Parameterization-Based Dataset Distillation of 3D Point Clouds through Learnable Shape Morphing
Authors: Dongwook Kim
Jae-Young Yim
Jae-Young Sim
Venue: ICLR 2026 Poster (2026)
Keywords: Dataset Distillation
Distilled Dataset Parameterization
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: Distilled dataset parameterization method for 3D point clouds using learnable shape morphing.

Abstract: Recent attempt in dataset distillation has been made to compress large-scale training datasets into compact synthetic versions, significantly reducing memory usage and training costs. While parameterization-based approaches have shown promising results on image datasets, their application to 3D point clouds remains largely unexplored due to the irregular and unordered nature of 3D data. In this paper, we first introduce a parameterization-based dataset distillation framework for 3D point clouds that enables the use of more diverse synthetic samples than conventional methods under the same memory budget. We construct an initial synthetic dataset containing multiple anchor samples with a coarser resolution than the original sample. We also generate new samples by morphing the shapes of the anchor samples with learnable weights to improve the diversity of the synthetic dataset. Moreover, we devise a uniformity-aware matching loss to ensure the structural consistency when comparing the original and synthetic datasets. Extensive experiments conducted on five standard benchmarks—ModelNet10, ModelNet40, ShapeNet, ScanObjectNN, and OmniObject3D—demonstrate that the proposed method effectively optimizes both the synthetic samples and the weights for shape morphing, outperforming existing dataset distillation methods.

Published as a conference paper at ICLR 2026
PARAMETERIZATION-BAST DATASET DISTILLATION OF 3D POINT CLOUDS THROUGH LEARNABLE SHAPE MORPHING
Dongwook Kim Jac-Young Yim!, and Jae- Yoe-Y Sim" Graduate School of Artificial Intelligence Ulsan National Institute of Science and Technology (UNIST) {donguk071, yimjae0, Sysim)eunist.aa
ABSTRACT
Recent attempt in dataset distillation has been made to compress large-scale train- ing datasets into compact synthetic versions, significantly reducing memory usage and training costs. While parameterizationnbaasee approaches have shown promis- ing results on image datasets, their application to 3D point clouds remains largely unexplored due the irregular and unordered nature of 3D data. In this paper, we first introduce a parameterization- based dataset distillation framework for 3D point clouds that enables the use of more diverse synthetic samples than conven- tional methods under the same memory budget. We construct an initial synthetic dataset containing multiple anchor samples with a coarser resolution than the orig- inal sample. We also generate new samples by morphing the shapes of the anchor samples with learnable weights to improve the diversity of the synthetic dataset. Moreover, we devise uniformity-awan aware matching loss to ensure the structural consistency when comparing the original and synthetic datasets. Extensive ex- periments conducted on five standard benchmarks MMoeeee-MM ModelNer40,, ShapeNet, ScanOhjectNN, and OmniOhjeer3DDddemmoeee demonstrate that the proposed method effectively optimizes both the synthetic samples and the weights for shape morphing, outperforming existing dataset distillation methods.
INTRODUCTION
Significant advances in data-driven techniques for computer vision have been made possible by the availability of large-scale image datasets (Deng al..
2009 Lin al. 2014). However, training
deep neural networks on large-scale datasets typically involves substantial computational costs and
high memory consumption. To alleviate these issues, dataset distillation (Wang et al., 2018 Zhao & Bilen 2023: Zhao et al.) 2021a Cazenavette et al. 2022 Zhang et al.) [024] Yim et al. (0255) has emerged as a promising approach for compressing large-scale datasets into compact yet represen- tative synthetic datasets. Moreover, recent advances in the image domain have introduced a more
storage-efficient paradigm known as distilled dataset parameterization (DDP).
DDP (Kim et al. 2022 Shin et al. 2023 Liu et al. 2022) represents the synthetic dataset in memory- efficient formats synthesize a diverse and informative set of samples under the constrained storage budget. Specifically, some methods (Kim et al.
2022 Shin et al. (2023) attempt to reduce redun- dancy, allowing more synthetic samples to be represented within the same budget. This includes techniques such as removing spatial redundancy through downsampling (Kim et al.) (022) and sup- pressing less informative frequency components (Shin et al. (202)). addition, other methods (Liu et al.) 2022; Deng al. 2022; Shin al., 2025) adopt alternative representations, such as using generative models (Liu et al. (022) to synthesize diverse training samples and neural fields (Shin
al. 2025 to represent datasets with compact implicit function.
Large-scale 3D point cloud datasets have also enabled a wide range of applications 3D vision (Zhao et al. 2021b Yu et al.) 2022 Park al. 2022). However, only lim-
Equal contribution. "Correponnding author.

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Published as a conference paper at ICLR 2026
ited number of studies have developed dataset distillation methods tailored to 3D point clouds (Zhang et al. 2024, Yim et al. 2025). Furthermore, the parameterization techniques for 3D point clouds have not yet been explored, hindering the efficient use of storage space. dataset distillation framework for 31)
In this paper, we first propose a parameterivation-bate point clouds that efficiently represent the synthetic dataset through learnable shape morphing. Figure [ illustrates the concep- tual difference between the proposed method and the previous methods. Whereas the previous methods di- synthetic point cloud dataset to match the original dataset, the proposed method param- eterizes more diverse synthetic sam- ples through learnable shape morph- ing. Specifically, we initialize the synthetic dataset by using multiple anchor samples at coarser resolu- tions, rather than representing sin- gle full-resolution sample. We fur- ther extend the synthetic dataset in- cluding additional samples generated by blending the shapes of aligned anchor samples via learnable weights. This design enables the use of larger number of samples within the same memory budget than the existing approaches. We jointly optimize the initial synthetic dataset and the set learnable weights that minimize the uniformity-awann matching loss between the original and synthetic samples. We conduct extensive experiments to validate the effectiveness of our method, which consistently outperforms existing dataset distillation methods across all benchmarks.
Methods
Storage Budgel
D3
rectly optimize
Shape Morphing
D
Proposed Method
Forward
Rackward
Beeaative Purtition
Figure The concept of the proposed distilled dataset pa- rameterization approach compared to the existing dataset distillation approach.
The key contributions are summarized as follows.
We are the first to propose a parameterization-baene dataset distillation framework for 3D point clouds, which generates diverse synthetic samples under constrained memory bud- get through learnable shape morphing.
We jointly optimize the initial synthetic dataset as well as the learnable weights by mini- mizing uniformity-awen matching loss between the partitioned original sample and the synthetic samples.
We demonstrate that the proposed method achieves superior performance compared with existing dataset distillation techniques through extensive evaluations on standard 3D benchmarks, including ModelNet10 (Wu et al., (015)). ModelNet40 (Wu al.] [2015). ShapeNet (Chang et al. 2015), ScanObjectNN (Uy et al. 2019), and OmniObject3D (Wu et al. 2023).
RELATED WORK
Dataset Distillation. Dataset distillation (Wang et al.j 2018 was first formulated as a meta- learning problem, in which small synthetic dataset optimized to match the behavior of model trained on the original dataset. Subsequent works (Zhao & Bilen 2023) Zhao et al. 2021a
Cazenavette al. 2022) have extended this idea in several directions. Gradient matching (Zhao et al. 2021a) aligns the training gradients of synthetic and original datasets, whereas trajectory matching (Cazenavette et al., 2022) extends this idea by matching the full training dynamics across multiple optimization steps. Distribution matching (Zhao & Bilen 2023) matches feature distribu- tions between the original and synthetic datasets, and achieves computational efficiency by avoiding the need to train network during the distillation process. Recently, methods using generative mod- els such as diffusion (Su et al. 2024 have also been explored for distilling informative samples.
