skeletal representations that enable fast, robust, and real-time test-time adaptation.

Abstract: Test-Time Training has emerged as a promising solution to address distribution shifts in 3D point cloud classification. However, existing methods often rely on computationally expensive backpropagation during adaptation, limiting their applicability in real-world, time-sensitive scenarios. In this paper, we introduce SMART-PC, a skeleton-based framework that enhances resilience to corruptions by leveraging the geometric structure of 3D point clouds. During pre-training, our method predicts skeletal representations, enabling the model to extract robust and meaningful geometric features that are less sensitive to corruptions, thereby improving adaptability to test-time distribution shifts.
Unlike prior approaches, SMART-PC achieves real-time adaptation by eliminating backpropagation and updating only BatchNorm statistics, resulting in a lightweight and efficient framework capable of achieving high frame-per-second rates while maintaining superior classification performance. Extensive experiments on benchmark datasets, including ModelNet40-C, ShapeNet-C, and ScanObjectNN-C, demonstrate that SMART-PC achieves state-of-the-art results, outperforming existing methods such as MATE in terms of both accuracy and computational efficiency. The implementation is available at: \url{https://github.com/AliBahri94/SMART-PC}.

SMART-PC
Skeletal Model Adaptation for Robust Test-Time Training in Point Clouds
Ali Bahri 12 Moslem Yazdanpanah 12 Sahar Dastani 12
Mehrdad Noori 12
Gustavo Adolfo Vargas Hakim 12
David Osowiechi 12 Farzad Beizaee 12 Ismail Ben Ayed 12 Christian Desrosiers" 12
Abstract
Test-Time Training (TTT) has emerged as a promising solution to address distribution shifts in 3D point cloud classification. However, exist- ing methods often rely on computationally expen- sive backpropagation during adaptation, limiting their applicability in real-world, time-sensitive scenarios. In this paper, we introduce SMART- PC, a skeleton-basee based framework that enhances resilience to corruptions by leveraging the ge- ometric structure of 3D point clouds. During pre-training, our method predicts skeletal rep- resentations, enabling the model extract ro- bust and meaningful geometric features that are less sensitive to corruptions, thereby improving adaptability to test-time distribution shifts. Un- like prior approaches, SMART-PC achieves real- time adaptation by eliminating backpropagation and updating only BatchNorm statistics, result- ing in lightweight and efficient framework ca- pable of achieving high trame-per-secee rates while maintaining superior classification perfor- mance. Extensive experiments on benchmark datasets, including ModelNet40-C, ShapeNet-C. and ScanObjectNN-CC, demonstrate that SMART- PC achieves state-of-the-art art results, outperform- ing existing methods such MATE in terms of both accuracy and computational efficiency. The implementation is available at: https: /// github. com/ Aliiaarriii 94/SM//SSSMMMSS SMART-PC.
Figure I. The blue points represent the sampled points on the sur- face of spheres created using the skeletal points as centers and their corresponding radii. Each sphere illustrates local geometric structure defined by the skeletal representation.
2022; Zhang et al., 2022b; Bahri et al., 2025; Liang et al., 2024; Bahri et al., 2024a). However, these models often assume that the data distribution matches the training distribution, an assumption that rarely holds in real-world scenarios. Distribution shifts, caused by factors such envi- ronmental conditions or sensor inaccuracies in LiDAR data, can introduce distortions that degrade model performance (Ren et al., 2022: Sun et al., 2022). This vulnerability is particularly challenging in critical domains like autonomous navigation and robotics. As pre-training for all potential scenarios is impractical, robust methods for unsupervised test-time adaptation essential to address these shifts ef- fectively.
In Test-Time Training (TTT) for classification, an additional technique is employed during pre- pre-training with the source dataset, which can be leveraged during the adaptation phase. During adaptation, the network utilizes unlabeled target data dynamically adjust shifts in data distributions at test time. Recent TTT approaches in the 2D image domain have explored various strategies, such as entropy- based regular- ization, updating BatchNorm statistics, and self-supervised tasks (Liang et al., 2020; Wang et al., 2020; Mirza et al., 2022; Sun et al., 2020). However, these methods strug- gle when directly applied to 3D point clouds, highlighting the need for TTT techniques specifically designed for 3D data. In the 3D domain, notable work is the MATE frame- work (Mirza et al., 2023), which addresses the problem of TTT for 3D point cloud classification. MATE introduces a 3D-specific approach that leverages the self-supervised paradigm, where a network is adapted to onttof-distribution (OOD) target data by solving self-supervised task. MATE
1. Introduction
Recent advancements in deep learning have significantly improved the classification of 3D point clouds (Pang et al.,
'LIVIA, ÉTS Montréal, Canada. "International Laboratory on Learning Systems (ILLS). Correspondence Ali Bahri <ali.bahri. (eens.etttmi etsmttll..c.>
Proceedings of the = International Conference Machine Learning. Vancouver, Canada. PMLR 267, 2025. Copyright 2025 by the author(s).

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Skelctal Model Adaptation for Robust Test-Time Training in Point Clouds
utilizes masked autoencoder (MAB), where the network reconstructs a point cloud from its partially removed data. This reconstruction process enables adaptation, enhancing robustness to distribution shifts during test-time.
We achieve state-of-the-art results benchmark datasets, demonstrating the effectiveness of our ap- proach compared to existing methods, including MATE.
While MATE represents a significant advancement in TTT for 3D point clouds, it has critical limitations. MATE fo- cuses on reconstructing points on the surface of objects, making it highly sensitive to surface-level corruptions, such as noise, which frequently occur in world scenarios. This focus on surface points makes the features learned by MATE highly sensitive to corruptions, resulting in reduced resilience to distribution shifts. Additionally, MATE heavily depends on updating all network parameters during adap- tation. This approach reduces the model's speed, making it less practical for real-time applications due its lower Frames/Second.
2. Related Works
Test-Time Training. TTT enhances model adaptability by leveraging unseen target data during inference, unlike domain generalization or adaptation methods, which oper- ate solely during training. TTT methods can be broadly categorized into regularization-base and self-supervised approaches. Regularization-Be based methods include TENT (Wang et al., 2020), which minimizes prediction entropy by adapting BatchNorm parameters, and SHOT (Liang et al., 2020), which combines entropy minimization with diversity regularization develop robust feature extraction. MEMO (Zhang et al., 2022a) applies data augmentations to test in- puts and minimizes the entropy of averaged outputs, im- proving robustness. DUA (Mirza et al., 2022) updates BatchNorm statistics to address distribution shifts with min- imal overhead, while T3A (Iwasawa & Matsuo, 2021) re- fines the linear classifier using secuoo-rototypes. achieving backpropagation- free adaptation. These methods highlight the diversity of TTT strategies for addressing distribution shifts.
In contrast, our method overcomes these limitations by intro- ducing a akeleton-based framework for TTT. By capturing the essential geometric structure of 3D point clouds, skele- tons enable the model to Iearn powerful and meaningful fea- tures that are inherently less sensitive to corruptions. This is achieved through the abstraction property of skeletal repre- sentations (Lin et al., 2021), which simplifies the shape into a compact form, filtering out high-frequency noise and local distortions. As result, the model focuses on the underly- ing geometric structure rather than surface-level variations, enhancing robustness to distribution shifts. By leveraging these robust features, our approach eliminates the need for backpropagation during adaptation, significantly improving speed. Instead, following prior studies (Nado et al., 2020; Li et al., 2018), we update only the BatchNorm statistics, which effectively adapts the model to distribution shifts without modifying its learned weights. This efficiency demonstrates that skeleton- based pre-training inherently equips the model with features that are more resilient to corruption compared to MATE, providing a lightweight and effective solution for TTT. As shown in Figure 1, the skeletal representation cap- tures the geometric structure of point clouds by abstracting them into compact and meaningful features. Our method not only overcomes the limitations of MATE but also introduces a paradigm shift in TTT by emphasizing the importance of meaningful feature extraction during pre-training, ensur- ing robustness and adaptability with minimal computational effort. Our main contributions are:
Among self-supervised approaches, TTT+ (Liu et al., 2021) introduces an additional sulf-supervised branch that leverages contrastive learning within the source model to facilitate adaptation the target domain. Another method, TTT-MAE (Gandelsman et al., 2022), employs Masked Autoencoder (MAE) to address the one-sample learning problem effectively, demonstrating improved performance across various visual benchmarks. A recent method, MATE (Mirza et al., 2023), is the first TTT framework specifically tailored for 3D point cloud data, enhancing the resilience of deep networks to distribution shifts during testing. It lever- ages a MAE objective, masking a significant portion of each target point cloud and tasking the network with reconstruct- ing the complete structure before classification. In addition to these approaches, several works on test-time adaptation have explored updating model parameters during inference to handle distribution shifts effectively (Wang et al., 2024; Bahri et al., 2024b).
Skeletal Representation. Skeletal representations are a compact and structural abstraction of 3D point clouds, cap- turing the underlying geometric and topological properties of objects. These representations simplify complex point cloud data by reducing it to a set of key skeletal points that capture the core of the geometry (Figure 1), which are particularly useful for tasks such as shape analysis, object recognition, and reconstruction. Several methods have been proposed to generate skeletal representations from 3D data.
We propose SMART-PC, a skeleton-based framework for TTT that enhances robustness by extracting geo- metric features that are less sensitive to distribution shifts.
Our method achieves test-time adaptation without back- propagation, relying solely on updating BatchNorm statistics, making it highly efficient for real-time appli- cations.
2

