PointMAC: Meta-Learned Adaptation for Robust Test-Time Point Cloud Completion
Authors: Linlian Jiang
Rui Ma
Li Gu
Ziqiang Wang
Xinxin Zuo
Yang Wang
Venue: NeurIPS 2025 poster ()
Keywords: point cloud completion
Primary area: applications

Abstract: Point cloud completion is essential for robust 3D perception in safety-critical applications such as robotics and augmented reality. However, existing models perform static inference and rely heavily on inductive biases learned during training, limiting their ability to adapt to novel structural patterns and sensor-induced distortions at test time.
To address this limitation, we propose PointMAC, a meta-learned framework for robust test-time adaptation in point cloud completion. It enables sample-specific refinement without requiring additional supervision.
Our method optimizes the completion model under two self-supervised auxiliary objectives that simulate structural and sensor-level incompleteness.
A meta-auxiliary learning strategy based on Model-Agnostic Meta-Learning (MAML) ensures that adaptation driven by auxiliary objectives is consistently aligned with the primary completion task.
During inference, we adapt the shared encoder on-the-fly by optimizing auxiliary losses, with the decoder kept fixed. To further stabilize adaptation, we introduce Adaptive $\lambda$-Calibration, a meta-learned mechanism for balancing gradients between primary and auxiliary objectives. 
Extensive experiments on synthetic, simulated, and real-world datasets demonstrate that PointMAC achieves state-of-the-art results by refining each sample individually to produce high-quality completions. To the best of our knowledge, this is the first work to apply meta-auxiliary test-time adaptation to point cloud completion.

PointMAC: Meta-Learned Adaptation for Robust Test-Time Point Cloud Completion
Linlian Jiang'
Rui Ma
Li Gu
Ziqiang Wang'
Xinxin Zuo
Yang Wang 1.3+
Concordia University Jilin University Mila Quebec Al Institute
Engineering Research Center of Knowledge-Driver Human-Machine Intelligence, MOE, China {linlian, jiang, li.gu, ziqiang. wang] Gmail. concordia. ca. ruimžjlu lu. edu. cn, {xinxin. zuo, yang. vang//concordia.co
Abstract
Point cloud completion is essential for robust 3D perception in safety-critical appli- cations such as robotics and augmented reality. However, existing models perform static inference and rely heavily on inductive biases learned during training, limit- ing their ability to adapt novel structural patterns and innsor-induced distortions at test time. To address this limitation, we propose PointMAC, a meta- framework for robust test-time adaptation in point cloud completion. It enables sample-specific refinement without requiring additional supervision. Our method optimizes the completion model under two supe-supe auxiliary objectives that simulate structural and sensor-level incompleteness. A meta-auxiliary learning strategy based Model-Agnostic Meta-Learning (MAML) ensures that adaptation driven by auxiliary objectives is consistently aligned with the primary completion task. During inference, we adapt the shared encoder on-the-fly by optimizing auxiliary losses, with the decoder kept fixed. To further stabilize adaptation, we in- troduce Adaptive X-Calibration, a meta-learned mechanism for balancing gradients between primary and auxiliary objectives. Extensive experiments synthetic, sim- ulated, and real-world datasets demonstrate that PointMAC achieves state-of-thed results by refining each sample individually to produce high-quality completions. To the best of our knowledge, this the first work to apply meta-auxiliary test-time adaptation to point cloud completion.
Introduction
Recent advances in 3D sensing have enabled safety-critical applications in autonomous driving [1], robotics [2], and AR [3], where reliable 3D perception is fundamental. Point clouds the direct often incomplete due occlusions, limited coverage, and sensor noise,
output of 3D sensors- severely impairing downstream tasks such as recognition, planning, and interaction. This highlights the urgent need for robust point cloud completion under diverse, unpredictable conditions.
Existing point cloud completion approaches largely adopt encoder-decoder architectures. Although recent works have introduced sophisticated decoders [4, 6] with progressive refinement via localized expansion, performance remains constrained by the expressiveness of extracted features from the incomplete inputs. This has motivated the development of wansformer-basel models [7, 8, 9], which primarily enhance the encoder's 's ability to capture rich, contextual features by modeling global context through attention mechanism. However, despite their improved representation capacity achieved by scaling up the model, inference remains static at test time, regardless of the specific input point cloud. This rigidity constitutes fundamental bottleneck: the inability adjust internal representations per input, limiting the model's ability to leverage visible cues, particularly under novel occlusions
*Corresponding authors
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Static Inference
Generic Completion
Test-time Adaptation
Sample-speciie Completion
GT
adapt
[ Fixed Inductive Bias
spepli-
E3E Encoder/ Weight-sharing Encoder Auxiliary Self-supervised Signals
Figure I: Existing point cloud completion models operate with fixed inductive biases at inference, often focusing on structurally stable regions (e.g., the fuselage). When such regions missing, static inference hinders reasoning over other parts (e.g., the tail), resulting in generic completions. Instead, our model applies test-time adaptation using self-supervised signals, enabling dynamic attention to visible cues and producing sample-specife completions that better match the ground truth.
or censor-induced distortions. As a result, the model tends to generate what we refer to as generic completions, which follow training priors and show limited sensitivity input cues, rather than sample-specific completions that adapt to unique observations and restore geometry details (see Fig. 1). The generation of generic completions is further exacerbated by dataset limitations: synthetic data [4, 10] lacks structural diversity, while real-world scans [1 [11] are limited in scale and coverage. These constraints reinforce inductive biases, causing the model to over-rely on structural priors and neglect sample-specific cues, ultimately degrading completion quality.
Motivated by these limitations, shift from static inference to dynamic, sample-specifie adaptation, enabling the model to refine predictions from each input's unique geometry and noise, leading to higher-quality completions. Test-time adaptation (TTA) offers a natural framework for this by enabling self-adaptation with unlabeled test data, and its effectiveness has been empirically vali- dated [12, 133. We thus treat each point cloud distinet domain, assuming that each input inherently reflects its source distribution. To this end, we introduce PointMAC, a TTA framework based on meta-auxiliary learning that performs per-sample refinement improve completion accuracy.
First of all, PointMAC considers point cloud completion as the primary task and introduces an auxiliary branch, Bi-Aux Units, which performs self-supervised spatial-masking reconstruction and artifact denoising. Unlike conventional TTA methods [12, 13, 14] that optimize auxiliary losses in isolation, often resulting in misalignment with the primary task [15, 16]. we adopt the Model-Agnostic Meta-Learning framework (MAML) [17] to regularize adaptation. Specifically, the auxiliary branches optimized in the MAML inner loop to simulate the sample-specific adaptation, while the primary point cloud completion task supervises the outer loop to align the adaptation with the main objective. This meta-learning formulation encourages the model adapt through optimizing auxiliary tasks in a way that directly benefits the primary task. At inference time, on-the-fly adaptation performed by minimizing self-supervised losses defined using pseudo-labels generated by Bi-Aux Units. These losses are optimized via backpropagation, allowing the model to refine sample-specific outputs dynamically and overcome static attention patterns learned during training. To further stabilize adaptation and prevent task interference, we introduce Adaptive X- X-Calibration, which harmonizes gradient contributions from the primary and auxiliary tasks during meta- meta-training.
Our contributions can be summarized as follows:
We propose PointMAC, a test-time adaptation method that addresses static encoder rigidity through sample-specific refinement, leveraging Bi-Aux Units to generate self-supervised signals under structural incompleteness and sensor noise for self-supervised adaptation.
We introduce Adaptive X-Calibration, a dynamic gradient balancing mechanism that miti- gates negative transfer [15] during meta-training and improves test-time stability.
To the best of our knowledge, this is the first application of meta-auxiliary learning and test-time adaptation in point cloud completion. PointMAC achieves state-of-the- re- sults synthetic, simulated scanning, and real-world benchmarks, demonstrating strong generalization and adaptation capabilities across diverse point cloud domains.
2 Related Work
Point Cloud Completion. Recent approaches to point cloud completion have focused on architectural innovations [18, 5, 6, 9, 19]. However, most methods rely static encoder features learned from

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(a) Meta-Learned Test-Time Training
(b) Sample-Specific TTA
Adaettive 2 Calibration
Cada aux
stochastic masking
Bi-Aux
Eth
Bi-Aux
Cada
Units
artifact injection
Shared Weights
inner adaptaat ion
pri
Primary Task
alignment
Forward Pass
D
Gradient
Incomplete P
Complete
Incomplete P
Figure 2: Overview of our test-time adaptation method. PointMAC formulates point cloud completion the primary task and introduces Bi-Aux Units provide self-supervised signals for test-time adaptation. The encoder ge is shared between primary and auxiliary branches. In the meta-learned test-time training phase (a), sample-specific parameters are updated in the inner adaptation using auxiliary losses, while shared parameters are optimized in the outer alignment via the primary completion loss. In the sample-specific TTA phase (b), adaptation proceeds in three steps: (i) the meta-learned model produces initial completions; (ii) the shared encoder E is updated via self- supervised losses from Bi-Aux Units; (iii) sample-spece completions are generated, adapted the unique structure and noise of each input.
biased synthetic datasets [4, 10), leading to inductive biases that generalize poorly to novel occlusions and sensor noise [1]]. Without sample-specific adaptability at inference time, these models often yield generic completions that overlook input-specific cues and degrade reconstruction quality,
Test-time Adaptation. Test-time adaptation (TTA) addresses domain shift by adapting models online using unlabeled test data. Prior works have explored TTA across various domains, such as dynamic seene deblurring [20, 21]. optical flow [22], and sequential modeling [23]. By leveraging self-supervised auxiliary signals from test inputs, TTA has demonstrated improved robustness and generalization [12, 24]. However, key challenge lies in the misalignment between auxiliary and primary tasks, which can lead to unstable or suboptimal adaptation [15, 16). To address this, we adopt the Model-Agnostic Meta-Leat Meta-Learning (MAML) framework [17] regularize adaptation.
Meta Learning. Meta-learning methods, such as Model-Agnostic Meta-Learning (MAML) [17], enable fast adaptation of pre-trained models to individual samples and have shown strong performance
in few-shot learning [25, 26], as well as in auxiliary-tai taallaar--tsk-gaeee task-guided multi-task training [27, 28). Building
on these advances [29, 30, 31], incorporate meta-learning into test-time adaptation framework to align self-supervised auxiliary objectives with the primary completion task, resulting in higher- fidelity and muucture-awart shape completion.
Method
In this section, we introduce the proposed PointMAC method, including the network architecture and the test-time adaptation framework. As illustrated in Fig. 2, the overall architecture consists of a shared encoder, a primary decoder for shape reconstruction, and Bi-Aux Units that provide self-supervised signals by simulating structural occlusions and sensor-induced distortions, detailed in Sec. 3.1. To train the network, we adopt a meta-auxiliary learning strategy based on MAML to align the sulf-supervised auxiliary adaptation with the primary point cloud completion objective, enabling sample-specific adaptation at test time (detailed in Sec. 3.2).
3.1 Network Architecture
3.1.1
Primary Branch
Given a partial and unordered point cloud p {p: C. I33 the goal of the primary task is to reconstruct complete point cloud C N C R with N > M. As illustrated in Fig. 2(a), {,,
we adopt hierarchical encoder Esh inspired by [32, 33), to extract both local and global geometric features from P, producing compact shape code, E dsh). The decoder D takes as