Point Cloud Dataset Distillation. Recently, dataset distillation has been extended to 3D point cloud data, which poses unique challenges due to their unordered and irregular nature. The earliest attempt, PCC (Zhang et al. 2024), applied gradient-matching distillation framework, demonstrat-
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Adeptive Morphing
Forward
KNN Neightor pains
W
Badkward
Quay paint
80 0.6
Cancsicration
Multiplictiinn
Bualys
PointÑet
Festure Extracto
Synthrtic Data Instalizaiion
Anchor Aligmment
Match
Uniformity Measurement
vic!
oec 0.51
PointNi
Ierative Pettioonin
Uniformity-Awar Matching
Figure 2: Overview of the proposed DDP framework for 3D point clouds. Adaptive 3D shape morphing increases the diversity of the synthetic dataset, while uniformity- matching ensures structural consistency between synthetic and original datasets.
ing the feasibility of dataset distillation in the 3D domain. A subsequent method, SADM (Yim) (ct al. (2025), extends feature distribution matching to 3D point clouds by introducing semanti- cally aligned loss that explicitly accounts for their unordered structure. Additionally, it jointly op- timizes the rotation angles, making the distillation process more robust to variations orientation. DD3D (Bo & Wang] (225) introduces rotation- invariint dataset distillation framework for point clouds by combining a rotator with a point-wise generator, enabling iopolution-flxxible synthesis.
Parameterization. Dataset distillation parameterization aims to further reduce storage overhead by representing distilled data in specialized formats rather than raw inputs. An early example, IDC (Kim et al. 2022), reduces storage cost by downsampling synthetic images to eliminate spa- tial redundancy, then upsampling them during training, allowing more samples to be stored under the same memory budget. FreD (Shin et al. (202)) performs dataset distillation in the frequency domain, discarding less important frequency components to reduce redundancy. This frequency- level compression allows more synthetic samples to be used under the same memory budget while preserving global structure. Different strategies, such as HaBa (Liu et al. 2022) and DDiF (Shin) al. 2025), adopt alternative parameterization strategies for efficient storage of synthetic datasets. HaBa employs generative parameterization that distills data in a discrete latent space instead of the pixel space. In contrast, DDiF represents each synthetic instance neural field, which is a continuous function that maps coordinates to data values. Other methods (Deng et al., 2022] Wei al. 2023) aim represent synthetic datasets more efficiently by capturing shared patterns data, rather than treating each sample independently.
METHODOLOOGY
We propose a parameterization-eiieee dataset distillation framework for 3D point clouds. As shown in Figure 2 it comprises two core components: adaptive shape morphing and uniformity- aware matching.
3.1 PROBLEM FORMULATION
Dataset Distillation. Let D. (xx) denote the original dataset and D. = { denote the synthetic dataset, where S < O. The goal of dataset distillation (DD) is generate optimal synthetic dataset D such that a model trained on D exhibits similar behavior to that trained on D.. In practice the optimization problem can be formulated
D = argmin L(D,, Dz),
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where L matching loss that measures the discrepancy between the original and synthetic datasets. Depending on how it is defined, the DD employs different approaches, such as feature distribution matching, gradient matching, training trajectory matching.
Distilled Dataset Parameterization. DDP represents the synthetic dataset in a more compact form using set of latent codes Z = = % and the parameters 9 of decoder go. such that the
synthetic dataset is defined as
D, = {g0(2:)]<=1
(2)
By storing the latent codes and the parameters of decoder rather than storing the synthetic samples directly, DDP enables efficient use of memory allowing a larger number of synthetic samples to be utilized under the same storage budget. Therefore, the objective of DDP is to jointly optimize the latent codes E" and the decoder parameters 0* such that the diserepancy between the original and synthetic datasets is minimized.
13",0"} argmin C (Do: Dz). (2.0)
3.2 SYNTHETIC DATASET PARAMETERIZATION THROUGH LEARNABLE SHAPE MORPHING
To increase the diversity of the synthetic dataset within the constrained memory budget, we pro- pose a distilled dataset parameterization method of 3D point clouds that utilizes additional synthetic samples generated by learnable shape morphing. As illustrated by the adaptive 3D shape morphing module in Figure 2 we randomly sample 3D point cloud objects from the original dataset to initial- ize the synthetic dataset. Instead of selecting an original (full-resolution) sample with N₁ points, take M distinct coarser samples, called anchors, each containing N2 points. The set of these M anchors is referred to as a group. Then we construct an initial synthetic dataset as
5
(4) where a.m E NN denotes the m-th anchor sample in the i-th group. To ensure that the total memory budget of M anchors is smaller than the full-resolution one, set the constraint such that MN2 I Ni.
Dint [
Inspired by 3D shape morphing, generate additional point cloud samples by blending the shapes of the selected anchors to further enhance the diversity of the synthetic dataset. Specifically, we first establish point-wise correspondences across the anchor samples. For each i-th group, align the anchor samples to the first anchor a,.1. We construct the pairwise Euclidean distance matrix be- tween a and each of the remaining M -11a anchor samples, and solve a linear assignment problem to obtain one-to-one correspondence. Then the points in each sample are reordered according to the resulting correspondences. We interpolate I. additional samples from the M re-ordered anchor samples by computing convex combination with learnable weights that adaptively control the con- tribution of the anchors. Specifically, the (-th new sample by Na x3 in the i-th group is obtained by blending the shapes of the re-ordered anchors āi. '8 as
M
by
Σ
w.m ā.m.
(5)
using a learnable weight vector w| [w........ w[,]] such that Em w/_.. = and wi_.. 2 0. Although interpolation is performed over the aligned point cloud samples, perfect point-wise correspondences are not guaranteed due to dataset-specific variations, such as random rotations around the up-axis. Thus we optimize each learnable weight vector w! in an adaptive manner to mitigate such potential mismatches. Note that this strategy introduces no additional memory cost as it reuses the existing anchors.
Finally, we merge the initial synthetic dataset Dimi with the set of the combined samples to construct a complete synthetic dataset g.
D. = ( J (b()/=))
Note that the conventional DD setting uses only a single full-resolution synthetic sample of s;, how- ever the proposed DDP method facilitates the use of M times more diverse shapes of anchor samples as well as L additional combined samples through the learnable convex combination, expanding the diversity of synthetic dataset.
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3.3 DATASET DISTILLATION WITH UNIFORMITY-AWARE MATCHING LOSS
We perform the dataset distillation based on the feature distribution matching by adopting the SADM loss (Yim et al., 2025), that matches semantically aligned feature distributions between the original and synthetic datasets, defined as
LSADM D.) KD..D, + KD..D -2DDD.
where KD..D, denotes the kernel function computed over the sorted feature representations. How- ever, SADM assumes that samples from Do and Ds share the same resolution, which not the case in our setting, where D, includes more coarsely sampled points than the full-resolution samples in D.. Therefore, we partition each sample x; in Du into M non-overlapping low-resolution samples by iteratively applying the farthest point sampling (FPS), where each low-resolution sample contains N2 points. Then we gather the m-th partitioned samples over all the original samples to construct the corresponding subset CD which are compared to the synthetic dataset D..
Note that the resulting subsets of C c c².....c C.M may exhibit spatial non-uniformity of point dis- tributions, that may degrade the reliability of distribution matching. We adaptively control the con- tribution of subsets to the loss computation according to their uniformity. Specifically, shown in the uniformity-
matching module of Figure 2 we estimate the uniformity score v(D) of the dataset D by using the average coefficient of variation (CV) of the local distances computed across the k nearest neighbors.