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Skelctal Model Adaptation for Robust Test-Time Training in Point Clouds
Early approaches often relied on medial axis transformations (MAT) (Choi al., 1997) or Voronoi-based (Ogniewicz & Ilg, 1992) methods to extract the central skeleton of an ob- ject. While effective in capturing global geometry, these methods are typically computationally intensive and sensi- tive to noise (Li et al., 2015; Sun et al., 2015; Yan et al., 2018). More recent learning-based approaches, such as Point2Skeleton (Lin et al., 2021) and Learnable Skeleton- Aware 3D Point Cloud Sampling (Wen et al., 2023), have shifted towards using neural networks to predict skeletal points directly from input point clouds. These methods lever- age local geometric features and end-to-end optimization to produce accurate and robust skeletal representations. By capturing the core geometric structure of 3D point clouds, skeletal representations provide a compact and meaning- ful abstraction that is less sensitive to noise and distortions compared to raw point cloud data. This robustness makes them particularly suitable for dynamic adaptation scenarios, where models need to generalize effectively to unseen con- ditions. In this paper, we leverage skeletal representations with technical enhancements that enable the network to Iearn robust geometric features, thereby eliminating the need for extensive parameter updates during Test-Time Adaptation (TTA). Instead, our method relies solely lightweight ad- justments, such as updating BatchNorm statistics. This ap- proach ensures both efficient and effective TTT while fully utilizing the inherent strengths of skeletal representations.
center Ce. captures the local geometric features around cp These neighborhoods provide localized context, allowing the network to efficiently process the point cloud by fo- cusing on both global structure (via the centers) and local geometric details (via the neighhors). This hierarchical tok- enization forms the basis for subsequent feature extraction and skeletal prediction.
With the point cloud tokenized into centers and their local neighborhoods, the next step is to define the skeletal repre- sentation. The skeleton of 3D shape provides a compact representation of its intrinsic geometric structure. It ab- stracts the shape by representing it as a collection of skeletal points and their corresponding radii, which together form a skeletal mesh. This concept was introduced by (Lin et al., 2021) for tearning-based approaches. A skeletal mesh is defined discrete set of skeletal spheres. Each sphere is represented as:
s= (co, ER
(3)
where C, E ERo is the center of the skeletal sphere, referred to as the skeletal point, and r(cg) E E is its associated radius, representing the distance from the center to the surface of the original 3D shape. The skeletal mesh is constructed by connecting the skeletal spheres using two main elements. First, edges, denoted as egy = Cy). connect two skeletal points o; and cy, forming the basic linear structure of the skeleton. Second, faces, denoted as Sijk (Cap Cays CsyyC,)) ) are triangular connections between three skeletal points that capture 2D non- manifold surfaces within the mesh.
3. Method
3.1. Preliminaries
A point cloud is represented P E RNX3, where N is the total number of points, and each point is defined by its 3D spatial coordinates (x. y, 2). This representation captures the geometric structure of 3D objects, but due to the irreg- ular and unordered nature of point clouds, processing all N points can be computationally expensive and redundant. To reduce redundancy and focus on the most representative points, we apply Farthest Point Sampling (FPS). This tech- nique selects a subset of M points from the original point cloud as centers, denoted as:
The skeletal representation possesses two key properties. The first is recoverability, where the skeletal mesh acts as complete descriptor of the shape. This means the original 3D shape can be reconstructed by interpolating the skeletal spheres. For skeletal sphere (Ca, ((x)), any point p its surface can be expressed as:
p = + r(cs) U,
(4)
where e is a unit vector specifying the direction. The second property is abstraction, by which the skeleton simplifies the representation of the shape by capturing its fundamen- tal structure, significantly reducing the complexity of the original point cloud while retaining its essential geometric information. This discrete skeletal representation approxi- mates the Medial Axis Transform (MAT), which is a con- tinuous formulation of the skeleton. Unlike the MAT, the skeletal mesh is more robust to boundary noise and pertur- bations, making it suitable for learning-basedd approaches. By predicting skeletal points and their radii, the skeletal representation provides a robust and efficient way to ana- lyze and process 3D shapes, even under noisy or corrupted conditions.
C' = FPS(P) E ER Mx3
(1)
where M < N. The FPS algorithm ensures that the se- lected centers are evenly distributed across the point cloud, preserving the overall geometric structure. For each center point G E C, we construct its local neighborhood by select- ing its K nearest neighbors from the original point cloud P using the K-Nearest Neighbors (KNN) algorithm. This results in a neighborhood tensor:
Procat (C, P) E R MXKx3
(2)
where K is the number of neighbors for each center point. Each neighborhood Piocat [i] E R Kx3, corresponding to a
3

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Skelctal Model Adaptation for Robust Test-Time Training in Point Clouds
Adaptation with Cakel
Encoder
Decoder
MLP:
Adaptation with Statistics y/
+
Training
MLP
Encoder
Decoder
MLPr
Patchification
Chair
Leamable weights
Car
Frozen weights
Classifier
Patch
Figure 2. An overview the SMART-PC framework. The framework integrates skeletal prediction and classification tasks, leveraging skeletal representations extract robust geometric features. During online adaptation, strategies employed: adaptation with the skeletal loss Caket and backpropagation, and lightweight, backpropagation-iie approach that updates only BatchNorm statistics.
3.2. Overview
The framework is divided into two key branches: the skele- tal branch for predicting skeletal representations and the classification branch for predicting class labels.
In this paper, propose novel skeleton-basee framework for Test-Time Training (TTT) designed to improve the ro- bustness and adaptability of 3D point cloud classification under distribution shifts. Our method consists of three com- ponents: Framework, Training, and Test- Time Adaptation (TTA).
Skeletal Branch. The skeletal branch predicts skeletal points R and their corresponding radii R RRx11 from the input point cloud. The encoder, denoted as E, processes the tokenized point cloud Piocal E MxX x3 to extract global and local geometric features:
In the Framework component, we define the structure of the model, which includes components for both classification and skeletal point prediction. This involves investigating the architectural details and ensuring the network is capable of handling both tasks effectively. During the Training phase, the model is trained on source data perform two tasks: skeletal prediction and classification. The skeletal-bas based pre-training enables the model to learn robust and meaning- ful geometric features that enhance resistance to corruptions and generalize effectively across varying distributions. In the TTA phase, our method eliminates the need for back- propagation when the adaptation strategy is online, meaning the model is not reset after each epoch. Instead, it relies solely on lightweight updates to the BatchNorm statistics, enabling the model adapt dynamically to target data while achieving higher frame rate for real-time applications. The overall network architecture is illustrated in Figure 2.
Fene = E(Piocat) Mxd
(5)
where d is the dimensionality of the feature space. The encoder captures both local geometric details and global structural information.
The decoder, denoted D, refines the encoded features produce contextually enriched representation:
Faee D(Fenc) RR
(6)
To predict skeletal points and radii, two separate Multi- Layer Perceptrons (MLP, and MLP,) are applied to the refined features Fdec- The skeletal points are predicted as:
C = MLP MLP. (Faec) E R Mx3
and the radii are predicted as:
3.3. Framework
== MLP, Mx1
(8)
Our framework is designed to perform two primary tasks: skeletal point and radius prediction, and point cloud classifi- cation. These tasks share unified encoder while leveraging separate network components for their specific objectives.
The skeletal branch leverages these predictions to model the underlying geometric structure the input point cloud. Unlike existing skeletal prediction methods (Lin et al., 2021;

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Skelctal Model Adaptation for Robust Test-Time Training in Point Clouds
Wen et al., 2023), which rely convex combinations of in- put points to generate skeletal points, our approach directly predicts the skeletal points and radii from the refined fea- tures Feec. Convex combination methods inherently inject raw point cloud data into the prediction process, making the task easier for the network. However, this dependency on the raw point cloud can hinder the encoder's ability to learn robust and meaningful geometric features, especially in the presence of noise or distribution shifts.
input points P:
Ly2s
Σ
min $ES
lp coll2 - r(ca)
PEP
Σ
min
||e, p112 - -n(rrrcc)))
(II)
PEP
where cs and r(c,) again denote the center and radius of skeletal sphere
Classification Branch. The classification branch uses the shared encoder to extract features for class prediction. To enhance the classification performance, the features from the encoder Fece and decoder Fdce are combined by a sum:
2) Sampling Loss. To further ensure alignment between the skeletal spheres and the input point cloud, the Chamfer Distance between sampled points on the surface of skeletal spheres and the input points is calculated:
F Poombined FFnne + Pdce-
L sampling
Σ min Up - 4|12 + Σ min || IE
Emin EET PEP
pl12, (12)
PEP
This combination allows the classification head leverage high-level features from the decoder that are closely related to the skeletal points, alongside the global context from the encoder. By incorporating these skeletal-related fea- tures, the model enhances its ability to classify point clouds, particularly by utilizing structural information that aids in distinguishing complex or similar classes. The combined features are passed through the classification head, which consists of multiple MLP layers, normalization, and dropout layers, to predict class probabilities:
where T represents points sampled uniformly the faces of the skeletal spheres, This loss aggregates geometric information from the neighborhood of input points, effec- tively reducing high-frequency noise. As each skeletal point represents the local center of a region in P. the influence of individual noisy points is averaged, resulting 8 being less sensitive to corruption.
3) Radius Regularization Loss. caused by overly small radii due to noise, regularization term that encourages larger radii is also used:
To avoid the instability
p = Softmax Softman(MMPPas (Fonmbined)) E B
(10)
Cradius Er(es) SES
(13)
where K is the number of classes.
Furthermore, the radii r, predicted alongside Cs. capture the extent of each skeletal point's influence. This loss en- courages larger radii, reducing sensitivity to localized noise by ensuring that each skeletal sphere represents a broader region of the point cloud. This abstraction minimizes the effect of outliers, further enhancing robustness.
3.4. Training
In our framework, the skeletal branch is trained in a self- supervised manner since no labels are available for skeletal points, while the classification branch is trained in a super- vised manner using labeled source data. To optimize these branches, we adopt loss functions inspired by prior works in skeletal representation (Lin et al., 2021; Wen et al., 2023). Below, detail the loss functions used for training the skeletal and classification branches.
The total skeletal loss weighted combination of these three terms:
Laket Ly2s A Samplling + d Cradiius
(14)
Skeletal Losses. The skeletal branch optimizes three com- plementary loss functions to ensure accurate prediction of skeletal points and their corresponding radii. Unlike existing skeletal methods that operate at the point level, our approach predicts skeletal points and radii at the patch level. For each patch, the model prediets the center of skeletal sphere and its radius, which together abstract the local structure of the point cloud.
where A 2 0 and A2 2 0 are hyperparameters balancing the contributions of each loss.
Classification Loss. For the classification branch, we train the network using labeled source data. The features from the encoder and decoder are combined as described earlier and passed to the classification head. The classification loss is defined as the cross-entropy between the predicted probabilities and the ground truth labels:
1) Point-to-Sphere Loss. This loss ensures that input points lie on the surface of their corresponding skeletal spheres and that skeletal spheres align closely with their associated
B
K
1
Lets
Vik log(ūn),
(15)
B
i=1 Ru
5