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input and reconstructs the final point cloud C = Dzz ) using coarse-to to-fee- refinement strategy, following [19]. To supervise the primary task, we adopt the Chamfer Distance (CD) to measure the discrepancy between the predicted point cloud and the ground truth G. The CD between two point sets X, y C IR is defined as:
()) = [
Σ #EX
min o
v112
+
1 D
min ll 2112. Σ NEY
TE
where | and [3 denote the number of points in each set. Based on Eq. (1), the primary loss is defined as pri = (C,(C,)), g), where C and G are the predicted and ground-truth point clouds.
The encoder g is shared between the primary branch for point cloud completion and the Bi-Aux Units (Sec. 3.1.2), and its parameters are denoted as 6pm We denote the full set of parameters for the primary task as Opri- During test-time adaptation, we freeze the decoder 'D and update only the shared encoder 8hh to enable sample-specific feature refinement via auxiliary losses (Sec. 3.2).
3.1.2 Bi-Aux Units
stochastic masking
Input
J.M
Reshape
Camr
Esh
ReLU
p
BN
Cnd
R 9
Token Synergy Integrator artifact injection
Positional Encading
Perturbation Operation
M
AttnMask
Point-wise
Figure 3: Overview of the proposed Bi-Aux Units, consisting of two self- self-supervieed StochSstccc Masked Reconstruction (Aux-""") and Artifact Denoising (Auz")), Both branches share the encoder Eh and Token Synergy Integrator TisI ensure consistent feature conditioning, and output features (FM, R9) that are projected to compute the auxiliary losses [amr and Cad anx
In addition to the primary branch, we introduce self-supprvised Bi-Auxiliary (Bi-Aux) Units, which generate auxiliary signals regularize the shared encoder during test-time training. Point cloud scans- whether synthetic or real face two major challenges: structural incompleteness and distortion robustness. While synthetic data often exhibits regular missing patterns, real-world scans suffer from irregular occlusions and sensor noise, which exacerbate these issues. To address this, we design Stochastic Masked Reconstruction (Auz.mr) and Artifact Denoising (Aua.d) promote resilient encoder representations without ground-truti supervision.
Stochastic Masked Reconstruction. To mitigate structural bias in the shared encoder and improve robustness to diverse missing patterns, we design Stochastic Masked Reconstruction (Aur:"mr param- eterized by ) which randomly masks spatial regions of the input cloud and trains the model to recover them.
As shown in Fig. apply Farthest-Point Sampling (FPS) extract N centroids Q = {qx} ) k= from the input cloud P, and embed them into region tokens z PF(q)) E R. using a learnable positional encoder, while the shared encoder Eo simultaneously generates global feature F from P.
To reduce redundancy and enable parameter sharing across Bi-Aux Units, we introduce a Token Synergy Integrator (ITsı) with parameters dth which maps F into a group-token matrix 7 NxD via an MLP stack (BN ReLU) followed by reshaping. The transformation, shared across auxiliary signals via (aix- encourages consistent conditioning across tasks and eliminates redundant parameterization. This also supports effective test-time adaptation by enforeing shared representation priors across auxiliary tasks. The resulting group-token matrix 7 is concatenated with region tokens {zz}, and the combined sequence is fed into dual-masked self-attention [34] to extract context-aware features:
AltnMask (Q, K, V; M) = Softmax (iaiiaax(QQKK) (QK /VD) - (1-M 20))

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where M is a binary mask applied to both rows and columns. The output features FM are aggregated via max pooling along the token dimension produce compact latent vector, which is decoded via a lightweight [18] to reconstruct the complete point cloud P. The self-supervised loss is defined as: LCD (P, P). (see the Supplementary for further architectural details).
aux
Artifact Denoising. Sensor-induced artifacts in real scans impede accurate shape recovery. Artifact Denoising (Auz* mitigates this by corrupting input point clouds with realistic perturbations and training the model to restore clean geometry. This process encourages the encoder to learn distortion- resilient representations, enhancing robustness under real-world scanning conditions.
We introduce an auxiliary branch Auz" parameterized by d which performs artifact- aware denoising. This branch learns a mapping function Yed R-Mx3 - TR&Mx3 x3 (with & = 4) to
reconstruct a clean and dense point cloud p = T (P) from noisy partial input P = P + N0 (see Supplementary for details).
Aua.d builds on a shared architecture: it reuses the encoder E from the primary branch to extract global features, and employs the shared Token Synergy Integrator Zrsi described above in Aux.mr to aggregate local context in a unified token space, yielding refined sequence RS, This design avoids duplicated learning efforts and facilitates effective cross-task knowledge transfer. Finally, we integrate the SpatialRefiner module from Dis-PU [35] to decode the features R back to the original input point cloud. The output P is supervised by Chamfer Distance: Lad LPn(P,
3.2 Model Learning
Given the above network architecture. will introduce our meta-auxiliary learning framework that allows sample-specific test-time adaptation in this section. Conventional TTA methods [27, minimize auxiliary losses at test time, but misalignment with the primary task can lead negative transfer [15]. PointMAC addresses this by leveraging first-order MAML [17] to align auxiliary updates with the primary objective.
Meta-Learned TTA: Training. As illustrated Fig. 2(a), we enable effective test-time adaptation by simulating sample-specific encoder updates during training through interleaved meta-inner and outer loops. In the inner loop, the shared encoder and auxiliary branches are adapted using a single input point cloud randomly sampled from the training set. The outer loop then updates the full model to ensure that these auxiliary adaptations contribute to improving performance on the primary task. The full training procedure is detailed below.
Inner Auxiliary Adaptation: We divide the model parameters into shared weights pri: 2 } and sample-specific weights {ópi. Pame dndd) J. For each auxiliary branch a E {smr, ad], we perform an inner-loop update at step t by minimizing the auxiliary loss:
po
(1)
smm(t)
V
CAmI
(p()),((t), p((), & sh(()) sh((t)
somr(t))
aaux
mux
Paux
(3)
1)
aax
aux
(1)
B
goel
Lad (pio, p(t);
Ppn Pax
sh(t) sh(t)
(ad(t)
Pux
where P and P are outputs of the two auxiliary branches, and a, B are their learning rates.
(ii) Outer Primary Alignment: Given the updated auxiliary task parameters, we align them with the primary objective by optimizing the primary task loss:
1 1 Σ (C(),, p,((); dppn): T
Syri
where T is the batch size, Lps denotes the primary task loss function, and Φpa (deh didee represents the set of parameters for the primary task. The parameters are updated using gradient descent over the mini-batch, where t. denotes the current update step.
(t+1) pei
T T
1
Cpa p(i); o
(5)
pri
1
where y is the learning rate for the primary task.
5

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Adaptive X-Calibration. Balancing multi-task losses is particularly brittle in test-time adaptation, where fixed weights can destabilize optimization or suppress the primary objective. While prior works (27, 29, 37] adopt static or manually tuned weights, such heuristics fail to generalize across samples or training stages, We propose Adaptive X-Calibration, a meta-learned, gradient- mechanism that dynamically adjusts the auxiliary weights Ammr and And during training.
Specifically, the weights are softmax-normalizer in logit space (Eq. (6)) and used to compute the total auxiliary loss in Eq. (7).
Wsmr
(log(1 + X \/ + X log(1 + >aa)))
Wad
Wamrs
(6)
Cada
Wsme"
Lamx
+ Wad Cad
(7)
Both the auxiliary branch parameters Paux {Asmrs } are jointly updated via gradient descent:
auxx Aad and the weighting coefficients
Paux
Paux
Venn
Cuda
A+> 7 V Cales
jointly optimizes the model parameters of auxiliary branches and their relative weights in a task- aligned manner, allowing the model to automatically calibrate auxiliary signals based on their utility the main objective.
Sample-Specithe TTA: Inference. At inference, we perform a few self-supervised gradient steps on auxiliary losses for each test sample, as shown in Fig. 2(b). The auxiliary branches and their calibrated weights, learned during meta-training, remain fixed during adaptation. The shared encoder refined by minimizing the combined auxiliary loss Cade summarized in Alg. 1.
Algorithm 1: Sample-Specifer Test- Time Adaptation
Trained parameters o Loph Ppri aax dsur test input Pies; step size y): number of steps K Output: Adapted encoder dih
Input:
Fuxx "
Apply stochastic masking and artifact injection using M and a via Auz and Auz"d, for 1 0 to K do
/* Inner-loop adaptation */
-- Auzz _Forward Plest M; (anx))
P + Aux _Forward (Piest: σ; (2dx);
Cade Agmee Laaee (P, Puex) + Aas Lad (P.Pus):
Ppii
sh(f++) 1)
Pp 17 V oal Luxx
sh((t)
/* Gradient descent shared encoder +/
return
pmi
sh(K)
These label-free inner-loop updates perform sample-specific test-time adaptation, refining the coder to better capture the visible structure and noise characteristics of each input. This improves feature quality, reduces completion error, and enhances generalization to novel inputs- all without supervision or retraining.
Experiments
In this section, we evaluate our method on three types of datasets: purely synthetic datasets (ShapeNet [10], PCN [4]), high-fidelity simulated scanning dataset (MVP [38]), and real-world scanned dataset (KITTI [11]). ShapeNet and PCN provide dense, uniformly sampled synthetic point clouds with paired ground truth for training and evaluation. MVP is high-resolution, multi-view rendered benchmark that closely mimics real- real-world 3D scanning conditions, including occlusions and view- point variation. KITTI consists of real LiDAR scans with sparse, uneven points and no paired ground truth, and is used for evaluation only (see Supplementary for additional details related to Sec. 4).
4.1 Datasets and Evaluation Metric
PCN. The PCN dataset [4] includes 30,974 CAD models across eight categories. We evaluate reconstruction quality using the -noormm Chamfer Distance and follow prior works [4, 5, 6, 8, 18, 19, 39] by using their released implementations and hyperparameters.