0 N(D)
Σ ΣE 13+C'
(( N(D)
(8)
where 23 and of denote the mean and standard deviation of the distances from the j-th point in the i-th sample to its & nearest neighbors, respectively, and is small constant for numerical stability. (D)) is the number of points each sample, which is identical across all samples in D. Then the penalty of Cm is estimated by
n exp -- (((x))))))))
where À is scaling parameter.
Finally, the uniformity-aware distribution matching loss for DD is designed as follows:
M
Distill (Do, D,) = Σ T
CSADM(C (D)).
(10)
Then the overall optimization objective to jointly optimize the initial synthetic dataset D init and the set of learnable weights W to minimize the distillation loss CDisaunD... D.):
iiit: W*]
argmin C (Do, (Di, Dz),
(11)
where W = ((w 1
Dinit is defined in (4 and Dj is defined in 6
3.4 STORAGE BUDGET ANALYSIS
In the conventional DD setting, each synthetic sample is stored at full-resolution with N1 points, where the coordinates of each point are represented by three 32-bit floating- numbers, requiring 96.N, bits per sample. Assuming K point clouds per class (PPC) for C' classes, the total memory budget is constrained to 96N, bits. On the other hand, the proposed method maintains this budget by representing each synthetic sample using M coarser anchor samples, each containing N₂ points, resulting in the storage cost of 96M,,, bits. Also, the learnable shape morphing further enhances the diversity incurring only a small overhead of (M- C' bits to store the learnable weights. Hence the total memory constraint for the proposed method is
96MNNKCC + - S KC.
(12)
Note that the weight storage term is proportional to M-1 rather than M, because one weight is determined by the condition ΣM w
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Table 1: Classification performance of the proposed method compared with coreset selection and dataset distillation methods. All methods were evaluated using PointNet under the same memory budget. 'Whole' indicates the result obtained by training on the entire original dataset. The best performance in each row is highlighted in bold. OOM denotes out of memory during distillation.
Coreset Selection
Datasct Distillation PCC
Datasel
PPC
Whole
Random
Herding
K-Cente
DM
DC
MTT
SADM
Ours
28.124,2 34.026.55 34.0#16.5
25 846.9
32.818.5
27.8=5.8 33,028.0 0 35.948.2 87.7
ModelNer10
74.5=1.8 76,9+1122 75.9
77.4
74.5+2.6 85.0++0.7
73.6=1.7 70.7+1.6
83.5+0.7
89,8+0.5
92.18
84.7=0077 10
86.1+-0.7
82.2+1.5
84.6+0.6
85.3+1.2
86.3+1.1
87,4+111
92.2+0..5
(34.0=2.1
54.1::2.1
31.11.1
50.342.0
33.41:.11
55.3:114
54.811.3
73.2.11.1
ModeINet40
59.9=1.6
69.11:10
2.1122.761.5:222
66.0+1.1
59.5=0.6
66.2:1:.6
71.340.7
80.310.5
88.78
73.3==0. 10
77.620.66
64.31:.3
74.920.8
74.3+0.9
73.4=0.5
77.910.9
79.610.6
82.5:10.6
33.5:2.55
49.11:2.4
49.112..42633333.6
48.7.116
32.4-2.6
50.933.5
51.142.3 6..5.1111
ShapeNet
53.4=1.4
58.811.0
50.611.6
52,5#116
56.61111
53.5-2.0
58.91177 62.24:1. 65.9-1.6
82.49
62.4=0.9 10
66.3+0.4
46.920.7
63.1=0088
63.7+0.8
62.3=1.1
65.4+0.8
68.0+0.55
68.9:10.6
13.5=1.8
15.1+1.7
13.771.
15.2+2.0
14.3+2.5
16.0+2.4
17.6+1.5 32.6+ 1.6
ScanObjectNN
19.7=0.7
26.9++14
188+1.1
26.4::2.4
24.6+22.2
20.1+11.3
25.5
3..64
1.1
63.43
34.111.6 38.311.66
23.51099
37.411..2
38.511.6
37.122.0
34.6.114
43.7.1..0
49.8.10.7
24.0==0.6
30.5+
10.5+1.011.110000
31.1+0.5
25.8+1.3
35.8+00.6
33,24:0.
41.9
OmniObjecttD
40,911.2
42.31114
35.911.5
10.3.1.
44.3110
44.821.1
46.2100.7
51.610.4
1..4.1..5
74.98
10 59.5=2.1 59.31177 58.341.6 60.011.6
56.510.8
0OM
57.010.6 65.241.7 68.4
4 EXPERIMENTAL RESULTS
4.1 EXPERIMENTAL SETUPS
Datasets. We evaluate the performance of the proposed method on five standard point cloud clas- sification datasets: ModelNet10 (Wu al. 2015). ModelNet40 (Wu et al. 2015), ShapeNet (Chang et al., 2013). ScanObjectNN (Uy et al.) 2019). and OmniObject3D (Wu et al. 2023). ModelNetI0 and ModelNet40 consist of 10 and 40 categories of clean 3D CAD models, respectively. ShapeNet includes 55 categories of large-scale 3D CAD models with finer-grained class distinctions. ScanOb- jectNN comprises 15 categories of real-world objects captured from RGB-D scans, and we use the PB T50 RS variant, which is the most challenging setting in this dataset. To further validate the scal- ability of our method, we conducted additional experiments on OmniObject3D (Wu et al.) 2023), dataset that contains a significantly larger number of object categories compared to conventional benchmarks. Since OmniObject3DD does not provide an official train-test split, we randomly sam- pled 80% of the data for distillation and used the remaining 20% for testing, while ensuring that each class included at least four test samples. This resulted in total of 156 categories used in our evaluation. We additionally evaluate part segmentation performance using ShapeNetPart (Yi et al., 2016).
Implementation Details. Both D and W were updated via stochastic gradient descent with a learning rate of 10 for 2,000 iterations. Training was performed for 500 epochs with batch size of 8, using step decay schedule with step size of 250 and decay rate of 0.1. All reported results were averaged over 10 independent runs by using a single NVIDIA RTX 3090 GPU. To ensure a strictly fair comparison, all baselines were fully re-implemented and evaluated under an identical and augmentation-fre setting.
4.2 PERFORMANCE COMPARISON
We compared the performance of the proposed method with representative dataset distillation meth- ods, including DM (Zhao & Bilen 2023). DC (Zhao et al., 202 and MTT (Cazenavette et al. 2022). which were originally developed for image domains and adapted to 3D point clouds. We also compared SADM (Yim et al. 2025), and PCC (Zhang et al., 2024). recent methods tailored to 3D point cloud dataset distillation. In addition, compared coreset selection methods commonly used to reduce dataset size, including random selection (Rebuffi et al. 2017). Herding (Castro et al.
2018) and K-Center (Sener & Savarese 2018).

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Published as a conference paper at ICLR 2026
Table 2: Comparison of cross-architecture generalization performance at PPC evaluated on PointNet++ et al., 2017b) (PN++), PointConv( Wu et al. 2019) (PC), Point Transformer (Zhao
al., 2021b) (PT), and PointMamba (Liang et 2024) (PM). The best performance each row highlighted in bold.
Dataset
Method
Random
DM
DC
MTT
PCC
SADM
Ours
PN++
22.416.9
12.1+2
15.3:6.5
20.416.9
20.716..