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Skeictal Model Adaptation for Robust Test-Time Training in Point Clouds
where B is the batch size, K is the number of classes, Vik is the ground truth label for the i-th sample in class k, and Vis is the predicted probability for the same class.
4. Experiments
FPS vs Accuracy for SMART-PC and MATE
The overall loss for training the network is a combination of the skeletal and classification losses:
SMART-PC
MATE
48
Real-Time Thresheld FPS)
(N-18)
(2224)
46
Liotal = Lakee + Lels-
(16)
Accuracy (%)
This joint optimization allows the model to learn robust skeletal representations in a self-supervised manner while simultaneously leveraging labeled source data for classifica- tion.
40
38
[N-1a (N-44)
3.5. Test-Time Adaptation (TTA)
10
15
20
25
30
Frames/Second (FPS)
In this work, we explore two modes of test-time adaptation, inspired by prior works such as MATE (Mirza al., 2023): online adaptation and standard adaptation. Both modes aim to adapt the model to corrupted target data during test time but differ in how the model state is managed across epochs and corruptions.
Figure 3. Frames/Second vs. Accuracy for SMART-PC and MATE during online adaptation on the ScanObjectNN dataset. SMART PC achieves higher frame rates and accuracy. surpassing the real- time threshold (30 Frames/Second) in the (N-24) setting.
Online Adaptation. In the online mode, the model is not re- set after each epoch, allowing the accumulated information from previous batches influence the adaptation process. However, the model is reset the beginning of adaptation for each new corruption type. Initially, we perform test-time adaptation in backpropagation- free manner, updating only the statistical parameters of the Batch Normalization (Batch- Norm) layers (i.e., the running mean and running variance). This approach leverages the observation that the features extracted by the model during pre-traine pre-training on source data are highly robust and less sensitive to corruption. By updat- ing only the BatchNorm statistics, the model dynamically adjusts to the target data, achieving higher frame rates for real-time applications while maintaining high performance.
In this section, we present comprehensive evaluation of our proposed method using multiple 3D point cloud datasets. To thoroughly examine its robustness and generalization capabilities, we conduct experiments on three benchmark datasets: ModelNet4O-C (Sun et al., 2022), ShapeNet-C (Chang et al., 2015), and ScanObjectNN-C (Uy et al., 2019). These datasets encompass a variety of real-world challenges, including different levels of corruption and noise, enabling us to showcase the effectiveness of our approach in diverse and complex scenarios. Furthermore, we evaluate the per- formance of our method in both online and standard modes. For the online mode, we compare the backpropagation-fi free strategy with backpropagation-bet based adaptation to highlight the efficiency and robustness of our approach. A detailed description of the dataset, additional experiments, expanded visualization results related to skeletons, and a comprehen- sive evaluation of our method across all datasets, including accuracy metrics for each corruption type, are provided in the Supplementary Materials.
To further analyze the effectiveness of the extracted features, we conducted an additional experiment where all model pa- rameters, along with the BatchNorm statistical parameters, were updated during test-time adaptation. Specifically, we optimized the skeletal loss functions Cy2s. sampling. and frrdius (as described in Section 3.4) to adapt the skeletal representation to the target distribution.
4.1. Implementation Details
Standard Adaptation. In the standard mode, the model is reset after every batch, meaning each test batch adapts inde- pendently without accumulating information from previous batches. In this case, updating only the BatchNorm statistics has limited effectiivness since the adaptation does not retain context across batches. Consequently, for standard adapta- tion, we update all the parameters of the model using the skeletal loss functions Ly2s, cemplingg and Lyadins This allows the model to adjust more comprehensively to each batch of corrupted data.
For both training and adaptation, we employed the Point- MAE backbone, following the settings outlined in the MATE paper (Mirza et al.. 2023) to ensure a fair and con- sistent comparison. The batch size is set for both adap- tation modes, with 1 iteration for the online mode and 20 iterations for the standard mode, identical to the MATE paper for fair comparison once again. The optimizer and learning rate are identical to those used in the MATE paper. For augmentation, scale- coale--rannferii is used during pre-training, similar to MATE, During adaptation, however, MATE em-
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Skelctal Model Adaptation for Robust Test-Time Training in Point Clouds
Table Topp- Classification Accuracy (%) for the ModeINet-40C, SeanOhjectNN-C and ShapeNet-C datasets. Differences between
SMART-PC variants and MATE counterparts are indicated as (# X3).. denotes reproduced results, denotes adaptation without backpropagation, and denotes diffusion--aat based lest-time adaptation methods.
ModelNet-40CC
SeanObjectNN-C ShapeNet-C
Org-SO"
54.0
37.0
61.3
MATE-SO*
54,4
34.5
56,5
SMART-PC-SO
61.7 (+7.%)
38.7 (+4.25)
64.5 (48.05)
DUA (Mirza et al., 2022)
54.7
38.7
60.8
TTT-Rot (Sun al., 2020)
53.0
60.9
SHOT (Liang et al., 2020)
26.6
29.6
36.2
T3A (Iwasawa & Matsuo, 2021)
55.7
54.4
TENT (Wang et al., 2020)
26.5
27.7
37.4
CloudFixe-SStnddann (Shim et al., 2024)
68.0
CloudFixer- Ouline (Shim et al., 2024)
77.2
DDA-Stande -tandard (Gao et al., 2023)
68.1
"WA-Standard" (Bahri et al., 2024b)
57.1
37.4
50.5
MATE-SEN AATE-Standard (Mirza et al., 2023)
63.0
36.9
63.1
SMART-PC PC-Standard
63.1 (+0.1%)
39.6 (+2.7%)
64.4 (+1.3%)
BFTT3D* (Wang et al., 2024)
$7.2
33.0
60.7
MATE-Online" (Mirza et al., 2023)
70.6
36.9
69.1
SMART-PC-Oniln
70.8
46.7
65.9
SMART-PC-Online
72.9 (-2.3%)
47.4 (+10.5%)
67.1 (-2.0%)
ploys random masking, whereas we use random rotation in all experiments. All experiments were conducted using single NVIDIA A6000 GPU, ensuring consistency across all tested configurations. Additionally, we evaluate the frames per second rate of our method and compare it with MATE.
For additional comparisons with other methods, including diffusion-basee approaches, please refer to the Supplemen- tary Materials.
4.2. Main Results
Real-Time Adaptation. Figure 3 illustrates the adapta- tion performance of SMART-PC and MATE in terms of Frames/Second on the ScanObjectNN-C dataset. The adap- tation strategy employed is online, with a batch size of and a single iteration per adaptation step. The terms "N- 48" and "N-24" represent the number of created batches, which are generated differently for the two methods: MATE uses random masking, while SMART-PC employs random rotations.
In all results tables, "Org-SO" refers to the evaluation of a naive pretrained model, without any additional pre- training branch, on the corrupted dataset without adaptation. "MATE-SO" represents a model pretrained with a recon- struction branch, evaluated on the corrupted dataset without adaptation. "SMART-PC-SO" denotes model pretrained with a skeleton branch clean data, evaluated on the cor- rupted dataset without adaptation. Results marked with indicate reproduced outcomes, and "t" refers to adaptation using BatchNorm statistical parameters without backpropa- gation.
A significant advantage of SMART-PC is its ability to per- form adaptation in online mode without requiring backprop- agation. By updating only the statistics of BatchNorm layers, SMART-PC achieves significantly higher Frames/Second compared MATE, which relies on computationally ex- pensive backpropagation during adaptation. As shown in Figure 3, reducing the number of batches from N-48 to N- 24 does not affect the accuracy of either method. However, MATE cannot increase its Frames/Second rate due to the in- herent limitations of backpropagation. In contrast, SMART- PC demonstrates a substantial increase in Frames/Second, surpassing the real-time threshold of 30 Frames/Second when fewer batches are used. This highlights the efficiency and scalability of SMART-PC, making it practical solution for real-world applications requiring real-time adaptation.
ModeINet-4OC. Table 1 presents the top-1 classification accuracy on the ModelNet40-C dataset under various cor- ruption types. In the source-only setting, SMART-PC-SO achieves an average accuracy of 61.7%, significantly out- performing both MATE-SO 53.7%) and Org-SO (54.0%). This demonstrates the robustness of our skeleton- based pre- training in handling distribution shifts without adaptation.
For adaptation strategies, in the standard mode, SMART- PC-Standard achieves average accuracy of 63.1%. higher than MATE-Standard (63.0%). In the online mode, SMART-PC-One attains the highest average accuracy of 72.9%, compared to 69.6% for MATE-Online. Notably, SMART-PC-Onlinei leverages a backpropagation-fiee strat-
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Skelctal Model Adaptation for Robust Test-Time Training in Point Clouds
The Effect of Batch Size Accuracy
Accuracy of Methods and Without Augmentation
50.9
47.36
46.8
40.3
Accuracy
71
07.-
37.01
34.48 225
300
0r
MATE-S0
MAPE
SMARSPO:00
SMAAEIRG Wa Ard
SM SMAMP sin
SHART PC W.N
SHARDIC WIO Piets
32
64
128
Different Methads
Batch Size
Figure 4. Effect of Augmentation During Adaptation in Our Figure 5. Impact of Batch Size on Mean Accuracy for the ModelNet- C Dataset in Standard Mode.
Method and Comparison with Other Methods on the ScanOb- jectNN Dataset in Online Mode.
egy, which enables efficient adaptation while maintaining superior performance. These results highlight the effec- tiveness and scalability of SMART-PC in both source-only (without adaptation) and adaptive settings under challenging conditions.
4.3. Ablation Study
Batch Size. We evaluate the effect of batch size method's performance using the ModelNer40-C dataset in standard adaptation mode, where all parameters are updated, with 20 iterations as in the MATE paper. As shown in Figure 5, increasing the batch size improves accuracy, from 67. 94. at batch size 8 to 73.42% batch size 128. This improvement showcases the effectiveness of larger batch sizes for achieving higher accuracy. To ensure fairness in comparison with MATE and other methods and to avoid increasing computational costs, we used a batch size of I for all main results in both standard and online modes.
ShapeNet-C. Table summarizes the top-1 classification accuracy the ShapeNet-C dataset under various corrup- tion types. In the source-only setting, SMART-PC-SO achieves an average accuracy of 64.5%, outperforming both MATE-SO (56.5%) and Org-SO (61.3%). This result high- lights the effectiveness of our skeleton- based pre- pre-training in generalizing to unseen corruptions without adaptation.
NcaNObjectNN-C. Table presents the top-1 classification accuracy across 15 corruption types on the ScanObjectNN-C dataset. In the source-only setting, SMART-PC-SC achieves an average accuracy of 38.7%, significantly outperforming both MATE-SO (34.5%) and Org-SO (37.0%).
Augmentation. Figure illustrates the impact of differ- ent augmentations and the absence of augmentation on our method during adaptation. This experiment was conducted the ScanObjectNN dataset in online mode with batch size 1 and iteration 1. Consistent with the MATE paper, we created 48 batches using random rotations to introduce diver- sity among the data. Even without augmentation (SMART- PC W/O Aug), our method achieves a 46.42% accuracy, outperforming MATE (36.93 (3%) and demonstrating the ro- bustness of our skeleton-based framework. Applying ran- dom rotations (SMART-PC With Rot) increases accuracy 47.36%. Other augmentations, including horizontal flipping (SMART-PC With H-Flip) and translations (SMART-PC With Trans), achieve comparable improvements, with accuracies of 46.8% and 47.08%, respectively.
In the standard adaptation mode, SMART-PC-Standann achieves an average accuracy of 39.6%, slightly higher than MATE-Standail (36.9)). This improvement highlights the effectiveness of leveraging skeletal representations for enhancing robustness to distribution shifts.
In the online mode, SMARR-PC--nline achieves the high- est average accuracy of 47.44444 47.4%, significantly outperforming MATE-Online (36.9%). Furthermore, SMART-PCt, which utilizes a backpropagatiin free adaptation strategy by updat- ing only the BatchNorm statistics, achieves a competitive ac- curacy of 46.7%. The efficiency of the backpropagation-freet strategy highlights its practicality for real-world applications requiring high Frames per Second.
Feature Summation. We conduct an ablation study on the impact of summing the features from the shared encoder, used for both skeletal and classification tasks, with the de- coder features. This summation improves SMART-PC's accuracy from 62.4% to 63.1 % on the ModelNet-C dataset in standard mode, compared to using only the encoder fea- tures for classification.
Overall, these results demonstrate that SMART-PC con- sistently achieves state-of-the performance across adaptation modes, with notable gains under challenging real-world corruptions, highlighting its robustness and gen- eralization capability.
Skeleton Loss Coefficients. To further evaluate the con- tribution of each loss component in our skeleton-basee pre-
8