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Input
PoinTr
ProxyFormer
CRA-PCN
Ours
GT
Boot
PCN
Lamp
Plane
ShapeNet
Chair
Figure 4: Visualization results on the PCN and ShapeNet datasets. Our method preserves fine-grained structures such the complex geometry of boats and lamps, plane propellers and tails, and chair back slats, demonstrating strong completion quality and generalization across diverse categories.
Table Quantitative comparison on the PCN dataset (per-por poir-point CD-4 x 1000). Both the output and ground truth point clouds consist of 16,384 points. (Lower CD is better)
Cx-1,xx000))
Plane
Cabinet
Car
Chair
Lamp
Couch
Table
Boat
CD-Avg
FoldingNet [18]
9.49
15.80
12.61
15.55
16.41
15.97
13.65
14.99
14.31
PCN [4]
5.50
22.70
10.63
8.70
11.00
11.34
11.68
8.59
9.64
SnowflakeNer [5]
4.29
9.16
8.08
7.89
6.07
9.23
6.55
6.40
7.21
PoinTr
4.75
10.47
8.68
9.39
7.75
10.93
7.78
7.29
8.38
ScedFormer [6]
3.85
8.06
7.06
5.21
8.85
6.05
6.74
ProxyFormer [8]
4.01
9.01
7.88
7.11
5.35
8.77
6.03
5.98
6.77
EINet [39]
3,96
8.81
7.74
6.93
5.03
8.80
6.15
5.57
6.63
CRA-PCN [19]
3.59
8.70
7.50
6.70
5.06
8.24
5.72
5.64
Ous
3,54
8.66
7.44
6.65
4.98
8.19
5.64
5.57
6.33
ShapeNet-55/34. Both datasets are derived from ShapeNet [10]. ShapeNet-55 provides 41,952 training and 10,518 testing shapes across 55 categories for category-agnostic evaluation. ShapeNet-34 offers 46,765 training shapes and 5,705 testing shapes from 34 categories, split into 3,400 seen- class and 2,305 unseen-class samples for eategory-specife generalization. Following standard protocol, we use Chamfer-&z distance and F-Score @ 1% [40] as evaluation metrics. Prior methods [4, 6, 7,8, 8, 18, 19, 39] are re- trairid and evaluated under identical settings for fair comparison.
MVP. The MVP dataset [38] is large-scale simulated scanning benchmark with over 100,000 partial-complete point cloud pairs across 16 categories. Partial shapes are rendered from 26 uniformly distributed views to simulate realistic occlusions. We use Chamfer-&2 distance and F-Score @ % for evaluation, and compare with prior methods [4, 19, 38, 41, 42, 43, 44] under their official settings.
We evaluate our method on the KITTI dataset [11], which consists of incomplete LiDAR- scanned car point clouds collected in real-world outdoor environments. Due to the lack of paired ground- tround-truth shapes, we adopt Fidelity and Minimal Matching Distance (MMD) as evaluation metrics. Comparisons are conducted against prior methods 6, 7, 18, 39, 41,45].
KITTI
4.2 Evaluation on Main Datasets
Results on PCN. In Table 1, PointMAC achieves SOTA performance across all categories. Fig. qualitatively compares our results with leading methods including PoinTr [7], ProxyFormer [8], and CRA-PCN [19]. Our method consistently generates more structurally coherent and high-fidelity
7

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Table 2: Quantitative comparison on ShapeNet-55. We report CD-&2 scores for the 10 major categories and the overall average across all 55 categories under three difficulty settings (CD-S, CD-M, CD-H for small, medium, hard), as well as the average score. (Lower CD and higher Fl are better.)
(x110))0000)
Car
Sofa
Bird
Bag
Rockel
CD-$
CD-Avg
FoldingNer [1S]
2.53
2.81
1.43
1.98
2.48
4.71
2.79
1.4
124
143
1.67
2.06
4,05
3.12
0.082
PCN PCN[4] [4]
2.13
2.29
1.02
1.85
2.06
4.50
2,86
1.33
0.89
1.32
1.94
1.96
4.08
2.66
0.133
PoinTT
0.44
0.91
0.79
1,86
0.3
0.53
0.38
0.57
0.58
1.79
0.464
SnowflakeNet
0.477
SeedFFormer [6]
0.72
0.81
0:40
0
0.71
0.50
0.77
1.49
0.92
0.472
ProxyFommer [8]
0.70
0.83
0.34
0.78
0.69
0.49
0.75
1.55
0.93
0.483
EINal
CRA-PON [19]
0.85
0.73
0.48
1.37
Ous
0.65
0.72
0.34
0.80
0.64
1.34
0.72
0.40
0.31
0.47
0.47
0.69
1.34
0.83
0,490
Table 3: Quantitative comparison on Seen ShapeNet-34 test set and Unseen ShapeNet-21 test set. CD-&y for small, medium, and hard cases (CD-S, CD-M, CD-H) are reported (lower is better).
CD-6((x1000)
34 seen categories
21 unseen categories
CD-M
CDH
CD-Avg
CD) M
CDH
FoldingNet [18]
1,86
L81
3.38
2.35
2.76
2.74
5.36
3.62
PCN [4]
1.87
1.81
2,97
3.17
3,08
5.29
3.85
PoinTT [7]
0.76
1.88
1.23
1.60
1.67
3.44
2.05
SeedFormer [6]
0.48
0.70
1.30
0.83
0.61
1.07
2.35
1.34
ProxyFormer [8]
ElNet [39]
0.46
0.68
1,24
0.79
0.59
1.01
2.19
1.26
CRA PCN [19]
0.45
0.65
1.18
0.76
0.55
0.97
2.19
1.24
Ours
0.44
0.64
1.14
0.75
0.53
0.96
2.16
1.22
completions, particularly in regions with fine-grate fine-grained geometry. For instance, PointMAC reconstructs boats (first row) with smooth, continuous surfaces and clearly defined upper structures, while competing methods often produce broken or overly smoothed shapes lacking geometric sharpness. In the case of lamps (second row), our model faithfully recovers thin, articulated components such as the arm and head with precise alignment, whereas prior approaches frequently exhibit distortions, discontinuities, or missing parts in these complex areas. These results demonstrate the benefit of sample-specific refinement for geometric accuracy challenging regions.
Results on ShapeNet-55/34. As shown in Table 2, PointMAC achieves SOTA performance on ShapeNet, demonstrating strong generalization across diverse object categories. Fig. 4 (last two rows) illustrates representative completions. For airplanes (third row), it accurately reconstructs fine structures such propeller blades and tail fins, which prior methods often over-smooth fragment. In the chair category (last row), our model recovers thin, densely arranged back slats with clear spacing and uniform thickness, while other approaches yield blurry slat structures and spurious points in unrelated areas (e.g., seat or legs). We also report results on the 34 seen categories of ShapeNet-34 in Table 3. On the 21 unseen categories, consistent improvement across all difficulty levels and both seen and unseen subsets supports the core motivation of PointMAC: adapting diverse structures and noise beyond training priors.
method achieves the best overall performance. The
Results on MVP. Table 4 presents quantitative results on the MVP simulated scanning dataset. Point- MAC outperforms all baselines on both CD-&z and F-Score metries. In particular, its advantage over CRA-PCN [19] suggests more accurate shape reconstruction and finer geometric detail preservation under realistic occlusions.
4.3
Cross-Datased Evaluation
Results on KITTI. Since there are no paired groundtruth for KITTI, we train our model on ShapeNet- Cars [4] and evaluate it KITTI. As shown in Table 5, PointMAC reduces the fidelity from 0.151 to 0.135 (a 10.6% relative reduction) and simultaneously decreases the MMD from 0.508 to 0.477, un- derscoring the effectiveness of sample-specific adaptation in handling real-world noise and recovering fine-grained geometry beyond training priors.
Table 4: Quantitative comparison MVP dataset. We CD-42
x10* and Fl Score for evaluation.
PCN [4]
TopNer
MSN [42]
CDN [43]
ECG [44]
VRCNet [38]
CRA-PCN [19]
Ours
CD- 4
9.77
10.11
7.90
7.25
6.64
5.96
5,33
5.24
F1
0.320
0.308
0.432
0.434
0.476
0.499
0.529
0.537

---

Table 5: Quantitative comparison on the KITTI dataset. We use the Fidelity Distance and Minimal
Matching Distance (MMD) for evaluation metrics. (Lower Fidelity and MMD are better)
PCN[4]
FoldingNet [1S]
GRNet [di]
[45]
PoinTr [7]
SeedFormer [6]
ProxyFommrr [8]
EINet [39]
Ours
Fidelity
2.255
7.467
5.354
0816
0,000
0.151
0,000
1,48
0.135
MMD!
1,36
0.66
0.568
0.516
0.512
0.477
4.4 Ablation Studies
Impact of Bi-Aux Units. We conduct ab- lation studies on the PCN and ShapeNet-55 datascts. As shown in Table 6, (A) denotes the baseline, while (B) shows that integrating Bi-Aux Units significantly improves perfor- mance across both datasets. This suggests that the self-supervised auxiliary tasks pro- vide reliable gradient signals, enabling the encoder learn more robust and informative features that benefit the primary completion task and enhance overall completion quality.
Table 6: Ablation study on the PCN and ShapeNet-55 datasets. (A)-(C) examine the impact of the auxiliary design: (A) is the baseline; (B) adds Bi-Aux Units; (C) removes the Token Synergy Integrator. (D) is the
full model without Adaptive X-Calibration. (E) is
our full framework with test-time adaptation.
Model
PCN (ł,)
ShapeNet
(A)
Baseline
6.62
0.90
(B)
w/ Bi-Aux Units
6.42
0.85
(C) Token Synergy Integrator
0.86
(D)
w/o Adaptive X-Calibration
6.38
0.84
(E)
Full Model
6.33
0.83
We further observe that removing the shared weights in (C) Token Synergy Integrator disrupts the synergy between the primary and auxiliary tasks, thereby impeding effective information transfer.
Impact of TTA Framework. We evaluate the effectiveness of incorporating TTA with auxiliary signals. As shown in Table 6 (D), this strategy not only removes the need for costly manual tuning but also improves overall model performance. Furthermore, our full model (E) incorporates test-time adaptation with auxiliary signals. During inference, the model performs sample-specific adaptation for each input sample, enabling personalized predictions that better align with the unique characteristies of the input, as illustrated in Fig. 5. While jointly training with Bi-Aux Units (B) effectively improves the overall completion quality, it tends to produce generic completion (e.g., straight lamp arm) that may overlook sample-specific completion (c.g., a circular ring). In contrast, our test-time adaptation strategy personalizes each input by refining the model's internal representation based on its unique geometry or noise pattern, moving beyond global statistical priors. This leads to more detailed and structurally faithful completions. As result, the model is better able to preserve subtle, sample-specific structural cues that are often lost under static inference.
Input
Baseline
w// Bi-Auxiliary Units
Full Model
GT
Figure 5: Visualization of the ablation study on different components of our framework.
5
Conclusion
We propose PointMAC, sample-specific test-time training framework that mitigates the rigidity of static encoder attention via meta-auxiliary learning. Through dynamic inference refinement and stabi- lized adaptation, PointMAC effectively adapts to diverse and unseen scenarios. Experiments across synthetic, simulated, and real-world datasets demonstrate ofate-of-the-ee the- performance, validating its robustness under occlusions and sensor noise.
Limitations and Future Work. Our experiments focus on single-object completion, but the frame- work can naturally extend to scene-level settings. Its label-free adaptation suits real-world scenarios like roboties and AR, where ground-truth supervision is costly. Future work includes scaling to complex environments and incorporating multi-modal guidance enhance completion quality.