25.977.1
8,,4:18.6
ModelNet10
PC
17.7+10.1
10.8+3.6
14.2+4.7
21.3+7.6
16.0+8.7
20.5+13.1
51.6+9.8
PT
44.116.3
22.419.1
26.716.9
39.317.4
45.947..5
49.0.7.77
$7.0-1109
PM
29.218.1
14.012.1
20.71224
31.62499
28.917.3
28.413.7
69.41
PN++
36.8+22.2
1.54
8.643.0
37.241.8
13.54:3.
40.0-12.88
47.7+1..0
ModelNet40
PC
23.143.8
3.9419
11.1334
24.244.6
14.743..
29.1+3.00
33.2:1..9
PT
28.9+1.2
6.2+4.4
14.8+3.1
19.0+1.2
40.2+2.2
44.5+11.
39.0+6.6
PM
34.1+1.3
12.3422.2
24.3+1..6
33.4414
38.34:2.44
35.9-12.00
59.6
PN++
25.3+2.5
2.0+1.4
7.2+2.0
24.6+2.5
21.8+3.2
35.1+11.3
44.7+22.0
ShapeNet
PC
19.0+3.1
3.8+1.0
10.3+34
19.4339
16.544.4
20.3+5.00
24.3+16.0
PT
26.3
7.412.9
19.1
16.321.7
38.3111.6
36.312.6
40.2.1.66
PM
17.41.33
5.6+1.3
13.1
17.3+14
30.7+1.1
25.64t3.1
49.0+0.77
PN+
18.0+144
14,5+-4.7
15.8+3.7
18.8+2.6
13.0+3.6
9.342.1
14.3++2.5
ScanObjectNN
PC
12.41222
10.012.11
9.9-1.8
13.1-1.8
12.04:2.5
10.9134
14.6:12.1
PT 12.5+0.9
10.2
12.3+18
12.2-16
16.2
158+22.
1.
PM
18.9+1.6
14,8+1.7
17.7+1.7
18.9+1.4
13.6+2.4
13.2+0.9
19.7+1.0
Table 3: Comparison of part segmentation performance on the ShapeNet dataset with PPC set to 1. K-Center results are omitted from the comparison because they are identical to Herding when the PPC is 1.
Class
Air.
Bag
Cap
Chair
Ear.
Guitar
Knife
Lamp
Laptop
Motor.
Mug
Pistol
Rocket
Skate.
Table
Avg.
Whole 82.0 65.5 65.3
75.0 88.6
68.2
90.2
83.0
77.7
94.9
63.0
92.8
79.0
53.9
70.5
81.3
76.9
Random 28.1 22.8 53.0
21.6
38.8
24.0
45.0
23.1
25.1
57.1
20.3
46.0
36.4
29.7
13.7
24.1
31.8
Herding
31.7 36.1
47.1
22.1
46.4
34,9
50.4
54.2
20.9
65.2
16.3
54.9
35.0
30.8
31.5
42.3
38.7
SADM
29.9 30.6
52.3
21.6
49.4
23.1
51.3
66.0
29.3
68.8
15.0
51.7
39.8
35.4
38.5
46.5
40.6
Ours 51.5 51.8
59.0
36.5
70.8
42.6
80.1
76.6
31.8
81.0
26.4
84.3
61.8
40.5
49.2
59.1
56.4
Evaluation on PointNet. Table [ compares the classification performance of different methods using PointNet (Qi et al. 2017a) under same memory budget. Specifically, we set N₂ 252, M 4, and L 16 for ModelNet10 (Wu et al. 2015). and N₂ 255, M 4, and L 4 for the other datasets, respectively, to satisfy the inequality in 12) with N1 = 1024 for the original datasets. The proposed method consistently outperforms all compared methods across all bench- mark datasets and PPC settings. In particular, the most substantial performance gain is observed when PPC is set to 1. For instance, MedelNet10 at PPC 1, method achieves accuracy 8 87.77, which is a remarkable improvement over 35.9%, the state-of-the-art (SOTA) performance of SADM. Similarly, on ModelNet40, our method reaches 73.2%, outperforming all the baselines by large margin. Moreover, our method improves the state-of-the-as performance from 17. 7.6% to 32.6% demonstrating its reliability on the challenging 1. On OmniObject3D, which includes a substantially larger and more fine-grained set of categories, our method achieves 41.9%, indicating that the proposed framework generalizes reliably even as the number of categories increases significantly. These results demonstrate that the proposed parame- terization technique provides more promising approach for dataset distillation of 3D point clouds under constrained memory budgets than the existing methods.
dataset ScanObjectNN at PPC
Cross-Architecture Generalization. To evaluate the cross architecture generalization perfor- mance, we compared the performance of the proposed method with the existing methods using four different architectures including PointNet++ (Qi et al., 2017b). PointConv (Wu et al.) 2019). Point Transformer (Zhao et al. 2022b), and PointMamba (Liang et al. 2024), after distillation is performed using PointNet (Qi et al. 2017a). As summarized in Table 2 the proposed method con- sistently achieves the best performance across the most datasets and architectures, demonstrating strong generalization ability. On MedelNet10, the proposed method achieves 55.4% with Point- Net++, significantly outperforming 25.9%, the performance of SADM. Likewise, on PointCony, Point Transformer and PointMamba, the proposed method provides 51.6%, 57.0%, and 69.4%,
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Menioor
Desk
Dour
Table
Pillow
Foilet
Sofa
(a)
(b)
Figure 3: Visualization of the resulting synthetic datasets: the first four rows show initial samples before optimization, consisting anchor samples (orange) and combined samples (blue), while the subsequent four rows illustrate samples obtained after applying the proposed method. (a) Model- Net10 and (b) ScanObjectNN.
respectively, maintaining substantial performance gaps over all the baselines. Similar trends of im- provement are observed on ModelNet40 and ShapeNet. While our method generally improves the performance across all architectures, the accuracy on ScanObjectNN with PointNet++ slightly lower. This is because our method designs the dataset distillation loss based on SADM loss (Yim) et al. 2025), which shows relatively low performance on ScanObjectNN with PointNet++. These results demonstrate that the synthetic samples, generated by combining diverse low-resolution chors in the proposed method, are not overfitted to specific architectures and instead capture useful geometric characteristics that generalize well across different backbone networks.
Part Segmentation Evaluation. To validate the generalization of the proposed method beyond classification, we additionally performed a part segmentation experiment on the ShapeNetPart (Yi al. 2016) using PointNet segmentation model. As shown in Table 3 the proposed approach consistently achieves higher mloU across all object categories. For example, the mloU on guitar increases from 51.3% to 80.1%. and mug improves from 54.9% 84.3%. The average mloU reaches 56. 56.4%, clearly surpassing the SADM average of 40.6%. These results show that the distilled dataset successfully captures the fine-gon mme-grained geometric structure required for accurate level prediction.
Table 4: Comparison of dataset distillation performance across four variants of the ScanObjectNN benchmark.
Variant
Random
Herding
K-Center
DM
SADM
Ours
PB_ T25
12.6
14.3
14.3
12.9
19.4
35.2
PB $2__22_RR PB T50 R
10.2
15.0
15.0
12.8
18.8
36.0
9.6
14.4
14.4
11.5
16.7
34.2
PB T50 RS
13.5
15.1
15.1
13.7
17.6
32.6
Evaluation on ScanObjectNN Benchmark Variants. To clearly assess the robustness of our proposed method under various challenging real-world scenarios, we conduct our experiments on the four variants of ScanObjectNN: PB.T25 PB. PB.T25_R, PB.T50.F and PB_T50_RS. As shown in Table our method consistently outperforms the baselines across all variants, demonstrating robust performance regardless of the increasing difficulty of the datasets.