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Skelctal Model Adaptation for Robust Test-Time Training in Point Clouds
Pr2Sphere
Sampling
RadiusReg
Source Acc(%)
Corrupted Acc(%)
1.0
1.0
0.0
67.82
0.0
1.0
1.0
91.6
67.79
1.0
0.0
1.0
91.6
67.80
1.0
1.0
1.0
91.2
72.84
0.3
1.0
0.4
91.3
72.95
Table Ablation study of skeleton loss coefficients on ModelNer40 and ModeINet4O-C (online adaptation). The best configuration corresponds to the original coefficients from the Point2Skeleton paper.
Dataset
Org-SO
MATE-SO
SMART-PC-SO
ScanObjectNN-C
33.00
33.22
35,90
ModeINet4O-C
57.16
54.71
65.25
ShapeNet-C
60.73
53.07
62.24
Table Mean accuracy (%) of BFTT3D using different pretrained models under the hackpropagation-free setting. SMART-PC-SO achieves the best results across all datasets.
training, we conducted an ablation study using different coefficients for the Skeletal loss terms: point-to-sphere loss, sampling loss, and radius regularization. The experiments were performed on the ModelNet40 and ModelNet4O-C datasets under the online adaptation setting.
using the BFTT3D (Wang et al., 2024) adaptation method across three different pretraining strategics (Org-SO, MATE- SO, and SMART-PC-SO), Each pretrained model was eval- uated using the same BFTT3D adaptation strategy under the backpropagatton--ree setting. As shown in Table our SMART-PC-SO model consistently outperforms both Org-SO and MATE-SO across all three datasets. This pro- vides strong evidence that the skeletal decoder encourages the model to extract more structure- and corruption- resilient features, which support effective test-time adapta- tion without updating model weights.
As shown in Table 2, the best performance is obtained with the coefficient set (0.3, 1.0, 0.4). which corresponds to the original settings in the Point2kkeleton paper (Lin et al., 2021). This configuration achieves the highest accuracy of 72.95% on corrupted data (Corrupted Acc), confirming that each skeletal loss term contributes meaningfully to the learning of robust features under corruption. Additionally, as described in the main paper, the Radius Regularization Loss (Equation 6) is designed to avoid instability caused by overly small radii, especially under noisy conditions. This loss encourages the model to learn larger and more stable radii, which improves the robustness of the skeletal abstrac- tion. Although we do not observe excessively large radii, the Point-to-Sphere and Sampling losses (Equations 11 and 12) implicitly constrain radius size by preserving geometric consistency. As shown in Table 2, removing the regular- ization term leads to drop in performance, confirming its importance.
5. Conclusion
In this paper, we proposed SMART-PC, a skeleton-basee framework for robust and efficient test-time training of 3D point cloud models under distribution shifts. By leveraging skeletal representations, SMART-PC enhances robustness to corruptions while enabling high Frames per Second during online adaptation through a backpropagation-free strategy that updates only BatchNorm statistics. Experiments on benchmark datasets, including MedeINer40-C, ShapeNet-C. and ScanObjectNN-C, demonstrate state-of-the-art perfor- mance in both source-only and adaptation settings. Overall, SMART-PC provides scalable and practical solution for real-world applications requiring robust 3D point cloud clas- sification under challenging conditions. Future work may explore extending this framework to other 3D tasks, such as semantic segmentation, instance segmentation, and object detection.
Evaluating Pretraining Strategies in Backpropagation- Free Adaptation. Our method supports two adapta- tion modes: one with backpropagation and one that is backpropagation-free The goal of the backpropagation-free mode is to show that pretraining with skeleton- de- coder encourages the model to learn robust and meaningful geometric features. These features are resilient enough that, during test-time, simply updating the BatchNorm statistical parameters (i.c., running mean and variance) is sufficient to improve performance without performing maddient-based updates.
Impact Statement
This paper presents work aimed at advancing the field of Machine Learning. While there are several potential societal implications of our work, we believe that none of them need to be specifically highlighted in this context.
To validate this effect, we conducted additional experiments

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Skelctal Model Adaptation for Robust Test-Time Training in Point Clouds
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Skeictal Model Adaptation for Robust Test-Time Training in Point Clouds
A. Implementation
Our approach was implemented using PyTorch, with the codebase organized into two main components: Pretrain and Adaptation.
Pretrain. We start with the initial pretraining phase of the base models (Point-MAE). In this phase, the backbone is pretrained with a classification branch in a fully supervised manner and a skeleton branch in a self-supervieed manner. The pretraining is performed on clean datasets such as ModelNet, ShapeNet, and ScanObjectNN, ensuring the models are well-prepared for the subsequent adaptation steps.
Adaptation. After completing the pretraining phase, we transition to the adaptation stage, which consists of two modes: online and standard. In the standard adaptation mode, all model parameters are updated using the skeleton loss, allowing the model to comprehensively adjust to the target data. In the online adaptation mode, we employ two distinct strategies. The first strategy involves adapting only the statistical parameters of the BatchNorm layers (e.g.. running mean and variance) without backpropagation. This approach significantly reduces computational costs, enabling our method to achieve higher Frames per Second and making suitable for real-time applications. The second strategy involves updating all model parameters using the skeleton loss, similar to the standard mode. These flexible adaptation strategies highlight the efficiency and scalability of our method, catering to both real-time and high-accuracy requirements.
B. Datasets
ModeINet-40C. ModeINet-4OC (Sun et al., 2022) serves as a robustness benchmark for point cloud classification, designed to evaluate the ability of architectures to handle real-world distribution shifts. extends the original ModelNet-40 test set by introducing 15 common corruption types, grouped into three categories: transformations, noise, and density variations. These corruptions simulate practical challenges like sensor errors and LiDAR noise, offering realistic assessment of model performance under diverse and challenging conditions.
ShapeNet-C.
ShapeNetCore-v2 (Chang et al., 2015) is widely used dataset for point cloud classification, containing
51,127 3D shapes spanning 55 categories. It is partitioned into three subsets: 70% for training, 10% for validation, and 20% for testing. To evaluate the robustness of models under real-world conditions, (Mirza et al., 2023) augmented the test set with types of corruptions, mirroring those ModelNet-40C. These corruptions, created using the open-source implementation from (Sun al., 2022), resulted modified version of the dataset known as ShapeNet-C.
ScanObjectNN-C.. ScanObjectNN (Uy al., 2019) is real-world dataset for point cloud classification, comprising 2,309 training samples and 581 testing samples across 15 categories. To assess robustness, (Mirza et al., 2023) applied 15 unique corruption types to the test set, using the approach described in (Sun et al., 2022). The resulting modified dataset is referred to as ScanObjectNN-C.
C. More Experiments
Comparison and Advantages of SMART-PC over BFTT3D in Backpropagation-Fire Mode. Unlike BFTT3D, which uses class-based prototypes from the source dataset during adaptation. our method relies solely the target dataset without accessing any source information. Another key distinction is efficiency: as shown in Table 4, our method in the backpropagation-tii mode achieves significantly higher FPS compared to MATE, SVWA, BFTT3D, CloudFixer, and DDA. In terms of performance, our method also outperforms BFTT3D across three corrupted datasets, as reported in Table Together, Table and Table demonstrate that our skeleton- based pretraining enables the model to learn robust
Method
FPS
DDA
0.04
CloudFixer
1.07
BFTT3D
6.83
SVWA
10.86 (for N.=2)
MATE
10.79
SMART-PC
59.52
Table 4. Comparison of inference speed (FPS) across different adaptation methods on the ModelNer4O-C dataset.
12