---

Acknowledgements
This work was supported in part by NSERC, the National Natural Science Foundation of China (No. 62202199) and Science and Technology Development Plan of Jilin Province (No. 20230101071JC).
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This supplementary document provides additional details to support the main paper.
In Section A, elaborate the implementation of our key components, including the stochastic masked reconstruction, artifact denoising, and the meta-auxiliary training strategy. Section B out- lines the training configurations, hyperparameters, and implementation details. Section C presents additional qualitative results to further demonstrate the effectiveness and generalization ability of our method. Specifically, we include visual comparisons on both the MVP dataset [38] -a high- resolution, multi-view rendered simulation datasct-and the KITTI dataset [11]. which contains real-world LiDAR scans without ground-trum -truth supervision. These visualizations highlight the capa- bility of our test-time adaptation framework to produce accurate, structurally coherent completions across diverse and challenging domains. Additionally, we provide additional completion examples on PCN [4] and ShapeNet [10], including zoom in visualizations of local regions to demonstrate the ability of our method to recover fine- mne-grained, sample-specins geometric details.
A Method Details
A.1 Details of the Stochastic Masked Reconstruction
A.1.1 Region Token and Global Feature Extraction
We first apply Farthest-Point Sampling (FPS) the input point cloud {p.) extract N representative centroids Q = {qu ) k= Each centroid qu is then embedded into region token using a learnable positional encoding module PE(():
Z4 = PE(q:) E ER",
In parallel, shared encoder (-)( processes the full input point cloud P to extract per-point features. These then aggregated using pooling to produce compact global feature vector:
(EX))))(E*(P)).
(10)
A.1.2 Token Synergy Integrator
The Token Synergy Integrator (Irsi) is designed project the global feature vector into token sequence that aligns with the spatially sampled region tokens. This transformation bridges global contextual information and local region-aware representations, enabling consistent conditioning across auxiliary tasks. The structure of the module is defined as:
ITSI(x) = Reltap(((aLLU((N((MIX))))) T ITSI (
(11)
where MaxPool(=2()) denotes 2D max pooling over the feature dimension. The MLP consists of two fully connected layers with intermediate dimensionality 2D, and produces a flattened vector of length N. D, which is reshaped to form the group-token matrix T E NDD.
Since both auxiliary branches, Auz. and Aua.d, require task-specific token generators condi- tioned on the global feature, naively implementing separate modules would introduce redundant parameterization and potential inconsistency. To address this, we share the Irsi module across both auxiliary branches. This design encourages consistent representation learning and facilitates stable and efficient test-time adaptation. In our ablation studies, we observe that removing Irst or breaking the parameter sharing leads to notable performance degradation, underscoring its importance in the overall framework.
A.1.3 Masked Attention for Group-Region Token Fusion
The attention input constructed by concatenating the group tokens 7 E XDD and region tokens { NDD along the token dimension, forming sequence of 2/V tokens with embedding
13

---

dimension D. All tokens are linearly projected using a shared projection matrix W E Rx to obtain the query, key, and value matrices:
Q=k=V= Z W.
(12)
where Z (1:....;ZN CRRV RND denotes the stacked region tokens.
To control contextual interactions, we follow the dual-masking strategy from [34] and construct binary attention mask M {0, 1 by independently sampling each element from a Bernoulli distribution. The mask is applied both the row and column dimensions of the attention matrix and is shared across all attention heads.
After masked attention, the resulting feature sequence JMM RR2xD is aggregated via max pooling along the token dimension to produce compact latent representation flateot E IR D, This step not only compresses token-level representations but also reduces the computational load and parameter count for the subsequent decoder.
A.2 Details of the Artifact Denoising
A.2.1 Noise Injection
To better simulate sensor-induced imperfections observed in real-world 3D seans, we inject point-wise Gaussian noise into the input sparse point cloud P { prior to artifact-aware denoising. For each point p;, the noise standard deviation op is independently sampled from a uniform distribution:
op ~ (0.0001,
(13)
representing 0.1% to 0.5% of the normalized coordinate scale. The perturbed point p then computed as:
= c~ c~
(14)
where I 3x3 is the identity matrix, implying isotropic Gaussian noise added independently to each coordinate axis. To avoid extreme perturbations, each dimension of is clipped to the range M is processed by the auxiliary denoising branch
[-0.02, 0.02]. The resulting noisy input P = {p/ it=
Auaad which applies the mapping T IR x + ReMx3 to reconstruct a clean and dense point cloud P (P).
This procedure captures the heterogeneous and spatially varying noise distributions commonly encountered in practical 3D acquisition scenarios, and supports robust refinement during TTA.
A.3
Details of the Model Learning
A.3.1 Learning a Dnttaaiiiiiinooaionn via Joint Optimization
Joint training assumes that auxiliary tasks consistently benefit the primary objective. However, under distribution shift, static optimization can lead to gradient conflict and negative transfer, where updates from auxiliary tasks misalign with the primary goal. To address this, we adopt MAMI. [17] to meta- optimize the shared parameters, ensuring that adaptations guided by auxiliary losses consistently improve primary performance. Specifically, we first jointly optimize the primary and auxiliary objectives on source-domain data obtain generalizable initialization. Let Lpá Caur and Cal denote the primary and auxiliary task losses, respectively. During this joint training stage, we simultaneously minimize weighted sum of the two objectives:
L = LprilC, P; diah Ppni) + C (P, P; C
pel
sh muxo
aux
Smi
+
Lad
(P,P; sh dh and
(15)
where y E (0, 1] balances the supervised primary loss and the two self-supervised auxiliary losses. This process updates all shared parameters to encourage learning representations that are both effective for the primary task and guided by complementary auxiliary signals.
14

---

The resulting parameters { &pa serve a warm-start for the subsequent meta- veeta-optmizztionn phase, providing a stable initialization that incorporates task-relevant structures learned from both objectives.
A.3.2 Meta-Learned TTA: Training Details
To align the auxiliary tasks with the primary objective under distribution shift, we adopt a meta- auxiliary training procedure that dynamically adjusts the contribution of each auxiliary loss during training. The goal is to ensure that updates guided by auxiliary signals consistently improve the primary performance, thereby yielding a better initialization for test-time adaptation.
Let o = ppp Ppti. &an maxx @mxx denote the full set of parameters, and @axx & denote the task- specific auxiliary heads for stochastic masked reconstruction and artifact denoising. We initialize the adaptive auxiliary weights (Asmr: >ad) in logit space and update them jointly with the model parameters in each iteration. Given a mini-batch {P(t), ((()) ((()) J1=1 we first compute the auxiliary losses Caus and Cad These losses are then combined into a single adaptive auxiliary objective using normalized task-specific weights, obtained via a softmax-like function. Both the auxiliary network parameters and the weighting coefficients are updated through gradient- optimization. Finally, the primary loss is used to update the full model, ensuring that the shared encoder is guided by auxiliary signals that consistently support the primary objective. This process produces an initialization better suited for downstream test-time adaptation. The complete process is illustrated in Alg. 2.
Algorithm 2: Meta-Auxiliary Training
Input: Parameters & = {drh pa Opri &anx: Dann dadx
}: learning rates n5: 7)= ?
Output: meta-trained weights o, calibrated (Aamer Aad)
Initialise Asmrs Asd < 0 (logit space); while not converged do sample mini-batch (p(t),c(t)]? (
auriliary forward
Evaluate the auxiliary losses Laux Laux
X normalisation
ā < -og((1 + X2mn). + log(1 + X2d):
Wsmr < -exp(ā)/(exp(e)
+ exp(3))), Wad
Wsmri
Cada +- Wsmr-ouxx Came + Wad
update aun dranch b update weights
Qaux + daux - 776 Orn
Cuda
(Duma rad) x- --Xamm (Nummr Aaa) aA Cada
7V &ppp
b outer update
Alg. 2 dynamically calibrates the auxiliary loss weights (Asmr: Asa) via per-iteration normalization and gradient-based updates. To prevent task misalignment and ensure that auxiliary supervision consistently benefits the primary task, we embed this process into a meta-learning framework based on MAML. In this formulation, the primary point cloud completion task supervises the optimization of auxiliary branches through outer-loop gradients, enabling the model to leverage auxiliary signals for more effective, sample-specifee adaptation.
A.4 Number of Gradient Updates
The number of gradient updates in the inner loop is a critical hyperparameter in our meta-auxiliary optimization framework. Existing test-time adaptation methods [30] typically adopt fixed and limited number of updates without systematically analyzing its impact on adaptation quality. However, insufficient updates may result in under-adaptation to the target distribution, whereas excessive updates can lead to overfitting on auxiliary tasks.
To investigate this trade-off, report the results in Table 7. All experiments are conducted on the PCN [4] and ShapeNet [10] datasets, with the number of gradient steps kept consistent between training and testing.
evaluate method with different update steps K E (1, and
As shown Table 7, our method outperforms sfate-of-the-art. approaches [8, 6, 39, 19] even with only three gradient updates (K = 3)), and further improves with five updates (K 5). These
15

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Table 7: Ablation on update steps: performance under different numbers of gradient updates (=
1, 3, 5) on PCN and ShapeNet. Lower is better.
Model
PCN (&1 4)
ShapeNet (62
(A) w/ Bi-Aux Units
6,42
0.85
(B) w/ Bi-Aux Units TTA (K
6.40
0.84
Bi-Aux Units TTA (K
6.33
0.83
(D) w/ Bi-Aux Units TTA (K =
6.28
0.81
results underscore the effectiveness of our test-time adaptation strategy in enabling high-quality, sample-specific completions through minimal per-instance optimization.
To balance accuracy and computational efficiency, we set 3 in all subsequent experiments. Investigation of larger update steps (e.g.. K 7) is left for future work.
B Implementation Details
We train the model for 250 epochs on the PCN [4] and ShapeNet [10] datasets, and for 200 epochs on MVP [38]. The batch size is set to 40 for PCN, 32 for ShapeNet, and 44 for MVP. During the joint training phase, we apply equal learning rates for the primary and auxiliary branches, with
= 2.5 10-5
In the meta-trann secta-training and meta-testing stages, we perform inner-loop gradient update steps to adapt the shared encoder using the auxiliary losses Lum and Lnd Optimization is carried out using Stochastic Gradient Descent (SGD) without momentum or weight decay. All experiments are conducted on two NVIDIA V100 GPUs.
C
Visualization
To further demonstrate the strong generalization ability of our test-time adaptation framework, we present additional qualitative results on MVP [38] (Fig.6) and KITTI[I]] (Fig. MVP high- resolution, multi-view rendered benchmark that closely mimics real-world 3D scanning conditions, while KITTI consists of real-world LiDAR seans without ground-truth supervision.
We compare our method with several state-of-the-arr completion approaches, including PoinTr[[],, ProxyFormer [8], and CRA-PCN [19]. Across both datasets, our method consistently produces more complete and detail-preservii often generate over-smoothed outputs and fail adapt to input-specific cues, our method preserves fine- ane-ggrined structures and sharp object boundaries. Compared to CRA-PCN, our approach yields cleaner contours and fewer noisy artifacts, especially in complex or partially occluded regions. These results highlight the strength of our dynamic, sample-specific adaptation strategy and its robustness across both synthetic and real-world domains without relying on ground-truth supervision.
reconstructions. Notably, unlike PoinTr and ProxyFormer, which
In addition, we provide more completion results on samples from PCN [4] and ShapeNet [10] (Fig. 8). including zoomed-in visualizations of local regions. These results further demonstrate the capability of our method to generate sample-specific completions by adapting to the unique structure of each input. Notably, our approach restores fine-grained details-such as thin bars, wings, and structural Prames-arrss. wide range of categories, highlighting the benefit of dynamic refinement over static inference.
16