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Table 5: Performance of the proposed adaptive shape morphing method with learnable weights compared with the static method of using fixed weights, evaluated on ScanObjectNN PPC = 1.
Table 6: Effect of the proposed uniformity-
aware matching loss using the penalty coeffi- cient 1).
Datasets
ModelNet10
ScanObjectNN
# of L
2
4
8
12
16
20
24
PPC
3
10
3
10
Static
19.8
30.1
30.8
32.5
31.7
31.4
30.8
w/o
88.4
88.3
90.1
30.7
40.6
47.6
Adaptive
21.0
32.6
32,4
34.8
35.1
35.6
33.0
w/
87.7
89.8
92.2
32.6
41.3
49.8
4.3
QUALITATIVE RESULTS
Figure illustrates how the resulting synthetic datasets evolve through optimization. For Mod- elNet10, the initial combined samples are generated by averaging the anchor samples with fixed weights and often appear as noisy point clouds lacking meaningful structure. In contrast, after applying the proposed method, the learnable weight vectors adaptively refine the combinations, producing structurally consistent 3D shapes. A similar trend is observed in the real-world dataset ScanObjectNN, where the initial combined samples, especially for classes such as door, sofa, and pillow, suffer from even more severe misalignment. Nevertheless, after optimization, the resulting samples exhibit significantly improved structural consistency. In both datasets, some combined sam- ples appear slight variations of anchor shapes, while others occasionally produce structures not present in the anchors, demonstrating that the proposed method effectively balances structural preservation and shape diversity.
4.4 ABLATION STUDY
Hyperparameter Selection. We analyze the behavior of two key hyperparameters: the num- ber of points per anchor sample (N2) and the number combined samples (L). Figure (a) shows the accuracy for different values of N2, while maintaining fixed total memory budget by adjusting the number of anchors M such that MN2 .N. where N1 is set to 1024. When evaluated with PointNet (Qi al. 2017a) and PoimtNet++ (Qi et al.] (017b)) PointNet per- forms better with smaller N₂ since it mainly focuses on global features and is less sensitive the local structural variation of coarse an- chors. In contrast, PointNet++ shows sharp performance drop at N2 = 128, indicating that it struggles to extract meaningful information when the resolution too low. Based on this trade-off, set N2 256 within the budget for experiments in [
40
Peint
35
PointNet:
Accursey (96)
128
25
100
Accurary
Timee
Resolution
(a)
(b)
Figure 4: Analysis of performance on ScanOb- jectNN at PPC = with respect changes in hy- perparameters. (a) Accuracy versus N₂ (number of points per anchor) under the same total mem- ory budget. (b) Trade-off between classification accuracy and training time when varying L (num- ber of combined samples).
Figure (b) also investigates the classification accuracy and training time in terms of the variation of the number of combined samples (L). The training time refers to the average time required to train the network during evaluation, averaged over 10 runs. In general, L increases, the accuracy is improved by enabling more expressive combinations, but the computational cost is also increased. The results show that, beyond L 4, the accuracy almost saturates while the training time continues to grow. Based on this trade-off, we use L 4 to strike a balance between the accuracy and efficiency, except using
for ModelNet10 (Wu et al. 2015) which has only 10 classes.
Effectiveness of Learnable Shape Morphing. To illustrate the contribution of the proposed learn- able shape morphing strategy, we conducted two experiments. First, we compared the adaptive weighting scheme against static baseline, where the weights are randomly initialized and remain fixed throughout the optimization. BableBrreport the classification accuracy with varying the num- ber of combined samples (I.) from 24. The results show that the adaptive setting consistently

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Table 7: Distillation results with various backbone architectures on ModelNet10 dataset with PPC set 1.
LSM
LSM
Datasets
SADM
Ours
82
Train/Tsst
PT
PC
PN+
PN
PT
PC
PN++
PN
3
PPC
PT
35.6 17.1
12.4
24.7
34.2
24.3
12.7
59.6
(b)
PC
21.6
11.1
15.5
16.8
18.0
11.7
11.4
24.7
Figure 5: Ablation study evaluating the ef-
PN++
44.2 21.8
11.5
33.8
35.4
23.6
23.4
78.4
fectiveness of the proposed learnable shape
DG
50.7
20.9
17.2
38.5
68.6
44.5
23.0
77.2
morphing (LSM). (a) ModelNet10 and (b) ScanObjectNN.
PN
49.0
20.5
25.9
35.9
57.0
51.6
55.4
87.7
outperforms the static setting. This indicates that learning the weights allows the model to control the relative contribution of each anchor sample more effectively, compensating for possible mis- alignments introduced by initial registration. Second, to evaluate the overall effect of the shape morphing strategy, we compared the framework with and without shape morphing, as shown in Fig- ure We see that applying the shape morphing improves the performance across all PPC settings. This validates that the shape morphing strategy enhances the diversity of synthetic dataset while generating semantically meaningful samples.
Effectiveness of Uniformity-Awar Matching Loss. To validate the effectiveness of the proposed uniformity-awe aware aware matching loss, we compared the models trained with and without using the penalty coefficient 7) in (9). As shown in Table 6 the uniformity-awe aware matching loss with 7) improves the performance across different PPC settings on ScanObjectNN and for higher PPC settings on Mod- elNet10. While a slight performance drop is observed at PPC on ModelNet10, the overall trend shows that applying the uniformity-aweer aware matching loss leads to more stable and improved perfor- mance. In contrast, without using 7), each partitioned subset C contributes equally to the overall loss regardless of how closely its spatial uniformity aligns with that of the original dataset, which can result in less reliable supervision. The observed performance gains suggest that the proposed uniformity-awee aware
aware matching loss effectively mitigates the limitation of the subset partitioning.
Results with Various Backbone Architectures. We also distilled the synthetic datasets using Point Transformer (Zhao al., 2021b), PointConv (Wu et al. 2019), PointNet++ (Qi et al.) 2017b). PointNet (Qi et al.) 2017a). and DGCNN (Wang et al. 2019), respectively, and evaluated them on ModelNetIO. The results are summarized in Table 7 When a more complex backbone used, it becomes inherently harder to align the feature distributions between the original and synthetic datasets. As more layers and operations such as local aggregation or attention are added, feature maps become more unstable, making it difficult to maintain consistent alignment between the two distributions. In contrast, simpler backbone produces more stable feature maps, so aligning the two distributions is easier. Therefore, we use PointNet the backbone for all experiments.
CONCLUSION
In this paper, we first proposed parameterizaaion-basse dataset distillation framework for 3D point clouds, capable of synthesizing informative and diverse samples under constrained memory bud- get. To this end, we devised learnable shape morphing strategy that diversifies synthetic samples by blending multiple anchor samples with coarser resolution in the initial synthetic set. More- over, we designed uniformity-aware matching loss that adaptively emphasizes the contribution of partitioned subsets of point clouds, improving the reliability of distribution matching between the original and synthetic datasets. Experimental results
five widely used benchmarks includ- ing ModelNet10 (Wu et al. 2013)). ModelNet40 (Wu et al. 2015). ShapeNet (Chang et al. 2015), ScanObjectNN (Uy et al. 2019), and OmniObject3D (Wu et al. 2023) showed that the proposed method achieves substantial performance improvements over existing dataset distillation methods across various PPC settings.