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Skelctal Model Adaptation for Robust Test-Time Training in Point Clouds
Clean Point Cloud
Comrupted Point Cloud (Success Case)
Compupted Point Cloud (Failure Case)
www.
ACC: 85.7
ACC: 80.4
ACC: 21.0
Figure 6. Visualization of clean point cloud and its corrupted versions. The predicted skeleton remains stable even under corruption. supporting accurate classification (ACC: 85.7 and 80.4 the clean and success cases, respectively). In the failure case, the corruption leads to misaligned skeletons and significantly lower performance (ACC: 21.0).
geometric features, allowing effective adaptation with only statistical parameter updates, achieving both high accuracy and fast inference.
Analyzing the Impact of BatchNorm Statistic Updates. Updating the running mean and variance in BatchNorm layers has been shown to improve robustness under covariate shift (Nado et al., 2020). Furthermore, the AdaBN (Li et al., 2018) paper supports the significance of this mechanism by stating that: "label related knowledge is stored in the weight matrix of each layer, whereas domain related knowledge is represented by the statistics of the Batch Normalization (BN)". This highlights that updating BN statistics is a meaningful and effective approach for handling domain shifts.
During pretraining, our method learns more abstract and robust features through the skeleton prediction branch. These features are resilient to corruption, such that during test-time adaptation, simply updating the statistical parameters of the BatchNorm layers (i.e., running mean and variance) can effectively suppress noise without requiring backpropagation.
As shown in Figure 6, our method maintains strong accuracy even under corruption. The clean input (left) yields high classification accuracy of 85.7%, while the corrupted input still achieves 80.4% in a successful case, demonstrating robustness. In contrast, a failure case (right) leads to significant misalignment, dropping accuracy to 21.0%. These results indicate that the learned skeletal representation acts as a noise-resistant abstraction, preserving semantic structure under moderate distortions.
To further analyze the effect of BatchNorm statistics update in our setting, we visualized the impact of distribution shift on the BatchNorm input and how updating the statistics help realign the model to the new distribution. In Figure 7, the Gaussian solid curves represent the statistics of the input data. As observed, the source distribution (blue) aligns well with the accumulated statistics from pre-training (black dashed curve), resulting in centered and scaled distribution with zero mean and unit variance. However, when facing a distribution shift (red) in the center column, the pre-training-timg -ime accumulated statistics no longer align with the corrupted target distribution, leading to an inconsistent input distribution to the subsequent compared what the model has seen during training (i.e., covariate shift). This misalignment is clearly visible in the center column as the distance between the red solid curve and the black dashed curve. By updating the BatchNorm statistics, the running mean and variance shift toward the target distribution. This helps mitigate the covariate shift introduced by the target domain (right column), moving the statistics (green dashed curves) closer to the data distribution (solid curves).
This pattern is consistent across different channels of both the first BatchNorm layer in the encoder (top row) and the classification head (bottom row). For each BN layer, channel input values are aggregated across batch samples and tokens, and plotted as histograms of the values. Similar to the input data statistics, the BN statistics curves are plotted as Gaussian curves, computed from the corresponding running mean and running variance.
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Skelctal Model Adaptation for Robust Test-Time Training in Point Clouds
Clean with Pretrained Statistics
Corrupt with Pretrained Statistics
Corrupt with Adapted Statistics
ch 8
ch
ch
ch
ch 64
ch
ch 96
104
112
-34
-32
-32
ch 8
ch
ch 40
ch
ch 72
ch
ch 96
104
Value
Figure 7. Distribution of the input (solid colors) and the running statistics (dashed curve) of BatchNorm layers across different channels. first BN layer in the encoder, Bottom: first BN layer in the classifier. Blue: clean source data, Red: corrupted target data, Solid Gaussian curve of data statistics, Black Dashed Curve: pretrained accumulated statistics, Green Dashed Curve: adapted statistics.
Top:
Curve:
D. Detailed Results
In this section, the performance our method is presented in Table 5, Table 6, and Table 7 showcasing detailed results for each corruption across all datasets. Our method consistently outperforms previous approaches in source-only (without adaptation), standard adaptation, and online adaptation modes. In most corruption scenarios, SMART-PC achieves higher accuracy compared to prior methods, demonstrating its robustness and effectiveness in handling distribution shifts across ModeINet40-C, ShapeNettCC, and ScanObjectNN-C. The improvements are particularly significant in challenging corruption types, highlighting the advantages of leveraging skeletal representations for test-time training.
E. More Visualizations
Skeletal Reconstruction. In Figure 8, present several 3D objects showcasing the original point clouds (blue dots) and their corresponding skeletal spheres. Each skeletal sphere is defined by skeleton point (the center of the sphere) and its associated radius, which represents the local geometric structure around the skeleton point. The spheres collectively capture the essential geometric and structural features of the objects.
This visualization demonstrates the capability of our model to learn meaningful and compact representations of 3D shapes through skeletal abstraction. The skeleton effectively captures the underlying structure while filtering out high- hig-frreyenccy
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Skelctal Model Adaptation for Robust Test-Time Training in Point Clouds
Table Top-1 Classification Accuracy (96) for all distribution shifts in the ModelNet4O-C dataset. All results based the PointMAE backbone trained on a clean training set and adapted to the OOD test set with batch size of 1. Results marked with indicate reproduced outcomes (adaptation only) using the pretrained models from the MATE GitHub repository. Results marked with *s indicate full reproduction from scratch, including both pretraining and adaptation. The symbol "I" denotes adaptation using only BatchNorm statistical parameter updates, without backpropagation.
ERINN
buck
impoll
upsann
rbz-inv
den-dec
dens-ine
shear
diston
ochioo
lidar
uni
Corruptions:
Avg.
Org-SO"
66.6
59.2
7.2
31.8
74.6
67.7
75.1
74.4
38.0
53.7
70.0
38.6
23.4
54.0
MATE-SO*
59.7
51.3
28.2
55.3
71.5
77.4
67.1
30.2
19.9
53.7
MATE-SO**
60.8
53.3
28.8
53.4
70.9
57.9
68.9
77.1
67.5
31.7
62.0
33,9
24.6
54.4
SMART-PC-SO
8188
79.5
13.6
65.4
84.3
75.7
77.8
62.0
65.9
73.4
42.7
69.1
73.8
36.3
24.4
61.7
DUA
65.0
58.5
14.7
48.5
68.8
62.8
63.2
62.1
66.2
68.8
46.2
64.7
41.2
36.5
54.7
ToT-Rot
61.3
58.3
34.5
48.9
66.7
68.6
55.2
27.3
54.6
40.0
29.1
$3.0
SHOT
29.6
28.2
25,4
32.7
30.3
30,9
31.2
32.1
22.8
27.3
29.4
20.8
18.6
26.6
T3A
64.1
62.3
33.4
65.0
75.4
63.2
66.7
57.4
63.0
72.7
32.8
54.4
67.7
39.1
18.3
55.7
TENT
29.2
28.7
10.1
25.1
33.1
30.3
29.1
30.4
31.5
31.8
22.7
27.0
28.6
20.7
19.0
26.5
MATR-Standard
69.8
61.8
18.9
63.9
72.5
74.0
80.8
71.0
36.7
38.4
29.9
58.9
MATE-Stundard
75.3
70.9
23.0
64.7
79.2
69.3
76.5
84.0
75.9
47.2
71.8
71.6
37.5
29.7
63.0
SMART-PC-Stundamt
82.4
80.1
12.0
67.1
84.5
76.0
78.6
67.3
72.9
73.3
43.9
72.6
73.5
37.4
24.8
63.1
MATE-Online*
80.6
79.5
20.7
71.5
82.6
78.1
80.7
78.1
86.6
79.6
54.9
78.4
77.4
45.4
49.6
69.6
MATE- *
82.1
78.3
29.6
74.1
84.3
86.7
78.8
55.2
79.1
49.8
70.6
SMART-PC-Online
85.0
83.2
31.6
77.6
85.9
79.2
80.8
77.8
79.3
77.8
60.3
80.6
76.8
45.2
40.4
70.8
SMART-PC-Online
85.4
84.0
49.4
79.7
85.7
80.1
81.3
81.7
82.7
78.3
60.5
82.6
76.7
44.4
41.8
72.9
Table 6. Top-1 Classification Accuracy (%) for all distribution shifts in the ShapeNet-C dataset. All results are based the PointMAE backbone trained on clean training set and adapted to outcomes, while "f" denotes adaptation using BatchNorm statistical parameters without backpropagation.
OOD test set batch size of 1. Results marked with indicate reproduced
ganss
backg
impul
upsam
obF-inv
den-dex
dens-ine
shear
distor
acision
Lddar
uni
1br
201
Corruptions:
Avg.
Org-SO"
77.4
71.8
8.6
54.4
77.9
75.5
76.0
85.3
76.5
80.5
57.1
85.1
76.0
11.0
7.1
61.3
MATE-SO*
69.7
63.3
2.1
50.6
71.1
70.2
72.1
85.9
77.8
75.6
44.0
85.4
70.3
7.0
3.1
56.5
SMART-PC-SO
80.6
78.5
11.4
61.3
81.6
81.1
81.5
84.9
74.4
81.1
64.1
85.0
79.9
11.8
10.0
64.5
DUA
70.1
14.3
60.9
80.0
83.8
77.1
$7.5
11.9
12.1
60.8
TTT-Rot
74.6
72.4
23.1
59.9
75.0
81.4
82.0
69.2
49.1
79.9
72.7
14.0
12.0
60.9
SHOT
44.8
42.5
12.1
37.6
45.0
43.7
44.2
48.4
49.4
45.0
32.6
46.3
39.1
6.2
5.9
36.2
T3A
70.0
60.5
6.5
40.7
67.8
67.2
68.5
79.5
79.9
72.7
42.9
79.1
66.8
7.7
5.6
54.4
TENT
44.5
42.9
12.4
38.0
44.6
43.3
44.3
48.7
49.4
45.7
34.8
48.6
43.0
10.0
10.9
37.4
MATE-Standard SMART-PC-Stannlee
77.8
74.7
4.3
66.2
78.6
76.3
75.3
86.1
86.6
79.2
56.1
84,1
76.1
12.3
13.1
63.1
80.8
78.9
8.9
60.4
81.8
81.1
81.7
84.8
78.4
80.8
63.7
84.9
79.8
11.5
8.8
64.4
MATE- *
81.5
78.6
40,9
75.9
79.7
84,9
85.9
81.8
70.8
14.2
16.6
69.1
SMART-PC-Onlinet
80.4
78.7
21.0
72.7
80.9
80.6
82.5
78.3
80.9
70.1
82.5
10.5
65.9
SMART-PC-Online
81.2
80.5
28.9
74.3
81.2
80.7
80.5
83.1
81.0
80.4
73.2
82.8
79.0
10.0
10.2
67.1
15