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Input
CRA-PCN
Ours
GT
Figure 6: Qualitative comparison of point cloud completion results on the MVP dataset [38]. From left right: incomplete input, results from CRA-PCN [19], our method, and ground truth. Compared to CRA-PCN, our completions present clearer structures and finer details with notably less contour noise. Specifically, our method better reconstructs critical regions across different categories: (row 1) the curved backrest of the chair, (row 2) the trigger area of the gun, (row 3) the complex hull structure of the boat, (row 4) the fine-grained details of the bench, and (row 5) the geometry of the lampshade. These results demonstrate the effectiveness of our test-time adaptation approach, which dynamically extracts sample-specifie information to produce structurally accurate and detail- preserving completions.
17

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Input
PoinTr
ProxyFormer
Ours
Figure 7: Qualitative comparison of point cloud completion results the KITTI dataset [11]. From left to right: incomplete input, completion by PoinTr [7], ProxyFormer [8], our method, and ground truth. Among existing methods, ProxyFormer achieves the strongest overall performance; however, its outputs remain generic and often overly smoothed, obscuring fine-grained structures. Poin'Tr similarly struggles to preserve input-specific geometric details. In contrast, dynamic, per-sample refinement via test-time adaptation, resulting more accurate and detailed completions. Notably, our results retain sharp object boundaries and recover distinctive features such as car windows and wheels, demonstrating superior geometric fidelity. Importantly, despite the absence of ground-truth during inference on real-world KITTI data, our approach leverages self-supervised signals to adapt effectively to each input, producing semantically meaningful and structurally consistent completions.
method performs
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Input
Ours
GT
Figure 8: Qualitative results of point cloud completion on samples from the PCN [4] and ShapeNet [10] datasets. From left to right: incomplete input, our completion result, a zoom-in of the region highlighted by the purple bounding box, and ground truth. The examples cover diverse categories such as chairs, cars, airplanes, ships, and lamps. Unlike existing approaches that tend to produce generic completions, our method dynamically adapts to each input and faithfully reconstructs both global object structures and fine-graine details suchi as the curved backrest bars of chairs, car rear wings, airplane tails, ship masts, and lamp frames. The zoomed-in views clearly demonstrate the effectiveness of our test-time adaptation strategy in preserving geometric fidelity and restoring delicate, sample-specific structural elements.
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NeurIPS Paper Checklist
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Please read the checklist guidelines carefully for information on how to answer these questions. For each question in the checklist:
You should answer [Yes], [No], or [NA].
[NA] means either that the question is Not Applicable for that particular paper or the relevant information is Not Available.
Please provide short (1-2 sentence) justifiicton right after your answer (even for NA).
The checklist answers are an integral part of your paper submission. They are visible to the reviewers, area chairs, senior area chairs, and ethics reviewers. You will be asked to also include (after eventual revisions) with the final version of your paper, and its final version will be published with the paper.
The reviewers of your paper will be asked to use the checklist as one of the factors in their evaluation.
While "[Yes] " is generally preferable to "[No] ", it is perfectly acceptable to answer "[No] provided proper justification is given (e.g., "error bars are not reported because it would be too computationally expensive" or "we were unable to find the license for the dataset we used"). In general, answering "[No] or "[NA] is not grounds for rejection. While the questions are phrased in binary way, we acknowledge that the true answer often more nuanced, so please just use your best judgment and write justification to elaborate. All supporting evidence can appear either in the main paper or the supplemental material, provided in appendix. If you answer [Yes] to a question, in the justification please point to the section(s) where related material for the question can be found.
IMPORTANT, please:
Delete this instruction block, but keep the section heading "NeurIPS Paper Checklist",
Keep the checklist subsection headings, questions/answers and guidelines below.
Do not modify the questions and only use the provided macros for your answers.
Claims
Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Answer: [Yes]
Justification:
The main claims stated in the abstract and introduction clearly reflect the paper's contributions,
Guidelines:
The answer NA means that the abstract and introduction do not include the claims made the paper.
The abstract and/or introduction should clearly state the claims made, including the contributions made the paper and important assumptions and limitations. A No or
NA answer to this question will not be perceived well by the reviewers.
The claims made should match theoretical and experimental results, and reflect how much the results can be expected to generalize to other settings.
It is fine to include aspirational goals as motivation as long as is clear that these goals are not attained by the paper.
2. Limitations
Question: Does the paper discuss the limitations of the work performed by the authors? Answer: [Yes]
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Justification:
The paper discusses several limitations
Guidelines:
The answer NA means that the paper has no limitation while the answer No means that the paper has limitations, but those are not discussed in the paper.
The authors are encouraged to create separate "Limitations" section in their paper.
The paper should point out any strong assumptions and how robust the results are to violations of these assumptions (e.g., independence assumptions, noiseless settings, model well-specification. asymptotic approxinations only holding locally). The authors should reflect how these assumptions might be violated in practice and what the implications would be.
The authors should reflect on the scope of the claims made, e.g., if the approach was only tested on a few datasets or with few runs. In general, empirical results often depend on implicit assumptions, which should be articulated.
The authors should reflect the factors that influence the performance of the approach. For example, facial recognition algorithm may perform poorly when image resolution low images are taken in low lighting. Or speech-to-text system might be used reliably to provide closed captions for online lectures because it fails to handle technical jargon.
The authors should discuss the computational efficiency the proposed algorithms and how they scale with dataset size.
If applicable, the authors should discuss possible limitations of their approach to address problems of privacy and fairness.
While the authors might fear that complete honesty about limitations might be used by reviewers as grounds for rejection, a worse outcome might be that reviewers discover limitations that aren't 't acknowledged in the paper. The authors should use their best judgment and recognize that individual actions in favor of transparency play an impor- tant role in developing norms that preserve the integrity of the community. Reviewers will be specifically instructed to not penalize honesty concerning limitations.
3. Theory assumptions and proof'
Question: For each theoretical result, does the paper provide the full set of assumptions and complete (and correct) proof?
Answer: [Yes]
Justification: The theoretical components of the paper include formalization of the meta- objective and role in aligning auxiliary and primary task gradients.
Guidelines:
The answer NA means that the paper does not include theoretical results.
All the theorems, formulas, and proofs in the paper should be numbered and cross- referenced.
All assumptions should be clearly stated referenced in the statement of any theorems.
The proofs can either appear in the main paper or the supplemental material, but they appear in the supplemental material, the authors are encouraged to provide short proof sketch provide intuition.
Inversely, any informal proof provided in the core of the paper should be complemented by formal proofs provided in appendix supplemental material.
Theorems and Lemmas that the proof relies upon should properly referenced.
4. Experimental result reproducibility
Question: Does the paper fully disclose all the information needed to reproduce the main ex- perimental results the paper to the extent that affects the main claims and/or conclusions of the paper (regardless whether the code and data are provided or not)?
Answer: [Yes]
Justification:
The paper provides detailed descriptions of experiments.
Guidelines:
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The answer NA means that the paper does not include experiments.
If the paper includes experiments, a No answer to this question will not be perceived well by the reviewers: Making the paper reproducible is important, regardless of whether the code and data are provided or not.
If the contribution is dataset and/or model, the authors should describe the steps taken to make their results reproducible or verifiable.
Depending on the contribution, reproducibility can be accomplished in various ways. For example, if the contribution is a novel architecture, describing the architecture fully might suffice, or if the contribution is specific model and empirical evaluation, it may be necessary to either make it possible for others to replicate the model with the same dataset, provide access to the model. In general. releasing code and data is often one good way to accomplish this, but reproducibility can also be provided via detailed instructions for how replicate the results, access to a hosted model (e.g., in the case of a large language model), releasing of a model checkpoint, or other means that are appropriate to the research performed.
While NeurIPS does not require releasing code, the conference does require all submis- sions to provide some reasonable avenue for reproducibility, which may depend on the nature of the contribution. For example
(a) the contribution is primarily a new algorithm, the paper should make clear how reproduce that algorithm. (b) If the contribution is primarily new model architecture, the paper should describe the architecture clearly and fully.
(c) If the contribution is a new model (e.g., a large language model), then there should either be way to access this model for reproducing the results or way to reproduce the model (e.g., with an open- source dataset instructions for how to construct the dataset).
(d) We recognize that reproducibility may be tricky in some cases, in which case authors welcome deseribe the particular way they provide for reproducibilityy In the case of closed-source models, it may be that access to the model is limited in some way (e.g., registered users), but it should be possible for other researchers to have some path to reproducing or verifying the results.
5. Open access to data and code
Question: Does the paper provide open access to the data and code, with sufficient instruc- tions to faithfully reproduce the main experimental results, as described in supplemental material?
Answer: [Yes]
Justification:
We will release the code and data processing scripts upon publication.
Guidelines:
The answer NA means that paper does not include experiments requiring code.
Please see the NeurlPS code and data submission guidelines (https://nips cc/
Beeiiii/guudes/oodeSuue stibbmmmssiooooicccy..... for more details.
While we encourage the release of code and data, we understand that this might not be possible, "No" is an acceptable answer. Papers cannot be rejected simply for not including code, unless this is central to the contribution (e.g., for a new open-source benchmark).
The instructions should contain the exact command and environment needed to run to reproduce the results. See the NeurlPS code and data submission guidelines (https: //n cts.sss.cc/uubic/guuuuu. gesds//////eeeeeee/o /CodeSubmian ss ierionnoolliii onoolicy) for more details.
The authors should provide instructions on data access and preparation, including how to access the raw data, preprocessed data, intermediate data, and generated data, etc.
The authors should provide scripts to reproduce all experimental results for the new proposed method and baselines. If only subset of experiments are reproducible, they should state which ones are omitted from the script and why.
At submission time, to preserve anonymity, the authors should release anonymized versions (if applicable).
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Providing as much information as possible in supplemental material (appended to the paper) recommended, but including URLs to data and code permitted.
6. Experimental setting/details
Does the paper specify all the training and test details (e.g., data splits, hyper- parameters, how they were chosen, type of optimizer, etc.) necessary to understand the results?
Question:
Answer: [Yes]
Justification:
The paper specifies all relevant training and test settings.
Guidelines:
The answer NA means that the paper does include experiments.
The experimental setting should be presented in the core the paper to a level detail that is necessary appreciate the results and make sense of them.
The full details can be provided either with the code, in appendix, as supplemental material.
7. Experiment statistical significance
Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?
Question:
Answer: [Yes]
Justification: The paper reports mean performance along with standard deviation across three independent runs with different random seeds.
Guidelines:
The answer NA means that the paper does not include experiments.
The authors should answer "Yes" if the results are accompanied by error bars, confi- dence intervals, or statistical significance tests, at least for the experiments that support the main claims of the paper.
The factors of variability that the error bars are capturing should be clearly stated (for example, train/test split. initialization, random drawing of some parameter, or overall run with given experimental conditions).
The method for calculating the error bars should be explained (closed form formula, call to a library function, bootstrap, etc.)
The assumptions made should be given (e.g.. Normally distributed errors).
should be clear whether the error bar is the standard deviation or the standard error of the mean.
OK to report 1-sigma error bars, but one should state it. The authors should preferably report a 2-sigma error bar than state that they have 96% CI, if the hypothesis of Normality errors is not verified.
For asymmetric distributions, the authors should be careful not to show in tables or figures symmetric error bars that would yield results that are out range (c.g. negative еттог rates).
If error bars are reported in tables or plots, The authors should explain in the text how they were calculated and reference the corresponding figures or tables in the text.
8. Experiments compute resources
For each experiment, does the paper provide sufficient information on com- puter resources (type of compute workers, memory, time of execution) needed to reproduce the experiments?
Question:
Answer: [Yes]
The paper reports the type of compute resources used, including NVIDIA A100 GPUs.
Justification:
Guidelines:
The answer NA means that the paper does not include experiments.
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The paper should indicate the type of compute workers CPU or GPU, internal cluster, or cloud provider, including relevant memory and storage.
The paper should provide the amount compute required for each of the individual experimental runs well estimate the total compute.
The paper should disclose whether the full research project required more compute than the experiments reported in the paper (e.g., preliminary or failed experiments that didn't make into the paper).
9. Code of ethics
Question: Does the research conducted in the paper conform, in every respect, with the NeurlPS Code of Ethics httpppss////ui/.....p httpss//earrppp.ccppppl//c//// d ines?
Answer: [Yes]
Justification:
Our research complies with the NeurIPS Code of Ethics.
Guidelines:
The answer NA means that the authors have reviewed the NeurIPS Code of Ethics.
If the authors answer No, they should explain the special circumstances that require deviation from the Code of Ethics.
The authors should make sure to preserve anonymity (e.g.. if there is special consid- cration due to laws or regulations in their jurisdiction).
Broader impacts
Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?
Answer: [Yes]
Justification: Our method improves robustness and adaptability in 3D point cloud comple- tion.
Guidelines:
The answer NA means that there is no societal impact of the work performed.
If the authors answer NA No, they should explain why their work has societal impact or why the paper does not address societal impact.
Examples of negative societal impacts include potential malicious or unintended uses (e.g., disinformation, generating fake profiles, surveillance), fairness considerations (e.g., deployment of technologies that could make decisions that unfairly impact specific groups), privacy considerations, and security considerations.
The conference expects that many papers will be foundational research and not tied partieular applications, let alone deployments. However, if there is direct path any negative applications, the authors should point it out, For example, it is legitimate point that improvement in the quality of generative models could be used to generate deepfakes for disinformation. On the other hand, it is not needed to point out that a generic algorithm for optimizing neural networks could enable people train models that generate Deepfakes faster.
The authors should consider possible harms that could arise when the technology is being used as intended and functioning correctly, harms that could arise when the technology is being used intended but gives incorrect results, and harms following from (intentional or unintentional) misuse of the technology.
If there are negative societal impacts, the authors could also discuss possible mitigation strategies (e.g., gated release of models, providing defenses in addition attacks, mechanisms for monitoring misuse, mechanisms to monitor how system learns from feedback over time, improving the efficiency and accessibility of ML).
11. Safeguards
Does the paper describe safeguards that have been put in place for responsible release of data or models that have a high risk for misuse (c.g., pretrained language models, image generators, scraped datasets)?
Question:
Answer: [NA]