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ACKNOWLEDGEMENTS
This work was supported in part by the National Research Foundation of Korea (NRF) grant funded by the [Ministry of Science and ICT (MSIT) under Grant DS-2024-003925533 in part by the Insti- tute of Information and Communications Technology Planning and Evaluation (IITP) grants funded by the Korean Government (MSIT), including the Leading Generative Artificial Intelligence (AI) Human Resources Development Program under Grant the Artificial Intelligence Graduate School Program [Ulsan National Institute Science and Tech- nology (UNIST)) under Grant 202000020000000000 and in part by the Al Star Fellowship Program (UNIST) under Grant RS-2025-25442824.
in part by
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A APPENDIX
Large language models (LLMs) were used solely for language refinement, and all research content was generated entirely by the authors.
A ALGORITHM
Algorithm Noutlines our parameterization-bant dataset distillation method. The process begins by initializing multiple coarse anchors per class and aligning them via solving the assignment problem. During each distillation step, synthetic samples are generated through shape morphing, and the anchors and blending weights optimized using uniformity-awarc distillation loss.
Algorithm 1 Parameterizztionn-aae Based Dataset Distillation via Learnable Shape Morphing
Require: Original dataset Do, number of anchors M, number of combined samples L, size of synthetic dataset $
Ensure: Distilled anchors {ār.m) and weights {w!)
1: Initialize Dintt {{ IM 1 and W {[w ME
18
2: Align the anchor samples within each group of Dinit
3: for each distillation step do
4: Construct synthetic dataset D. = (ai. Ji U 1Em= wf,m âim 12 H
5: Sample mini-batches B, Do, B. D.
6:
Partition B, into subsets C ..M CM
7:
Compute penalty coefficients 17"h exp ( -((((B,) =(C()))2)
8:
Compute distillation loss DDisilll = -m= 11 LSADM(C (Cth, ,B)
9: Update Dinit W w.r.t. LDistlll
10: end for
B EXPERIMENTAL DETAILS
B. IMPLEMENTATION DETAILS
While the original LSADM consists of both C., which matches the entire feature map, and Cg. which matches only the most prominent feature, use only La in implementation. The configuration in Table X ()) outlines the hyperparameters used for training the evaluation network. The network was optimized using stochastic gradient descent (SGD) with learning rate of 0.01, momentum of 0.9, and a weight decay of 0.0005. The batch size was set to 8, and training was conducted for 500 epochs. To adjust the learning rate during training, StepLR scheduler was employed, with step size of 250 and decay factor of 0.1.
Table 8: (a) Hyperparameters used train the evaluation network, and (b) hyperparameter settings of the baselines.
Param
Value
DC
DM
MTT
PCC
SADM
Optimizer
SGD
Backbone
PointNet PointNet PointNet PointNet PointNet
Momentum
0.9
Initialization
Random Random
Random
Herding
Random
Weight Decay
5e-4
Batch Size D.
8
8
8
Batch Size
8
Batch Size D.
8
8
8
Learning Rate
0.01
Learning Rate
0.0001
0.0001
0.0001
10
Epochs
500
Distillation Steps
2000
2000
2000
2000
2000
(a)
(b)
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B.2 BASELINES
Since the official code for most baselines is either tailored for image-based tasks or unavailable, we re-implemented all baseline methods in our framework for a fair comparison. Table(8(b) summarizes the hyperparameter settings used for each baseline, including DC (Zhao et al. (02 DM (Zhao & Bilen 2023), MTT (Cazenavette et al.) 2022), PCC (Zhang et al. 2024). and SADM (Yim al. 2025]. All methods were implemented with PointNet as the backbone network and were traindd under consistent configuration where both the original and synthetic datasets used batch size of 8, and each method was optimized for 2000 steps. For initialization, random initialization was used for most methods, except PCC, which employed herding initialization. While most methods used relatively low learning rates, DM and SADM adopted larger values of I and 10, respectively.
C ADDITIONAL EXPERIMENTS
C.I PLUG-AND- APPLICATION OF THE PROPOSED METHOD
To evaluate the independence of our method from specific distillation strategies, we apply a plug-and aad---d-ppy manner top of DM. As shown in Table | our method substantially improves the performance of DM, particularly when PPC is low. For example, on ModelNet10 with PPC = 1, the accuracy rises from 25.8% to 79.8%, indicating that method can effectively enhance even weaker baseline. The last row (+Ours") presents the result of combining our method with SADM, which is identical to Table I in the main paper. This setting also shows the largest improvements at PPC 1. The consistent trend across both baselines suggests that our method is not tailored to any specific distillation framework but can serve as a general plug-and-play module that improves performance, especially under constrained memory budgets.
Performance comparison with and without our plug-and and-play method applied to DM and SADM. * indicates the result of SADM combined with our method, which is identical to the perfor- mance already reported in the main paper.
Table 9:
Datasct PPC
ModelNet10
ModelNet40
ShapeNet
ScanObjectNN
10
DC
32.818.5 745526
84.610.
50.34:2.0
66.0111.1
74.310.9
48.7=116
16.11111
63.720.
15,2120
24.612222
38.511.6
MIT
27885588 73.6#1.7
85.3+12
33,4+2.1
59.540.6
73.410.5
32.4=26
53.5-20
62.3#111
14.3+2.5
20.1+1..
37.1+2.00
PCC
33.0+8.0
70.7#1.6
8633111
55.3+144
66.2+1.6
77.9+0.9
50.9=3.5
58.9-177
65.4=088
16.0+24
25.5+2.2
34,6+1.4
DM
25.8++699
77.41:22
85.0=0077
61.542.11
74.940.8
2633-36
52.5116
63.1
13.74
26,44224
37.4+1122
Ouns
79,811.6 82.411.4
8641144
55.610.11
67.3..066
76.3100.6
52.311.4
599113
63510.
18.1108
29.3100.1
37.311.0
+54.0
+5.0
+1.4
+24.5
+5.8
+1.4
+26.0
+7.4
+0.4
+4.4
+2.9
-0.1
SADM
35.9118.2
83.5:00.7
87.421.1
54.8113
713310.7
79.610.6
51.1=23
62.22116
68.020.5
17.62:55
32.61:16
43.742.0
+ Ours
87.7+0.7
89.8=0.5
92.2=00.5
73,2+111
80.3+-0.5
82.5+006
60.5=1.1
65.9-0.6
68.9=0.6
32,6++166
41,3+111
49.8+0.7
+51.8
+6.3
+4.8
+18.4
+9.0
+2.9
+9.4
43.7
40.9
+15.0
+8.7
+6.1
C.2 ABLATION ON DATASET COMPOSITION AND OPTIMIZATION STRATEGY
To analyze the effect of dataset composition and optimization strategy, we compare four synthetic dataset settings, each differing in how D, is constructed and which parameters are optimized during distillation.
D3 consists only of D comb, and only the combination weights W are optimized.
W argmin LDisin (Do, D.) where D. D comb-
(13)
W
DD includes both the fixed anchors D in and the generated samples Deomhh while only W is optimized.
W argmin CDistlll (Do, Dz) where D, - DDinn init U Dcomb comb
(14)
DD consists only of D comb. but both the anchors init and the weights W are optimized.
{Di mnt W') argmin Lpssunl (Do. D.) where D, D comb
(15)
(Dm..W)
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DD includes both D and D comb. and both are optimized during distillation.
(Dinu init W argmin Distill (Do: Ds) where D; = Dnit U D Domb
(16)
(Dm.W)
To ensure a fair comparison, we adjust the value of L to equalize the total dataset size across all settings. As shown in Table T0 the results show that the best performance is achieved when both Dint and W are jointly optimized and both components are included in the final synthetic dataset.
Table 10: Ablation study on synthetic dataset composition and optimization strategy. Each row corresponds to a different formulation described in I3 (16).