---

Skeictal Model Adaptation for Robust Test-Time Training in Point Clouds
Table Top-1 Classification Accuracy (%) for all distribution shifts in the ScamOhjeetNN-C dataset. All results based the PointMAE backbone trained on a clean training set and adapted to the OOD test set with batch size of 1. Results marked with indicate reproduced outcomes (adaptation only) using the pretrained models from the MATE GitHub repository. Results marked ** indicate full reproduction from scratch, including both pretraining and adaptation. The symbol "f" denotes adaptation using only BatchNorm statistical parameter updates, without backpropagation.
8AHIS
backge
impuul
upsam
rbr-inv
den-dec
den- dec
dens-ine mins-ime
shear
diston
oclsion
lidar
un
Corruptions:
Avg.
Org-SO"
21.7
18.8
16.9
18.4
22.2
46.0
47.0
72.1
69.4
48,9
35.6
73.0
49.4
6.7
9.3
37.0
MATE-SO*
20.3
32.2
18.9
21.2
20.5
35.6
36.7
69.9
66.6
38.9
28.7
70.4
39.4
8.3
9.8
34.5
MATE-SQ**
15.3
20.1
13.4
11.0
15.3
28.7
29.4
69.2
64.5
32.9
25.0
70.6
33.4
9.1
9.0
29.8
SMART-PC-SO
26.7
37.7
16.9
21.3
56.3
48.5
72.3
11.0
38.7
DUA*
30.5
40.1
10.2
23.6
29.9
43.7
46.1
68.3
66.3
48.5
38.9
68.7
48.4
8.6
8.1
38.7
SHOT*
30.2
34.1
16.2
22.6
22.6
32.4
32.1
45.5
45.0
34.5
29.3
47.8
36.2
7.1
8.1
29.6
TENT
29.5
31.6
17.6
24.8
27.2
32.4
40.7
35.0
30.2
26.6
36.6
29.3
10.5
12.4
27.7
MATE-Standard
27.5
29.4
14.3
22.2
25.6
40.8
42.0
73.7
63.2
45.1
35.3
73.3
45.3
7.1
9.3
36.9
MATE-Standurd*
22.9
31.5
13.4
17.0
24.3
34.1
35.5
69.5
63.7
39.1
28.4
68.8
38.7
7.7
6.2
33.4
SMART-PC-Standae
27.5
39.1
19.3
21.5
29.8
44,2
48.9
68.3
60.2
49.4
45.4
70.1
49.1
8.4
12.2
39.6
MATE-Online*
33.0
44.1
13.3
25.3
29.1
36.8
37.7
73.3
65.2
37.2
31.3
72.6
40.6
7.2
7.4
36.9
MATE-Online**
29.4
33.9
16.0
25.5
32.5
34.8
38.2
69.4
66.6
40.6
30.6
70.2
40.3
8.1
7.9
36.3
SMART-PC-Onling
39.4
54.6
19.6
40.1
40.3
54.4
55.9
73.3
69.5
55.1
50.1
74,9
57.8
6.9
7.9
46.7
SMART-PC-Online
42.3
53.7
23.8
37.5
41.6
57.0
57.0
71.1
68.8
57.3
53.4
72.1
58.2
8.4
47.4
Figure Visualization of objects with original point clouds (blue dots) and their corresponding skeletal spheres.
noise, enabling the model to focus on the fundamental geometry of the objects. The diversity of shapes in this figure-ranging from guitars and airplanes to furniture- the robustness of our approach generalizing across different object classes. These visualizations also provide insight into how the skeletal abstraction simplifies complex point cloud data into manageable representations, facilitating better performance in downstream tasks such classification and adaptation under challenging conditions. This compact representation ensures that the geometric structure is preserved, even when the original point clouds are corrupted or noisy.
16