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Justification: Our work does not involve models or datasets with high risk of misuse. Guidelines:
The answer NA means that the paper poses no such risks.
Released models that have a high risk for misuse or dual-us usal-use should be released with necessary safeguards to allow for controlled use of the model, for example by requiring that users adhere to usage guidelines or restrictions to access the model or implementing safety filters.
Datasets that have been scraped from the Internet could pose safety risks. The authors should describe how they avoided releasing unsafe images.
We recognize that providing effective safeguards is challenging, and many papers do not require this, but we encourage authors to take this into account and make best faith effort.
12. Licenses for existing assets
Are the creators or original owners of assets (e.g., code, data, models), used in the paper, properly credited and are the license and terms of use explicitly mentioned and properly respected?
Question:
Answer: [Yes]
Justification: All datasets and codebases used in this paper publicly available and properly cited with their corresponding licenses and terms of use. used ShapeNet and ScanNet under their respective academic licenses, and all third-party code is credited in the references or supplemental material.
Guidelines:
The answer NA means that the paper does not existing assets.
The authors should cite the original paper that produced the code package dataset.
The authors should state which version of the asset used and, possible, include URL
The name of the license (e.g., CC-BY 4.0) should be included for each asset.
For scraped data from a particular source (e.g., website), the copyright and terms of service of that source should be provided.
If assets are released, the license, copyright information, and terms of use in the package should be provided. For popular datasets, paperswi thcode. www.co/////ooooo datasets has curated licenses for some datasets. Their licensing guide can help determine the license of dataset.
For existing datasets that are re-packaged, both the original license and the license of the derived asset (if it has changed) should be provided.
If this information is available online, the authors encouraged to reach out the asset's 's creators.
13. New assets
Question: Are assets introduced in the paper well documented and is the documentation provided alongside the assets?
Answer: [NA]
Justification:
The paper does not introduce any new datasets.
Guidelines:
The answer NA means that the paper does not release new assets.
Researchers should communicate the details of the dataset/code/model as part of their submissions via structured templates. This includes details about training, license, limitations, etc.
The paper should discuss whether and how consent was obtained from people whose asset used.
At submission time, remember to anonymize your assets (if applicable). You can either create anonymized URL or include an anonymized zip file.
14. Crowdsourcing and research with human subjects
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Question: For crowdsourcing experiments and research with human subjects, does the paper include the full text of instructions given to participants and screenshots, if applicable, as well as details about compensation (if any)?
Answer: [NA]
Justification:
This paper does not involve crowdsourcing or research with human subjects.
Guidelines:
The answer NA means that the paper does not involve crowdsourcing research with human subjects.
Including this information in the supplemental material is fine, but if the main contribu- tion of the paper involves human subjects, then as much detail as possible should be included in the main paper.
According to the NeurIPS Code of Ethies, workers involved in data collection, curation, or other labor should be paid at least the minimum wage in the country of the data collector.
15. Institutional review board (IRB) approvals or equivalent for research with human subjects
Does the paper describe potential risks incurred by study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals (or equivalent approval/review based the requirements of your country or institution) were obtained?
Question:
Answer: [NA]
Justification: This work does not involve human subjects or participant-basdd studies.
Guidelines:
The answer NA means that the paper does not involve crowdsourcing research with human subjects.
Depending on the country in which research is conducted, IRB approval (or equivalent)
may be required for any human subjects research. If you obtained IRB approval, you should clearly state this in the paper.
We recognize that the procedures for this may vary significantly between institutions and locations, and we expect authors to adhere to the NeurIPS Code of Ethics and the guidelines for their institution.
For initial submissions, do not include any information that would break anonymity (if applicable), such as the institution conducting the review.
16. Declaration of LLM usage
Question: Does the paper describe the usage of LLMs if it is an important, original, or non-standard component of the core methods in this research? Note that if the LLM is used only for writing, editing, formatting purposes and does not impact the core methodology, scientific rigorousness, or originality of the research, declaration is not required.
Answer: [NA]
Justification: Large language models (LLMs) are not used as part of the core methodology in this work.
Guidelines:
The answer NA means that the core method development in this research does not involve LLMs as any important, original, non-standard components.
Please refer to our L.LM policy httpps://neurriii httprrppss.pccooeeeeeeeee/////// for what should or should not be described.
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Reviews:
[{"review_id": "iebWNwmA1C", "reviewer": "Reviewer_sY8X", "summary": "PointMAC proposes a novel test-time adaptation framework for point cloud completion that enables per-sample refinement without requiring labels. Unlike existing models that rely on static inference and training-set priors, PointMAC adapts its shared encoder at test time using two self-supervised auxiliary tasks—Stochastic Masked Reconstruction and Artifact Denoising—which simulate occlusions and sensor noise, respectively. A meta-learning strategy based on MAML ensures that these auxiliary adaptations consistently benefit the main completion task, while an Adaptive λ-Calibration mechanism dynamically balances the influence of auxiliary losses. Extensive experiments across synthetic, simulated, and real-world datasets show that PointMAC achieves state-of-the-art performance by producing sample-specific completions that generalize well beyond training data.", "questions": "* What is the computational cost (e.g., runtime per sample, FLOPs, or memory overhead) of the proposed test-time adaptation step?\n\n* Have you attempted to apply your test-time adaptation module to any existing static inference baselines (e.g., PCN or PoinTr)? If not, could you comment on its feasibility and any practical challenges?\n\n* Consider revising Figures 2 and 3 to better distinguish between shared components, auxiliary branches, and adaptation steps. A clearer flow would help readers follow the interactions between modules more easily.\n\n* Please consider expanding the related work section to include recent test-time adaptation methods specifically designed for 3D point clouds.", "limitations": "Yes.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "__Strengths:__\n* The paper is technically solid and methodologically well-designed.\n* The use of self-supervised auxiliary tasks (masked reconstruction and denoising) is thoughtfully designed to simulate real-world data challenges\n* The experimental evaluation is extensive, covering synthetic, simulated, and real-world datasets.\n* The combination of sample-specific refinement and MAML in the context of 3D point cloud completion is impactful.\n\n__Weaknesses:__\n* The block diagrams could be made more modular and annotated to better illustrate the interactions between the encoder, auxiliary branches, and meta-learning loops.\n* The pipeline involves several components (meta-learning, auxiliary decoders, adaptive balancing), making reproducibility difficult without access to code. More implementation details or pseudocode would help.\n* The paper lacks analysis of the computational overhead introduced by test-time adaptation. Reporting runtime or FLOPs would clarify its practicality in real-time settings.\n* The proposed TTA method could likely be applied to static baselines (e.g., PCN, PoinTr) with some integration. Demonstrating this would show broader utility.\n* The paper could be strengthened by referencing recent point cloud-specific TTA methods like:\n  - “Test-Time Adaptation of 3D Point Clouds via Denoising Diffusion Models”\n  - “CloudFixer: Test-Time Adaptation for 3D Point Clouds via Diffusion-Guided Geometric Transformation”", "was_revised": true, "final_justification": "The rebuttal satisfactorily addresses my main concerns. The authors provided computational cost analysis (Table A), demonstrated plug-and-play applicability on PoinTr and SnowflakeNet (Table B), and committed to improving figures, expanding the related work to contain recent 3D point cloud TTA methods (e.g., 3DD-TTA, CloudFixer), and releasing code. These clarifications strengthen the paper’s technical soundness. I maintain my borderline accept recommendation, now with higher confidence.", "rebuttal": "We sincerely thank Reviewer sY8X for the valuable suggestions. \n# Q1: Computational Cost \nIn Rebuttal Table A, we report computational cost in terms of model parameters, FLOPs, and ms/sample. As shown in the table, our model is lightweight compared to existing ones, while still achieving the best performance.\nTTA adds a relatively small amount of overhead to the inference time since most gains are obtained within the first few adaptation steps. The inference time is reported under three adaptation steps.\nPlease note that ms/sample is reported using one Nvidia V100. \n\n**Rebuttal Table A**. Computational cost.\n\n| Method       | Params (M) | FLOPs (G) | Inference (ms/sample) |\n|--------------|------------|-----------|-----------------------|\n| GRNet        | 76.71      | 40.44     | 45                    |\n| SnowflakeNet | 19.32      | 10.32     | 19                    |\n| PoinTr       | 30.9       | 10.41     | 22                    |\n| ProxyFormer  | 12.16      | 9.88      | 42                    |\n| CRA-PCN      | 7.46       | 8.13      | 27                    |\n| Ours (w/o TTA)        | 10.92      | 9.26      | 21 (w/o TTA)          |\n| Ours (w/  TTA)        | 10.92      | 9.26      | 36 (w/ TTA)           |\n\n# Q2: Evaluating TTA on General Backbones\nWe agree that such experiments are essential for demonstrating the broader applicability of our method. As shown in Rebuttal Table B, we have **validated the plug-and-play effectiveness of our TTA** on PoinTr and SnowflakeNet, to demonstrate practical generalization. \n\n**Rebuttal Table B**. TTA on general backbones.