Dataset
ModelNer10
ModelNet40
ShapeNet
ScanObjectNN
13
80.4++0.9
54.5++0.4
46.3++2.1
18.5+
78.8+
55.5++0.9
51.3++0.7
18.1+1.3
87.3+0.9
71.9++0.9
59.3++0.8
28.4+112
I6
87.7.10.7
73.2:11.1
60.5:1.11
32.6 1.6
C.3 ABLATION ON DATA AUGMENTATION STRATEGY
Table 11: Ablation study comparing the baseline PointMixup and our method on ModelNet10 and ScanObjectNN under PPC=
Methods
ModelNet10
ScanObjectNN
PointMixup
82.3
21.9
Ours
87.7
32.6
To further investigate the effectiveness of our proposed method, we additionally conduct a compar- ison against PointMixup (Chen al. 2020), a representative data augmentation technique for point clouds. Unlike PointMixup, which interpolates point clouds using fixed coefficient without con- sidering the distillation objective, our method synthesizes both the anchors and blending coefficients jointly with the distillation process. As shown in Table [1] our learnable shape morphing framework consistently outperforms PointMixup across both datasets.
C.4 EFFECTIVENESS POINT CLOUD DATA AUGMENTATION
We evaluated the effect of standard point cloud augmentations ScanObjectNN PPC 1, 3, and 10. The augmentation strategies include point jittering with Gaussian noise of standard deviation 0.001, random scaling within the range 0.8 1.2, point dropping with ratio of 0.875, and PointMixup Chen et al. (2020) with a = 0.2. Methods that are not designed for point cloud (DM, DC. MTT) exhibited inconsistent behavior under these augmentations, with accuracy fluctuating depending on the PPC setting, suggesting that they do not reliably preserve structural information in point clouds. contrast, point cloud dataset distillation methods (PCC, SADM, Ours) consistently benefited from the use of these augmentations. Furthermore, even when all methods were trained under the same augmented pipeline, our method achieved the highest accuracy at every PPC setting. These results demonstrate the robustness of proposed method to standard point cloud augmentations.
D ADDITIONAL QUALITATIVE RESULTS
Figures d and show additional qualitative results obtained with I. on the ModelNet40 and ShapeNet datasets, respectively. These results demonstrate that our method generates slight vari- ations from the original anchors. In all visualizations, blue point clouds represent the combined samples, while orange point clouds denote the anchors. Figures [igures present the distilled dataset under storage budget of PPC = on the ModelNet10 and ScanObjectNN datasets, respectively, where the increased capacity allows the synthesis of more diverse shapes. Figures I0 illustrate the results on the ModelNet10 datasets when L = 16, showing that our method can generate a wide range of shapes from limited set of anchors.
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Published as a conference paper at ICLR 2026
Table 12: Classification accuracy ScanObjectNN under standard augmentations. "Aug." denotes
whether augmentations were applied.
PPC
Aug.
Random
Herding
K-Center
DM
DC
MTT
PCC
SADM
Ours
X
13.5
15.1
15.1
13.7
15.2
14.3
16.0
17.6
32.6
1
/
15.5
18.1
17.4
15.3
15.3
15.9
19.4
22.7
37.6
x
19.7
26.9
18.8
26.4
24.6
20.1
25.5
32.6
41.3
3
19.2
29.0
21.4
21.1
22.6
19.6
31.7
35.1
44.2
X
34.1
38.3
23.5
37.4
38.5
37.1
34.6
43.7
49.8
10
/
33.7
40.0
25.8
35.7
38.5
33.0
40.8
45.1
51.5
Figure 6: Visualization of distilled samples from Mode/Net40 under a storage budget of PPC 1.
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Published as a conference paper at ICLR 2026
Figure 7: Visualization of distilled samples from ShapeNet under a storage budget of PPC = 1.
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Published as a conference paper at ICLR 2026
Figure 8: Visualization of distilled samples from ModelNet10 under a storage budget of PPC = 3.
Figure 9: Visualization of distilled samples from SeanObjectNN under storage budget of PPC 3.
Figure 10: Visualization of synthetic samples from ModelNet10 with L 16 under a storage budget of PPC 1.
19


Reviews:
[{"review_id": "MKQExgK5vB", "reviewer": "Reviewer_5FmN", "summary": "This paper introduces a novel parameterization-based dataset distillation framework for 3D point clouds. The approach leverages learnable shape morphing to generate diverse synthetic samples within a constrained memory budget. The core idea involves using multiple coarser anchor samples and blending their shapes with learnable weights.  While the paper presents promising results on standard benchmarks, several critical issues need to be addressed before it can be considered for acceptance.", "questions": "1. The authors need to provide a thorough justification for the choice of experimental parameters and explicitly demonstrate how the memory constraint MN2 ≤ N1 is satisfied (or revise the constraint to be mathematically correct). If the constraint is not satisfied, the experiments need to be re-run with valid parameters. Provide a clear explanation as to why a seemingly small violation of the constraint is acceptable and doesn't invalidate the comparisons.\n2. Expand the experimental evaluation to include more diverse and state-of-the-art backbone architectures. Consider including more recent Transformer-based networks or graph neural networks to assess the generalization capabilities of the distilled datasets.\n3. Elaborate on the reasons behind the lower accuracy observed on ScanObjectNN with PointNet++ (as mentioned in Section 4.3). Investigate whether the choice of the SADM loss is indeed a limiting factor and explore alternative loss functions that might be more suitable for this dataset and architecture combination.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 3, "strengths": "1. The application of parameterization techniques to dataset distillation for 3D point clouds is a relatively unexplored area, and this work introduces a potentially efficient approach. The idea of using learnable shape morphing to generate diverse synthetic samples is innovative.\n2. The paper demonstrates promising performance on several benchmarks (ModelNet10, ModelNet40, ShapeNet, ScanObjectNN), outperforming existing dataset distillation methods in some cases.\n3. The paper is generally well-written and explains the proposed method clearly.", "weaknesses": "1. Critical Constraint Violation: The paper states the memory constraint MN2 ≤ N1, where M is the number of distinct coarser samples (anchors), N2 is the number of points in each anchor, and N1 is the number of points in the original (full-resolution) sample. The experimental setup violates this constraint. For instance, the paper mentions N2 = 252, M = 4, and N1 = 1024. In this case, MN2 = 4 * 252 = 1008, which is LESS than the constraint, should be MN2 <= N1. This casts significant doubt on the validity of the experiments and the claims made about memory efficiency. \n2. Limited Backbone Architectures: The experimental evaluation primarily relies on PointNet as the backbone network. While PointNet is a common choice for point cloud processing, it's essential to demonstrate the generalization ability of the distilled datasets with various backbone architectures. Evaluating with PointNet++ (as reported in Table 2), PointConv, and Point Transformer is a good starting point, but further investigation may be warranted. The paper needs to provide a more comprehensive evaluation across different backbone architectures to demonstrate the robustness and general applicability of the proposed dataset distillation method. Section 4.2 states that for the cross-architecture analysis, 3 different architectures are used, and claims the method \"demonstrates strong generalization ability\" across most datasets and architectures, but Section 4.5 states that the datasets were distilled with a PointNet backbone. Therefore, it's only demonstrated that the distilled dataset works well with other architectures, but not that the method produces good datasets when other architectures are used to distill the data.