Reviews:
[{"review_id": "Ium17SO23L", "reviewer": "Reviewer_k4Xv", "summary": "This paper proposes a test-time adaptation framework for point cloud recognition, establishing a novel self-supervised fine-tuning paradigm that utilizes Skeletal Representation as a pretext task. By predicting skeletal points and their corresponding radii, the method extracts noise-insensitive geometric features. The authors claim that their approach eliminates the need for backpropagation during adaptation, significantly reducing computational overhead. Extensive comparative experiments are conducted, and the algorithm is validated under diverse experimental conditions.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We appreciate the reviewer’s thoughtful feedback and their recognition of our contribution to pretext task design and its connection to prior work in test-time adaptation for point cloud recognition.\n\n**1. Theoretical Claims** \n\nTo clarify, **our method does not use labeled source data during the test-time training phase**. The classification loss discussed in Section 3.4 is only applied **during the pre-training phase** on the source dataset to learn the initial model parameters. During test-time training, only the target domain data without labels is used, and no source labels are required. Only the encoder and decoder are adapted at test time; the classifier is frozen (Figure 2). Furthermore, we emphasize that our method **strictly follows the standard definition of Test-Time Training (TTT)**, as established in prior works such as the MATE paper. \nWe acknowledge that our wording in Section 3.4 may have caused confusion, and we will clarify this distinction in the final version of the paper. \n\n***\n**2. Comparison with Recent Test-Time Adaptation Methods** \n\nThank you for the suggestion. As shown in **Table 1**, we repreduced **BFTT3D[2]** and **SVWA[3]** using our settings. Since SVWA produced very low results in our standard setting (MoodelNet40-C: 5.54% and ScanObjectNN-C: 21.79%), we used BS=4, It=1, and N_v=2 for adaptation with the pretrained model available on their GitHub repository. Diffusion-based methods (**CloudFixer[1]**, **DDA[4]**) are reported separately due to slow Frame/Second (see **Table 2**). Due to time constraints, we were unable to reproduce them during the rebuttal period.\n\n[1] CloudFixer: Test-Time Adaptation for 3D Point Clouds via Diffusion-Guided Geometric Transformation\n\n[2] Backpropagation-free network for 3d test-time adaptation\n\n[3] Time Adaptation in Point Clouds: Leveraging Sampling Variation with Weight Averaging.\n\n[4] Diffusion-driven adaptation to test-time corruption.\n\n### Table 1: Top-1 Accuracy (%) Comparation With More Recent Methods (🆕)\n\n|Method|ModelNet40-C|ScanObjectNN-C|ShapeNet-C|\n|-|:-:|:-:|:-:|\n|**Source-Only**|\n|Org|54.0|37.0|61.3|\n|MATE|53.7|34.5|56.5|\n|**SMART-PC**|**61.7**|**38.7**|**64.5**|\n|**Diffusion**|\n|🆕CloudFixer-Standard|68.0|-|-|\n|🆕CloudFixer-Online|77.2|-|-|\n|🆕DDA-Standard|68.1|-|-|\n|**Standard**|\n|🆕SVWA|57.1|37.4|50.5|\n|MATE|58.9|36.9|63.1|\n|**SMART-PC**|**63.1**|**39.6**|**64.4**|\n|**Online**|\n|🆕BFTT3D|57.2|33.0|60.7|\n|MATE|69.6|36.9|**69.1**|\n|SMART-PC†|70.8|46.7|65.9|\n|**SMART-PC**|**72.9**|**47.4**|67.1|\n\n\n### Table 2: Frame per Second Comparison (FPS), N_v is the number of sampling variations.\n|Method|FPS|\n|-|:-:|\n|DDA|0.04|\n|CloudFixer|1.07|\n|BFTT3D|6.83|\n|SVWA|10.86 , for N_v=2|\n|MATE|10.79|\n|**SMART-PC**|**59.52**|\n\n***\n**3. Clarification on Batch Normalization Updates**\n\nDuring pretraining, our method learns more abstract and robust features through the skeleton prediction branch. These features are resilient to corruption, such that during test-time adaptation, simply updating the **statistical parameters** of the BatchNorm layers (i.e., running mean and variance) can effectively suppress noise without requiring backpropagation.\nAs shown in [Figure 1] (https://anonymous.4open.science/r/SMART-PC-ICML-737C/skeleton.pdf), the predicted skeleton offers a noise-resistant representation that aligns corrupted inputs with clean data. Further details will be provided in the supplementary material.\n\n***\n**4. Clarification on Feature Fusion in the Classification Branch**\n\nThe encoder and decoder outputs share the same shape **(B, N, D)**, where **B** is the batch size, **N** the number of tokens, and **D** the feature dimension. These features are combined through **element-wise addition** and passed to the classification head. This design enriches the encoder’s representation with structural information from the decoder, which captures the **skeletal geometry** of the object. As shown in **Table 2** in the main paper, this improves classification accuracy. We will clarify this mechanism and revise **Figure 2** in the final version.\n\n***\n**5. Comparison and Advantages of the Proposed Backpropagation-Free Approach over BFTT3D**\n\nUnlike **BFTT3D**, which uses class-based prototypes from the source dataset during adaptation, our method relies solely on the target dataset without accessing any source information. Another key distinction is efficiency: as shown in **Table 2**, our method in the backpropagation-free mode achieves significantly higher **FPS** compared to **MATE**, **SVWA**, **BFTT3D**, **CloudFixer**, and **DDA**. \nIn terms of performance, our method also outperforms **BFTT3D** across three corrupted datasets, as reported in **Table 1**. Together, Tables 1 and 2 demonstrate that our skeleton-based pretraining enables the model to learn robust geometric features, allowing effective adaptation with only statistical parameter updates, achieving both high accuracy and fast inference.", "claims_and_evidence": "The paper presents two main claims. The first is the use of Skeletal Representation as a pretext task to adapt the model to target domain data, which is both interesting and proven to be effective. The second claim is the direct updating of the mean and variance in the batch normalization (BN) layers to fine-tune model parameters without backpropagation, which indeed offers a novel perspective on model parameter adaptation. Both of these claims are clear and well-articulated.", "methods_and_evaluation_criteria": "The proposal of using Skeletal Representation as a pretext task to assist in fine-tuning model parameters is quite interesting. Introducing this self-supervised method into the backbone appears to be highly effective for enabling the model to adapt to target domains. I believe this contribution is well-suited for research on test-time adaptation in point cloud recognition, and it could also be extended to related fields.", "theoretical_claims": "In Section 3.4, when discussing the \"Classification Loss\", the paper mentions, \"we train the network using labeled source data.\" Does this imply that source domain data is being used during the test-time training phase? If labels from the source domain data are indeed utilized, I believe this is inappropriate. Any modules that rely on source domain data for training should not be incorporated into the TTT (Test-time Training) process; they should be part of the source domain training phase rather than the test-time training phase [1][2]. I hope the authors can clarify the details here, as I consider this to be of significant importance.\n\n[1]. Liang J, He R, Tan T. A comprehensive survey on test-time adaptation under distribution shifts[J]. International Journal of Computer Vision, 2025, 133(1): 31-64.\n\n[2]. Sun Y, Wang X, Liu Z, et al. Test-time training with self-supervision for generalization under distribution shifts[C]//International conference on machine learning. PMLR, 2020: 9229-9248.", "experimental_designs_or_analyses": "1. The comparative methods in this paper appear outdated. It is recommended that the authors include more recent algorithms specifically designed for test-time adaptation in point cloud recognition [1][2], or adapt general test-time adaptation algorithms to the point cloud recognition task [3] for a more comprehensive comparison.\n\n2. The experiments in this paper are quite comprehensive, encompassing both online adaptation and standard adaptation tests. The analysis of the batch normalization statistics, in particular, further substantiates the rationale for the lightweight design.\n\n\n[1]. Shim H, Kim C, Yang E. CloudFixer: Test-Time Adaptation for 3D Point Clouds via Diffusion-Guided Geometric Transformation[C]//European Conference on Computer Vision. Cham: Springer Nature Switzerland, 2024: 454-471.\n\n[2]. Wang Y, Cheraghian A, Hayder Z, et al. Backpropagation-free network for 3d test-time adaptation[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2024: 23231-23241.\n\n[3]. Yuan Y, Xu B, Hou L, et al. Tea: Test-time energy adaptation[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2024: 23901-23911.", "relation_to_broader_scientific_literature": "1. [1] demonstrated that constructing an efficient pretext task in point cloud recognition can effectively enable model adaptation to target domain data. The current study further explores and proposes a novel method for designing pretext tasks specifically for test-time adaptation in point cloud recognition.  \n\n2. The analysis of batch normalization layer statistics has frequently been utilized in past studies on test-time adaptation for segmentation tasks [2][3]. However, most approaches involve constructing loss functions based on the mean and variance of BN layers to minimize the discrepancy between the source and target domains. This paper introduces a new approach by directly updating these two parameters without backpropagation. Experimental results show promising outcomes while maintaining recognition accuracy.\n\n[1]. Mirza M J, Shin I, Lin W, et al. Mate: Masked autoencoders are online 3d test-time learners[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision. 2023: 16709-16718.\n\n[2]. Shiyu Liu, Daoqiang Zhang, Xiaoke Hao. Efficient Deformable Convolutional Prompt for Continual Test-Time Adaptation in Medical Image Segmentation. AAAI 2025.\n\n[3]. Chen Z, Pan Y, Ye Y, et al. Each test image deserves a specific prompt: Continual test-time adaptation for 2d medical image segmentation[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2024: 11184-11193.", "essential_references_not_discussed": "[1] and [2], published at ECCV 2024 and CVPR 2024 respectively, are highly relevant to this study, focusing on test-time adaptation for point cloud recognition. While [1] is not cited in this work, Paper 2 is mentioned in the related work section with the statement: 'In addition to these approaches, several works on test-time adaptation have explored updating model parameters during inference to handle distribution shifts effectively.' However, [2] presents a classic test-time adaptation algorithm for point cloud recognition that does not require backpropagation, which aligns closely with the motivation of this paper. Despite this, it is neither included in the comparative experiments nor analyzed in the discussion.\n\n\n[1]. Shim H, Kim C, Yang E. CloudFixer: Test-Time Adaptation for 3D Point Clouds via Diffusion-Guided Geometric Transformation[C]//European Conference on Computer Vision. Cham: Springer Nature Switzerland, 2024: 454-471.\n\n[2]. Wang Y, Cheraghian A, Hayder Z, et al. Backpropagation-free network for 3d test-time adaptation[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2024: 23231-23241.", "other_strengths_and_weaknesses": "The two main contributions of this paper are highly insightful, as they adeptly apply previous research to the current task and achieve performance improvements.", "other_comments_or_suggestions": "I recommend that the authors provide a more detailed description of the batch normalization layer parameter updates to distinguish their approach from previous methods that directly update the BN layer weights and biases by backpropagation. This would help readers more clearly understand the authors' contributions.", "questions_for_authors": "1. In the Classification Branch, the authors mention \"adding the features from the encoder and decoder together\", but I did not fully understand this description. Additionally, the main network diagram (Figure 2) does not clearly illustrate which specific features are being added or how this operation is performed. Why not directly feed the features obtained from the encoder into the classification head?\n\n2. How exactly do the authors use the source domain label data in this paper? Is it during the TTT process? (The detail can be seen in Theoretical Claims).\n\n3. Compared to [1], which backpropagation-free method is more effective? What are the advantages of the backpropagation-free approach proposed in this paper?\n\n[1]. Wang Y, Cheraghian A, Hayder Z, et al. Backpropagation-free network for 3d test-time adaptation[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2024: 23231-23241.", "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": "ls2j6gajij", "reviewer": "Reviewer_J6vZ", "summary": "This paper proposed the method of test-time training for point cloud classification by leveraging skeletal representations. It aims to enhance the model's robustness to different distribution in test time samples. To this end, it introduced skeleton feature extraction branch besides classification branch to enhance the encoder and decoder of model.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank the reviewer for recognizing the novelty, clarity, and effectiveness of our method in improving robustness and efficiency for point cloud test-time training.\n\n**1. METHODS AND EVALUATION CRITERIA - inconsistent Notation for Mask Predictors**\n\nWe'll correct the notations in the final version for clarity and consistency.\n***\n\n**2. METHODS AND EVALUATION CRITERIA - Clarification on Decoder Structure in Figure 2** \n\nOur intention was to illustrate that each skeleton point has an associated radius, predicted separately using MLP\\_s and MLP\\_r. Both outputs are used to construct the final figure. For visualization purposes, they were shown connected. However, we acknowledge that this may cause confusion. In the final version, we will revise the figure to clearly separate these components and better reflect the equations in the main paper.\n***\n\n**Theoretical Claims**\n\nBy incorporating skeletal representation learning, we believe our model introduces a structural inductive bias that prioritizes shape and topology, which aligns with deep learning theory emphasizing that such biases improve generalization and robustness under distribution shifts [1].