\n| Backbone (CD-ℓ₁)   | Method             | Plane | Cabinet | Car  | Chair | Lamp | Couch | Table | Boat | Avg  |\n|---------------------|--------------------|-------|---------|------|-------|------|-------|-------|------|------|\n| PoinTr              | baseline           | 4.75  | 10.47   | 8.68 | 9.39  | 7.75 | 10.93 | 7.78  | 7.29 | 8.38 |\n| PoinTr              | w/ Bi-Aux          | 4.61  | 10.15   | 8.39 | 9.11  | 7.57 | 10.63 | 7.53  | 7.09 | 8.14 |\n| PoinTr              | w/ (Bi-Aux + TTA)  | 4.59  | 9.98    | 8.26 | 8.95  | 7.41 | 10.37 | 7.39  | 6.95 | 7.98 |\n| SnowflakeNet        | baseline           | 4.29  | 9.16    | 8.08 | 7.89  | 6.07 | 9.23  | 6.55  | 6.40 | 7.21 |\n| SnowflakeNet        | w/ Bi-Aux          | 4.13  | 8.94    | 7.80 | 7.65  | 5.93 | 8.99  | 6.34  | 6.22 | 7.00 |\n| SnowflakeNet        | w/ (Bi-Aux + TTA)  | 4.08  | 8.70    | 7.69 | 7.57  | 5.86 | 8.75  | 6.20  | 6.09 | 6.86 |\n\n\n# Q3: Figure Clarity and Workflow\nWe will revise Figures 2 and 3 accordingly in the final version. Please also refer to **Algorithm 2 in Supplementary A.3.2** for the detailed Meta-Auxiliary Training process. \nWe will release the source code upon acceptance.\n\n# Q4: Expand Related Work\n\n\nWe agree that adding recent TTA methods for 3D point clouds, such as 3DD-TTA [1] and CloudFixer [2], will strengthen our related work section. \n\nWe will include these approaches, which primarily focus on **input-level adaptation** (e.g., modifying the test data distribution via diffusion or denoising). In contrast, **our model adopts a model-level adaptation** strategy, dynamically updating model parameters for each test sample. We will clarify these distinctions and expand our discussion in the revised manuscript. We welcome any further recommendations from the reviewer.\n\n\n[1] Test-time adaptation of 3d point clouds via denoising diffusion models.\n\n[2] Cloudfixer: Test-time adaptation for 3d point clouds via diffusion-guided geometric transformation.\n\n**[Dear Reviewer sY8X]** We value your feedback and look forward to discussing any further concerns you may have."}, {"review_id": "DgkmVrKzbv", "reviewer": "Reviewer_SjCL", "summary": "To address the domain gap between training data and application, this paper proposed a time-time adaptation method for point cloud completion, named PointMAC. This method optimizes the completion model under two self-supervised auxiliary objectives that simulate structural and sensor-level incompleteness. This is also the first work to apply meta-auxiliary test-time adaptation to point cloud completion.", "questions": "1. The Limitations and Future Work part mainly discussed the future work. What is the limitation about this work in the object-level completion setting. Or in other words, under what kind of setting this work may fail?\n2. Please provide some stats for computational cost.", "limitations": "yes", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1. The first method to apply meta-auxiliary test-time adaptation to point cloud completion.\n2. good performance\n\nWeaknesses:\n1. cherry-pick concern: Figure 1 is not a convincing proof or illustration of the claim \"model tends to generate what we refer to as generic completions\". The plane tail structure from your method can be a cherry-picked result. I am not sure why you do adaptation with supervision constructed from a plane without tail and can give the model information about tails. Therefore, it may just be a cherry-picked result. \n\n2. be more specific about the claim: On the other hand, it is validated by many sources that deterministic method tend to produce average prediction (generic completions in the point cloud completion case) while probabilistic method can produce results with sharp and clean features. See Figure 2. of Diffusion Models as Masked Autoencoders. Therefore, I think the authors need to be more specific about this claim. \n\n3. concern about application and significance: it would be also important for a completion method to be fast and real-time. Therefore, the frame per second would be an important metric. The authors should provide some about the computation cost to obtain the good performance. On the one hand, lost real-time processing capability means it can only be applied in limited uses cases, on the other hand, the performance is kind of incremental (small improvement in Table 2 and 3), people need to consider whether it worth doing the proposed TTA.", "was_revised": true, "final_justification": "My final justification is maintaining my positive rating.", "rebuttal": "We sincerely thank Reviewer SjCL for constructive comments.\n> **Question**\n# Q1: Limitations and Failure Cases\n\nWhile TTA improves completion in many challenging cases as compared with existing approaches, it still has inherent limitations under certain conditions. Specifically, our sample-specific TTA is unable to **“hallucinate”** plausible fine-grained geometry beyond what is **already encoded in the model’s priors**. Therefore, when objects have extremely complicated geometric structures and the input point cloud provides minimal information, the adaptation process may still overfit to the limited input, resulting in blurry completions, especially for categories like birdhouses.\n\nFor future work, we intend to explore TTA for **post-training quantized (PTQ) generative models** [1], aiming to enhance completion accuracy in challenging scenarios while maintaining the diversity of generated results. We will clarify this limitation and discuss these potential directions in the revised manuscript.\n# Q2: Computational Cost\nIn Rebuttal Table A, we report computational cost in terms of model parameters, FLOPs, and ms/sample. As shown in the table, our model is lightweight compared to existing ones, while still achieving the best performance.\nTTA adds a relatively small amount of overhead to the inference time since most gains are obtained within the first few adaptation steps. The inference time is reported under three adaptation steps.\nPlease note that ms/sample is reported using one Nvidia V100. \n\n**Rebuttal Table A**. Computational cost.\n\n| Method       | Params (M) | FLOPs (G) | Inference (ms/sample) |\n|--------------|------------|-----------|-----------------------|\n| GRNet        | 76.71      | 40.44     | 45                    |\n| SnowflakeNet | 19.32      | 10.32     | 19                    |\n| PoinTr       | 30.9       | 10.41     | 22                    |\n| ProxyFormer  | 12.16      | 9.88      | 42                    |\n| CRA-PCN      | 7.46       | 8.13      | 27                    |\n| Ours (w/o TTA)        | 10.92      | 9.26      | 21 (w/o TTA)          |\n| Ours (w/  TTA)        | 10.92      | 9.26      | 36 (w/ TTA)           |\n\n> **Weakness**\n# W1: Cherry-picking Concern on Figure 1\nFigure 1 shows a typical example from the PCN test set. The improvement can also be observed in the results shown in Fig. 4 and Fig. 5 of the main paper.\nThey illustrate the primary benefit of our sample-specific adaptation. We believe the underlying reason for getting the improvement is that the inner-loop optimization in TTA with MAML updates model parameters along the gradient of the self-supervised loss, gradually steering the model output toward globally and semantically plausible completions (e.g., an airplane with a reasonable tail) for each test sample. The outer-loop meta-optimization, driven by the completion task, ensures that the auxiliary branches provide effective adaptation signals. Please refer to **Alg. 2 in Supplementary A.3.2 (Lines 846–866)**. \n\nThis represents a **paradigm shift from traditional static**, **one-size-fits-all inference**. As a result, improvements are **consistent rather than cherry-picked**. This is also validated in Tables 1–5 **(across all benchmark datasets)** of the original submission. We will include more compelling examples in the final version to further demonstrate its generalization ability.\n# W2: Specifics of the Claim (Conventional Generic Completion vs. Our Sample-specific Completion with TTA)\nWe appreciate the reviewer’s insightful suggestion and agree that it is important to clarify this distinction. Basically, in point cloud completion, there are two main types of models: deterministic models and probabilistic models (e.g., diffusion models). In this work, we study deterministic models that usually yield averaged (generic) predictions in existing works due to mean regression. That motivates us to develop a sample-specific adaptation strategy to predict the geometry details for each test sample. \n# W3: Concern about Application and Significance\n\nAs shown in Rebuttal Table A, our method consistently achieves improved accuracy while maintaining a reasonable computational cost. Moreover, for **real-world deployment**, where test and training data often differ significantly due to changes in sensors, objects, and other factors, **domain shift** can lead to performance degradation when deploying a fixed, pre-trained model. On the contrary, our **TTA approach enables continuous personalization to new environments and samples** by dynamically adapting to each test instance. This claim is also **validated in our cross-dataset evaluation** as shown in Table 5 of the main paper and Fig. 7 in the Supplementary (Line 911)\n\n[1] Post-training quantization on diffusion models.\n\n\n**[Dear Reviewer SjCL]** We value your feedback and look forward to discussing any further concerns you may have."}, {"review_id": "0DTKxeyOt9", "reviewer": "Reviewer_PV7J", "summary": "This paper proposes PointMAC, a novel meta-learning framework for robust test-time adaptation in point cloud completion, aiming to address the limitations of existing models that perform static inference and rely heavily on training-learned inductive biases. Extensive experiments on several benchmarks show its effectiveness.", "questions": "Refer to the Weakness.", "limitations": "The author analyzes the limitations and societal impacts adequately.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Goodness:\n\n1.\tThe work introduces a novel application of meta-auxiliary learning and test-time adaptation (TTA) to point cloud completion, to address the static inference limitations of prior models.\n\n2.\tThis paper accounts for the the static inference limitations of prior models, particularly under novel occlusions or sensor-induced distortions. PointMAC directly tackles this problem with sample-specific adaptation, enabling the model to refine predictions from each unique geometry and noise of input, leading to high-quality completions.\n\n3.\tExtensive experiments demonstrate its effectiveness.\n\n4.\tThis paper is written and organized well.\n\nWeakness:\n\n1.\tSince the proposed Bi-Aux Units module has the ability to learn the specific part of the input, it will be interesting to add more visualization results of the simulated results. \n\n2.\tTest-time adaptation (TTA) inherently involves performing several gradient update steps for each sample during inference. This inevitably increases the per-sample inference time. It will be interesting to report on the computational cost impact of this component.\n\n3.\tThe proposed Bi-Aux Units consist of two specific self-supervised tasks: stochastic masked reconstruction and artifact denoising. While these aim to simulate common structural incompleteness and sensor noise, their sufficiency in covering all possible or more extreme types of missing data and distortion patterns could be further explored. \n\n4.\tAlthough Adaptive λ-Calibration addresses the balancing of auxiliary task weights, the MAML framework can be sensitive to hyperparameter choices. Further discussion on the selection strategy for these hyperparameters and their impact on final performance would be beneficial.", "was_revised": true, "final_justification": "The authors addressed my concerns and questions.  I will maintain my rating", "rebuttal": "We thank Reviewer PV7J for the recognition and constructive comments.\n\n# Q1: Bi-Aux Units Visualization\n\nWe sincerely thank the reviewer for this suggestion. We agree that additional visualizations—such as attention maps, feature activations, and region-wise responses—will clarify how Bi-Aux Units focus on specific input regions and improve interpretability. We will include these in the revision and welcome further feedback.