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "KjLc6A0W3s", "reviewer": "Reviewer_tN6U", "summary": "This paper presents the first parameterization-based dataset distillation framework for 3D point clouds, enabling the creation of compact synthetic datasets under strict memory constraints. Unlike prior 2D methods, it introduces learnable shape morphing to generate diverse samples by blending coarser anchor shapes with learnable weights, maximizing diversity within the same memory budget. A uniformity-aware matching loss further enforces structural consistency by balancing contributions from different data partitions. Joint optimization of anchors and morphing weights minimizes this loss, yielding superior results on ModelNet10/40, ShapeNet, and ScanObjectNN, especially under tight memory conditions.", "questions": "N/A", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "+ The paper introduces a novel parameterization-based dataset distillation framework for 3D point clouds\n+ Outperforms existing dataset distillation techniques across all four standard benchmarks (ModelNet10, ModelNet40, ShapeNet, and ScanObjectNN) at various Point Clouds Per Class (PPC) settings.\n+ On ModelNet10 at PPC=1, it achieves an accuracy of 87.7% (previous SOTA 35.9%). \n+ On the challenging real-world dataset, ScanObjectNN improves the previous SOTA from 17.6% to 32.6% at PPC=1.", "weaknesses": "- Comparison of cross-architecture generalization performance at PPC = 1 (Table 2) is made on relatively older models (PointNet++, PointConv, and PointTransformer) and not on recent models (e.g., PointMamba). Hence, it is unclear if the proposed approach works on recent methods.\n- Results limited to a few selected datasets: the paper does not mention if the results of the real-world dataset ScanObjectNN [1] presented in the paper are for the hardest variant PB_T50_RS. \n- Moreover, these datasets have already reached saturation points; recent real-world datasets (e.g., 3DGrocery100[2], OmniObject3D[3]) are not used to evaluate the proposed approach.\n\n[1] Uy, Mikaela Angelina, et al. \"Revisiting point cloud classification: A new benchmark dataset and classification model on real-world data.\" Proceedings of the IEEE/CVF international conference on computer vision. 2019.\n\n[2] Sheshappanavar, Shivanand Venkanna, et al. \"A benchmark grocery dataset of real-world point clouds from single view.\" 2024 International Conference on 3D Vision (3DV). IEEE, 2024.\n\n[3] Wu, Tong, et al. \"Omniobject3d: Large-vocabulary 3d object dataset for realistic perception, reconstruction and generation.\" Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2023.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "YCUb4ZKtvv", "reviewer": "Reviewer_3htV", "summary": "This paper introduces the first parameterization-based dataset distillation (DDP) framework for 3D point clouds, enabling more diverse synthetic samples under fixed memory budgets. Rather than optimizing full-resolution synthetics (as in prior DD), it initializes with M coarse anchors per class (MN₂ ≤ N₁) and generates L additional samples via learnable convex morphing of aligned anchors (akin to 3D mixup). Optimization uses a uniformity-aware SADM loss, which partitions full-res originals into coarse subsets and downweights non-uniform partitions via CV-based penalties. Evaluated on ModelNet10/40, ShapeNet, and ScanObjectNN with PointNet (500 epochs), it achieves SOTA DD accuracy (e.g., 82.5% MN40@10PPC vs. prior 79.6% SOTA) and strong cross-arch transfer (e.g., 47.7% PN++ on MN40@1PPC). However, large gaps to oracle (\"Whole\") persist (e.g., 73% vs 89% MN40@1PPC).", "questions": "1. How does learnable morphing outperform fixed-uniform weights or point cloud mixup (e.g., PointMixup) baselines? Ablate alignment (e.g., w/o Hungarian)?\n2.Explicitly confirm aug for all methods/oracle (e.g., Table: jitter σ=0.01, rot [±15°], scale [0.8-1.2])—did SADM/PCC reimpls match your 500-epoch setup?\n3. Add \"Oracle (Full Dataset)\" column w/ aug details; report relative gap (e.g., 82% of oracle @10PPC)? Test on downstream (PartNet seg, KITTI det)?\n4. Ablate M/L/N₂ tradeoffs (Fig?); why N₂=256, M=4, L=3? Test higher-res anchors or topology-aware alignment (e.g., functional maps)?\n5. Show qualitative synthetics (morphing fails?); robustness to noisy ScanObj aug (occlusion/clutter)? Extend to dynamic scenes (nuScenes LiDAR)?", "limitations": "", "rating": 4, "confidence": 2, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. First to parameterize point clouds via coarse anchors + morphing, yielding ~M+L samples vs. 1 prior—simple, zero-extra-cost diversity boost (storage: 96MN₂KC + 32L(M-1)KC bits ≤ prior budget).\n2. Consistent SOTA over 2D-transferred (DM/DC/MTT), 3D-tailored (PCC/SADM), and coresets across 4 datasets/PPCs; excellent cross-arch gen (Table 2) shows robustness.\n3. Anchor alignment (Hungarian on dist matrices) + uniformity penalty elegantly handles irregularity/coarseness; ablation potential high (e.g., morphing ablation implied).\n4. Fig.1/2 crisp; budget math (Sec.3.4) rigorous; oracle baselines throughout.", "weaknesses": "1. Morphing is point-wise linear interpolation post-alignmen fundamentally 3D mixup on anchors. Lacks novelty over image DDP (IDC/FreD/HaBa) or point cloud mixup; alignment brittle for topology-varying shapes (e.g., chair/table).\n2. DD perf drops ~15-40% vs. oracle (e.g., 32% vs 63% ScanObj@1PPC), understates distillation limits; no segmentation/transfer tasks (e.g., PartNet).\n3. Baseline fairness is unclear. Do priors (PCC/SADM) use data aug (jitter/rotate/scale/chroma std for PointNet)? Paper implies yes (\"standard\"), but unconfirmed; oracle likely uses aug, inflating gaps.\n4. Limited scope/ablation: No hyperparam sensitivity (M/L/N₂); fixed PointNet (not DGCNN/PT from start); no real-time distilling or downstream (e.g., detection).", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "K0VvWvlFUM", "reviewer": "Reviewer_mBpX", "summary": "This paper proposes a parameterized dataset distillation framework for 3D point clouds, which stores multiple coarse \"anchor\" point clouds for each synthetic sample and then generates additional synthetic samples through learnable shape deformation. To reduce the storage budget, the proposed method replaces the full-resolution synthetic sample with several low-resolution anchors plus lightweight blending weights. Experiments on ModelNet10/40, ShapeNet, and ScanObjectNN show substantial gains over prior DD and 3D-DD baselines, especially at low “points per class” (PPC) settings.", "questions": "See weaknesses.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. This paper proposes a novel 3D data parameterization method that expands the diversity of the synthetic dataset under a fixed budget. Replacing one full-res synthetic sample with M low-res anchors and L blended variants is reasonable.\n\n2. The proposed method has strong empirical gains, especially at extreme compression ratios. For example, at PPC=1 with PointNet evaluation, the proposed method improves SOTA by large margins across ModelNet10/40, ShapeNet, and ScanObjectNN. Moreover, it also shows the best cross-architecture transfer in most cases.\n\n3. The proposed method exhibits plug-and-play applicability. Applying it to DM and SADM has a significant performance boost.", "weaknesses": "1. The experiments are conducted on the classification task only. Although effective, there are still some other important tasks in 3D data, e.g., segmentation and detection. Can the proposed method be generalized to other tasks?\n\n2. As the number of classes increases (e.g., ModelNet10 -> ModelNet40), the performance of the proposed method degenerates. Therefore, we may doubt whether the proposed method can be applied to large-scale datasets with more categories. It would be better if the author could provide more experimental results to validate its effectiveness.\n\n3. The number of PPC is limited within \\{1, 3, 10\\}, which cannot achieve lossless performance in most datasets. More experimental results are required.\n\n4. The source code is not provided. The reproducibility is not guaranteed.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)