\n\n[1] Shortcut learning in deep neural networks. Nature Machine Intelligence\n\n***\n**EXPERIMENTAL DESIGNS OR ANALYSES**\n\n1. We have included a visualization [Figure 1] (https://anonymous.4open.science/r/SMART-PC-ICML-737C/skeleton.pdf) that illustrates how our method abstracts both clean and corrupted point clouds into compact and meaningful skeleton representations. The top row shows a point cloud under different conditions (clean, Uniform noise, Background noise), and the middle row relates to the corresponding skeletons. From this visualization, the model can estimate the skeleton under a uniform noise, preserving the overall shape of the clean data. This shows that the skeleton's abstract representation is less sensitive to the noise compared to the original points (Success Case). But under a harsh noise condition (right), the skeletons try to cover all the background outlier points, making the radius excessively large, hindering the effective representation of the main inlier points. Accuracy gain (bottom row) shows the trend; more visualizations will be provided in the final version.\n***\n\n2. We provided an ablation study in **Table 1** to quantify the impact of each skeletal loss component. The best performance is achieved with coefficients **(0.3, 1.0, 0.4)**—as suggested in the Point2Skeleton paper—showing a good improvement in mean corrupted accuracy (**72.95\\%**) compared to other settings. This confirms that each loss term contributes to learning more robust features.\n\n### Table 1: Ablation study of skeleton loss coefficients (ModelNet40 / ModelNet40-C, online adaptation)\n\n|Pt2Sphere|Sampling|RadiusReg|Source Acc(%)|Corrupted Acc(%)||\n|:-:|:-:|:-:|:-:|:-:|-|\n|1.0|1.0|0.0|91.3|67.82||\n|0.0|1.0|1.0|91.6|67.79||\n|1.0|0.0|1.0|91.6|67.80||\n|1.0|1.0|1.0|91.2|72.84||\n|0.3|1.0|0.4|91.3|**72.95**|coefficients from Point2Skeleton paper|\n***\n\n3. As described in the main paper, the **Radius Regularization Loss** (Equation 6) is designed to avoid instability caused by overly small radii, especially under noisy conditions. This loss encourages the model to learn **larger and more stable radii**, which improves the robustness of the skeletal abstraction. Although we do not observe excessively large radii, the **Point-to-Sphere** and **Sampling** losses (Equations 11 and 12) implicitly constrain radius size by preserving geometric consistency. As shown in **Table 1**, removing the regularization term leads to a drop in performance, confirming its importance.\n***\n\n4. In [Figure 1] (https://anonymous.4open.science/r/SMART-PC-ICML-737C/skeleton.pdf), we have included a failure case (right) to showcase a potential limitation of the skeleton estimation. From this figure, when the noise expands far beyond the main object's dimensions, the skeleton gets distracted by this out-of-distribution dimensionality change, and while it remains focused on the original points, its radii get excessively large in a way that it no longer represents the original point cloud's shape.\n***\n\n5. In standard mode, the batch size is 1, and the model is reset for each sample, so updating BN statistics (backpropagation-free) cannot help the model. To further investigate, we conducted an ablation study on the ScanObjectNN-C dataset in standard mode using the backpropagation-free setting. We tested batch sizes of 8, 16, and 32 with a single iteration for efficiency, as shown in **Table 2**. It shows that in standard mode, SMART-PC can improve performance on corrupted datasets in the backpropagation-free setting when the batch size is increased.\n\n### Table 2: Ablation study of SMART-PC (standard mode and backpropagation-free) on ScanObjectNN-C.\n|Batch Size|Iteration|Mean Acc.(%)||\n|:-:|:-:|:-:|-|\n|1|--|38.7|SMART-PC-SO (source only)\n|8|1|39.73||\n|16|1|39.96||\n|32|1|39.98||", "claims_and_evidence": "The authors' claims are clear and straightforward, and they effectively address the limitations of previous work. For example, MATE lacks geometric understanding, rendering it less robust to surface-level corruptions. Additionally, the authors tackle the efficiency issues present in MATE. These limitations are directly addressed by the solutions proposed in this paper.", "methods_and_evaluation_criteria": "Most parts of the Method section are clear and self-contained, and Figure 2 effectively illustrates the architecture and training pipeline of SMART-PC. Here are a few minor recommendations:\n\n1. The notation for the mask predictors is inconsistent—MLP_skel (Equation 7) and MLP_s (Figure 2) are used interchangeably (similarly for MLP_radius and MLP_r). Please standardize these terms to avoid confusion.\n\n2. At the top of Figure 2, MLP_r appears to be connected sequentially after MLP_s, which does not match the equations in the main paper. Could the authors clarify this discrepancy?", "theoretical_claims": "This paper presents an empirical-results-driven method, without offering any theoretical contributions.", "experimental_designs_or_analyses": "Below are several suggestions to further enhance the experimental design:\n\n1. Could the authors include visualizations that demonstrate how corrupted point clouds are restored or abstracted using skeleton points?\n2. Would ablation studies on the skeletal losses help quantify their impact?\n3. Is regularization necessary to prevent the radii from becoming excessively large?\n4. Are there any failure cases that could be analyzed to better understand the method’s limitations?\n5. Can the authors explore a backpropagation-free strategy for SMART-PC-Standard?", "relation_to_broader_scientific_literature": "Key contributions acknowledged by both the authors and the reviewer include:\n\n1. first work to improve test-time training of point cloud classification using skeleton representation.\n\n2. This paper improves the performance while achieving efficient adaptation and inference pipeline.", "essential_references_not_discussed": "N/A", "other_strengths_and_weaknesses": "Please see above.", "other_comments_or_suggestions": "Please see above.", "questions_for_authors": "Please see above.", "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": "160qz5Bs1q", "reviewer": "Reviewer_pfv6", "summary": "The paper titled introduces a novel skeleton-based framework, SMART-PC, designed to enhance the robustness and efficiency of 3D point cloud classification models during test-time training (TTT). This paper leverages skeletal representations to extract robust geometric features that are less sensitive to corruptions, enabling the model to adapt effectively to test-time distribution shifts. Extensive experiments on several benchmarks demonstrates its effectiveness", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank the reviewer for recognizing the novelty of our skeleton-based framework and the effectiveness of skeletal representations, as well as our thorough experimental validation.\n\n**Weakness**\n\n**1 and 2**. Our method supports two modes of adaptation: one with backpropagation and one that is backpropagation-free. The goal of the backpropagation-free mode is to demonstrate that, during pretraining, the use of a skeletal prediction branch encourages the model to learn **robust and meaningful geometric features**. As a result, during test-time adaptation, simply updating the **statistical parameters** of the BatchNorm layers (i.e., running mean and variance) is sufficient to enhance performance on corrupted datasets. This effect is validated by our results in **Table 1** of the main paper.\n\nTo further support this claim, we conducted additional experiments using the BFTT3D[1] method, employing three different pretrained models:\n\n     1. Org-SO: A baseline model pretrained with only an encoder and classification head.\n\n     2. MATE-SO: A model pretrained using the MATE framework, which includes an encoder, a reconstruction decoder, and a classification head.\n\n     3. SMART-PC-SO: Our model, pretrained with an encoder, a skeleton-based decoder, and a classification head.\n\nEach of these models was evaluated using the same BFTT3D adaptation strategy. The results in Table 1 show that our pretrained model significantly outperforms both the baseline and MATE-pretrained models in the backpropagation-free setting across all three datasets. This provides strong evidence that the skeletal prediction branch enables the model to extract more **structure-aware and corruption-resilient features**, which allow effective test-time adaptation even without updating model weights via backpropagation.\n\n**Table 1: Mean Accuracy (%) of BFTT3D with Different Pretrained Models**\n\n| Dataset     | Org-SO | MATE-SO | SMART-PC-SO |\n|-|:-:|:-:|:-:|\n| ScanObjNN-C| 33.00| 33.22| **35.90** |\n| ModelNet40-C| 57.16| 54.71| **65.25**|\n| ShapeNet-C| 60.73| 53.07| **62.24**|\n\n[1] Backpropagation-free network for 3d test-time adaptation\n***\n\n**3.** We confirm that our method does **not use any supervised ground truth for skeleton prediction**. Instead, the model learns to predict the skeleton structure (position and radius) in an **unsupervised manner** using the loss functions defined in Equations 11, 12, and 13. These losses serve as supervisory signals and include: (1) a point-to-sphere distance loss ensuring coverage of the input shape, (2) a skeleton-to-point loss encouraging compactness, and (3) a radius regularization term. Together, they provide strong geometric guidance, even without explicit supervision.\n\nOur goal is not to recover the point cloud itself (as in standard reconstruction branches), but to extract a more **structured and abstract representation** that captures the underlying geometry. The effectiveness of this approach is validated by the results in **Table 1 of the main paper**, which show that the features learned through the skeleton prediction branch lead to significantly better performance on corrupted datasets, especially in the backpropagation-free adaptation mode, compared to prior reconstruction-based methods like MATE.\nAdditionally, we tested our pretrained model (SMART-PC-SO) with the BFTT3D method in the backpropagation-free setting, and compared it to Org-SO and MATE-SO. As shown in **Table 1**, our skeleton-pretrained model achieves higher robustness across corruptions, further supporting the generalizability and strength of the learned skeletal features.\n***\n\n**4.** Since our implementation is largely based on the official MATE codebase, it is crucial to ensure the reproducibility of their reported results for a fair comparison. To this end, we faithfully reproduced their results using the exact code, hyperparameters, and pretrained models provided in their public GitHub repository (we witnessed a discrepancy in their results on ModelNet40-C and ScanObjectNN-C). For transparency and verification, we have also included the corresponding log files in our anonymous repository (https://anonymous.4open.science/r/SMART-PC-ICML-737C/).\n***\n\n**5.**  We thank the reviewer for pointing this out. Please refer to our response to Reviewer **k4Xv** for the updated **Table 1**. We regret that, due to space constraints, we are unable to include the full content here.\n***\n\n**6.** We conducted this experiment, and the results are shown in **Table 2** in our response to reviewer **k4Xv**. For DDA and CloudFixer, we report the FPS values directly from their respective papers. For all other methods, we measured the FPS ourselves, including the time required for adaptation. Importantly, the reported FPS for SMART-PC corresponds to the **backpropagation-free mode**, highlighting its efficiency under test-time adaptation without gradient updates.\n***\n\n**7.** We will revise and improve the writing of our paper in the final version.", "claims_and_evidence": "Refer to the Strengths and Weaknesses", "methods_and_evaluation_criteria": "Refer to the Strengths and Weaknesses", "theoretical_claims": "Refer to the Strengths and Weaknesses", "experimental_designs_or_analyses": "Refer to the Strengths and Weaknesses", "relation_to_broader_scientific_literature": "Refer to the Strengths and Weaknesses", "essential_references_not_discussed": "Refer to the Strengths and Weaknesses", "other_strengths_and_weaknesses": "Goodness:\n\n1.\tThis paper leverages skeletal representations to extract robust geometric features that are less sensitive to corruptions.\n\n2.\tExtensive experiments on several benchmarks demonstrates its effectiveness\n\nWeakness:\n\n1.\tWhy leveraging skeleton representation can eliminate the need for back propagation during adaptation is confusing, it would be better to adding more explanations or analysis.\n\n2.\tSince the authors claim the skeleton representation is more robust than current representation like point cloud, it will be better to demonstrate its generalization and  extend the skeleton representation to more methods, not limited to MATE.\n\n3.\tDuring the training process of predicting the skeletons, is there any more supervision object? Current framework predicts the skeleton(position+radius) of the input via an implicit way (Eq. 7,8). I am not sure whether it truly works, for the reason that just adding extra reconstruction branch can well help the model getting more inner information from the data.[1,2]\n\n4.\tThe results of MATE[3] (both standard and online) on ModelNet40-C is not consistant with the paper. \n\n5.\tIn Tab.1, lacking comparsion with current methods like BFTT3D[4], [5], \n\n6.\tIt will be more convincing to adding more comparison with other methods about the efficiency in Fig.3.\n\n7.\tThe writing needs to be improved. This point will not affect my rating score.\n\n[1] Improving Language Understanding by Generative Pre-Training\n\n[2] Generative Pretraining from Pixels\n\n[3] MATE: Masked Autoencoders are Online 3D Test-Time Learners\n\n[4] Backpropagation-free Network for 3D Test-time Adaptation\n\n[5] Test-Time Adaptation in Point Clouds: Leveraging Sampling Variation with Weight Averaging", "other_comments_or_suggestions": "Refer to the Weakness.", "questions_for_authors": "Refer to the Weakness. I will improve my rates when the weaknesses are solved.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Meta-review: All reviewers expressed a positive view of the paper and recommended acceptance. The work introduces two key contributions that are both interesting and well-supported by experimental results.

First, the use of Skeletal Representation as a pretext task for domain adaptation is novel and shown to be effective. This strategy offers a compelling approach for adapting models to target domain data in a self-supervised manner. Second, the proposed method of directly updating the mean and variance in Batch Normalization (BN) layers—without relying on backpropagation—provides a fresh and efficient perspective on model parameter adaptation. Both contributions are clearly presented and well-motivated.

The authors were responsive during the rebuttal phase, addressing experimental concerns raised by reviewers through additional experiments. While these additions significantly strengthened the paper, it is also noted that a substantial amount of clarification and refinement occurred during the rebuttal process.

Overall, the paper presents solid technical contributions and demonstrates thoughtful empirical validation. Despite the initial need for clarification, the final version reflects a promising and well-executed piece of research.
Decision: Accept (poster)