\n\n# Q2: Computational Cost \nIn Rebuttal Table A, we report computational cost in terms of model parameters, FLOPs, and ms/sample. As shown in the table, our model is lightweight compared to existing ones, while still achieving the best performance.\nTTA adds a relatively small amount of overhead to the inference time since most gains are obtained within the first few adaptation steps. The inference time is reported under three adaptation steps.\nPlease note that ms/sample is reported using one Nvidia V100. \n\n**Rebuttal Table A**. Computational cost.\n\n| Method       | Params (M) | FLOPs (G) | Inference (ms/sample) |\n|--------------|------------|-----------|-----------------------|\n| GRNet        | 76.71      | 40.44     | 45                    |\n| SnowflakeNet | 19.32      | 10.32     | 19                    |\n| PoinTr       | 30.9       | 10.41     | 22                    |\n| ProxyFormer  | 12.16      | 9.88      | 42                    |\n| CRA-PCN      | 7.46       | 8.13      | 27                    |\n| Ours (w/o TTA)        | 10.92      | 9.26      | 21 (w/o TTA)          |\n| Ours (w/  TTA)        | 10.92      | 9.26      | 36 (w/ TTA)           |\n\n\n# Q3: Evaluation under Extreme Patterns\n\nWe thank the reviewer for this important question. We explicitly evaluate the robustness of our approach on both synthetic and real-world datasets under severe structural incompleteness and sensor degradation. **Visualizations of extreme patterns are provided in Supplementary Sec. C (Line 911).**\n* On ShapeNet **(synthetic)**, we tested with up to 75\\% of input points missing across 55 categories, as reported in Tables 2–3 of the main paper (CD-H metric). \n* On KITTI **(real-world)**, the input is even sparser (averaging 440 points, or 5.4\\% of the completion target, with some samples below 10 points), where our model still achieves strong performance. \n\nImportantly, TTA with meta-auxiliary learning provides a **general framework**: the auxiliary task is not restricted to enumerating all possible failure modes. Instead, it can be **tailored to different objectives as needed**, fundamentally overcoming the limitations of static, pre-defined strategies. This enables dynamic, per-sample adaptation to previously unseen patterns, allowing our model to robustly recover fine details where traditional static training or enumerative augmentation would fail.\n\n# Q4: Hyperparameter Choices\n\nWe appreciate the reviewer’s concern regarding hyperparameter sensitivity. Please see **Supplementary Sec. A4 (Lines 867–881)**, where we found that the number of **gradient steps** is the most impactful factor and have provided a thorough analysis. Furthermore, our adaptive $\\lambda$-calibration with MAML is designed to **alleviate the need for expensive manual hyperparameter tuning** by learning suitable hyperparameters during meta-training. As such, the learning rate simply follows the initial task-specific setting.\n\n\n**[Dear Reviewer PV7J]** Thank you again for your rigorous and constructive review, which has helped us further improve the quality of our work."}, {"review_id": "brChZjAllO", "reviewer": "Reviewer_FjXF", "summary": "In this paper, the authors proposed Bi-Aux Units, which include two self-supervised tasks, Stochastic Masked Reconstruction and Artifact Denoising, and adopted a MAML-based meta-auxiliary learning strategy to align the auxiliary tasks with the primary point cloud completion objective, so that only the shared encoder can be adjusted during the test time to perform sample-specific adaptation for each sample. Experiments on large-scale synthetic, simulated, and real datasets verify the leading performance of this method.", "questions": "Q1. The coverage of the robustness evaluation of PointMAC is not clear. This paper proposes Stochastic Masked Reconstruction and Artifact Denoising to enhance the robustness of the encoder for point cloud completion tasks. The experiment lacks analysis of interference such as extreme occlusion and different noise intensities.\n\nQ2. The explanation of the generalization ability of PointMAC is insufficient. Please further explain the effectiveness of the applied TTA mechanism in cross-dataset/cross-category scenarios. The article only uses ShapeNet-Cars for training and tests on KITTI, which is not enough to show that the model can avoid overfitting and maintain stable performance. It is recommended to further supplement cross-dataset experiments.\n\nQ3. Some design details are not explained. Please explain the specific motivation and rationality of choosing Farthest-Point Sampling (FPS) instead of other sampling methods in Stochastic Masked Reconstruction, and using Gaussian noise instead of other noise to simulate sensor artifacts in Artifact Denoising. Please explain in detail how the learnable positional encoder in Stochastic Masked Reconstruction is designed and trained. At the same time, the impact of adaptive steps, learning rate and other hyperparameters on stability and efficiency during testing is not discussed in depth, making it difficult to fully understand the design motivation and decision.\n\nMinor issues: In section 4.3 Cross-Dataset Evaluation, the text says that CD-l2 and F1 are used, but Table 5 lists Fidelity and MMD. Please confirm whether the descriptions of the two indicators are consistent.", "limitations": "The work listed the limitation, but only gave the future possible direction that can be easily extended. It is less informative.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "S1. It proposes a practical solution to the problem that point clouds are incomplete due to occlusion and noise in real scenes, which is significatn.\n\nS2. The proposed MAML-based strategy is intuitive.\n\nW1. Insufficient evaluation of PointMAC robustness. The current experiment only examines limited noise intensity and occlusion ratio, lacks a systematic evaluation of heavy occlusion and abnormal noise distribution scenes, and it is difficult to prove the stability of the model under extreme real-world conditions.\n\nW2. Insufficient evaluation of PointMAC generalization. In addition to testing KITTI after ShapeNet-Cars training, the paper does not show a systematic comparison across categories and dataset domains, so it cannot fully demonstrate that the method can fully handle real-world noise.\n\nW3. Insufficient interpretability of some modules in the PointMAC design architecture and lack of explanation for hyperparameters. For example, the learnable positional encoder in Bi-Aux Units is not explained, and key hyperparameters such as the number of adaptive steps and learning rate during testing are not given.", "was_revised": true, "final_justification": "The authors added an additional experiment on cross dataset experiment, which somehow validates the robustness of the proposed method.", "rebuttal": "We thank Reviewer FjXF for the valuable comments. Specific concerns are also addressed below.\n\n# Q1: Robustness Evaluation Coverage\n## Q1-1: Extreme Occlusion Evaluation\nWe thank the reviewer for highlighting this important aspect. We explicitly evaluate the robustness of our approach on both synthetic and real-world datasets under severe structural incompleteness and sensor degradation. **Visualizations of extreme patterns are provided in Supplementary Sec. C (Line 911).**\n* On ShapeNet **(synthetic)**, we tested with up to 75\\% of input points missing across 55 categories, as reported in Tables 2–3 of the main paper (CD-H metric). \n* On KITTI **(real-world)**, the input is even sparser (averaging 440 points, or 5.4\\% of the completion target, with some samples below 10 points), where our model still achieves strong performance. \n## Q1-2: Noise-Level Diversity\nWe inject varying noise intensities to improve robustness **(see Supplementary Sec. A.2, Lines 816-829)**. Furthermore, KITTI scans naturally contain a wide range of noise intensities, density, and missing regions caused by sensor and environmental factors, making it an ideal benchmark for evaluating robustness. \n## Q1-3: Benchmark Coverage\nTo the best of our knowledge, our approach is among the most thoroughly evaluated, achieving SOTA on **four major benchmarks** (PCN, ShapeNet, MVP, KITTI), while most **prior works report on only two or three**. We welcome suggestions and are willing to conduct additional experiments as suggested.\n# Q2: Cross-dataset Experiment\nIn addition to KITTI, we evaluated additional cross-dataset experiments with the PCN-pretrained model on Completion3D dataset[1]:\n\n**Rebuttal Table A.** Cross-dataset Results on Completion3D.\n\n| CD (ℓ₂)      | Plane | Cabinet | Car  | Chair | Lamp | Couch | Table | Boat | Avg   |\n|--------------|-------|---------|------|-------|------|-------|-------|------|-------|\n| ProxyFormer  | 4.15  | 13.53   | 7.81 | 8.44  | 7.01 | 11.73 | 7.28  | 5.05 | 8.12  |\n| CRA-PCN      | 4.03  | 13.36   | 7.60 | 7.92  | 6.33 | 11.21 | 6.82  | 4.29 | 7.70  |\n| Ours         | 3.77  | 12.49   | 7.17 | 7.42  | 5.86 | 10.54 | 6.32  | 3.85 | 7.18  |\n\n# Q3: Explanation of Design Details\n## Q3-1: Choice of Farthest-Point Sampling (FPS)\nWe adopt FPS as the **default masking strategy** in this field, following a well-established standard [2-4], which has shown FPS produces more uniform and globally distributed missing regions than random or grid-based sampling. This characteristic is especially beneficial for our point cloud completion task, enabling the model to better infer unseen parts when facing missing regions.\n## Q3-2: Choice of Gaussian Noise\nWe choose Gaussian noise because it is the **standard choice for simulating sensor artifacts** in point cloud learning [5-6]. Measurement errors in real-world 3D sensors are well-approximated by a Gaussian distribution (per the central limit theorem).\n## Q3-3: Positional Encoder\nThe learnable positional encoder is implemented as an **MLP** that maps each patch center’s spatial coordinate into a high-dimensional embedding, a technique **widely used in point cloud representation learning** [3] [7]. \n\nThe positional encoder, together with all other components of the Stochastic Masked Reconstruction module, is trained jointly in an end-to-end manner by minimizing the masked reconstruction loss **(see Supplementary Sec. A1, Lines 780–815)**.\n## Q3-4: Key Hyperparameters\nPlease see **Supplementary Sections A3–B (Lines 830–890)** for details of key hyperparameters and their impact on stability and efficiency. We will move this key discussion into the main text in the final version.\n\n**[Dear Reviewer FjXF]** We sincerely appreciate the reviewer’s attention to detail and high standards. We value your feedback and look forward to addressing any further questions or concerns.\n\n[1] Topnet: Structural point cloud decoder.\n\n[2] Pointnet++: Deep hierarchical feature learning on point sets in a metric space.\n\n[3] Masked Autoencoders for Point Cloud Self-supervised Learning.\n\n[4] Point transformer.\n\n[5] PointCleanNet: Learning to Denoise and Remove Outliers from Dense Point Clouds.\n\n[6] Point cloud denoising review: from classical to deep learning-based approaches.\n\n[7] Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modeling."}]

Author rebuttal:

Meta-review: This paper proposes PointMAC, a novel meta-learning framework for robust test-time adaptation in point cloud completion. The motivation is to address the limitations of existing models that 1) perform static inference and 2) require training-learned inductive biases. Extensive experiments on several benchmarks show its effectiveness.

All reviewers agreed that the paper proposes a solid approach and the evaluations are satisfactory. The AC agrees with the recommendation to accept this paper.
Decision: Accept (poster)