SegGraph: Leveraging Graphs of SAM Segments for Few-Shot 3D Part Segmentation
Authors: Yueyang Hu
Haiyong Jiang
Haoxuan Song
Jun Xiao
Hao Pan
Venue: NeurIPS 2025 poster ()
Keywords: 3d Part Segmentation; few-shot
Primary area: applications
TL;DR: This work proposes SegGraph, a novel propagation method based on SAM segment graphs that leverages 2D foundation model features and geometric relationships for few-shot 3D part segmentation.

Abstract: This work presents a novel framework for few-shot 3D part segmentation. Recent advances have demonstrated the significant potential of 2D foundation models for low-shot 3D part segmentation. However, it is still an open problem that how to effectively aggregate 2D knowledge from foundation models to 3D. Existing methods either ignore geometric structures for 3D feature learning or neglects the high-quality grouping clues from SAM, leading to under-segmentation and inconsistent part labels. We devise a novel SAM segment graph-based propagation method, named SegGraph, to explicitly learn geometric features encoded within SAM's segmentation masks. Our method encodes geometric features by modeling mutual overlap and adjacency between segments while preserving intra-segment semantic consistency. We construct a segment graph, conceptually similar to an atlas, where nodes represent segments and edges capture their spatial relationships (overlap/adjacency). Each node adaptively modulates 2D foundation model features, which are then propagated via a graph neural network to learn global geometric structures. To enforce intra-segment semantic consistency, we map segment features to 3D points with a novel view-direction-weighted fusion attenuating contributions from low-quality segments. Extensive experiments on PartNet-E demonstrate that our method outperforms all competing baselines by at least 6.9% mIoU. Further analysis reveals that SegGraph achieves particularly strong performance on small components and part boundaries, demonstrating its superior geometric understanding.

SegGraph: Leveraging Graphs of SAM Segments for Few-Shot 3D Part Segmentation
Yueyang Hu', Haiyong Jiang'", Haoxuan Song', Jun Xiao Hao Pan
"Shool of Artificial Intelligence, University of Chinese Academy of Sciences
Schooo of Software, Tsinghua University
(huyueyang23, songhaoxuan24) 0maniss ucas ac. cn Chaiyong jiang, xiaojun] Oucas. ac. cn haopan@tsinghua. edu.cn
Abstract
This work presents a novel framework for few-shot part segmentation. Recent advances have demonstrated the significant potential of 2D foundation models for low-shot 3D part segmentation. However, still an open problem that how to effectively aggregate 2D knowledge from foundation models to 3D. Existing methods either ignore geometric structures for 3D feature learning or neglects the high-quality grouping clues from SAM, leading to under- muder-segmentatioon and inconsistent part labels. We devise a novel SAM segment graph-based propagation method, named SegGraph, to explicitly Iearn geometric features encoded within SAM's segmentation masks. Our method encodes geometric features by modeling mutual overlap and adjacency between segments while preserving intra-segment semantic consistency. We construct a segment graph, conceptually similar to an atlas, where nodes represent segments and edges capture their spatial relation- ships (overlap/adjacency). Each node adaptively modulates 2D foundation model features, which then propagated via graph neural network to learn global geometric structures. To enforce intra-segment semantic consistency, we map seg- detirection-weighhte fusion attenuating
ment features to 3D points with novel contributions from low- low-quality segments. Extensive experiments on PartNet-E demonstrate that our method outperforms all competing basclines by at least 6 6.9% mloU. Further analysis reveals that SegGraph achieves particularly strong per- formance on small components and part boundaries, demonstrating its superior geometric understanding. The code is available at: hatps://github.com/ htapt/luw..ooo/yyyayggggiti 2000/Seggraph
Introduction
3D part segmentation is fundamental task in computer vision and graphics with broad implications
for shape analysis [1, 3D modeling 2 3. and robotic manipulations 4 5]. For instance, a user can edit 3D shape based on part components and embodied agents need interact with diverse object parts for different manipulations, such as identifying a drawer's 's handle or bottle's neck. Many of these real-world applications involve novel shapes, and it is essential to achieve high-quality 3D part segmentation with only a few number of annotations.
2D foundation models (FMs) with powerful generalization capacities have brought new possibilities to part segmentation. Nevertheless, the modality gap between 2D images and 3D geometric shapes poses a fundamental challenge for their direct deployment in 3D domains. A prevalent strategy 6
Joint Corresponding Authors. Haiyong is the Project Lead.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Point Cloud
Paint Cloud
Point Cloud
Point Cloud
Rendered Images
Rendered Images
Rendered Images
3D Feature Leaminn
2D FM
SAM
2D FM
SAM
2D FM
View Pooling
View Pooling
2D Labels
Segment Masks
3D Features
3D Labels
Segment Masks
3D Features
KNN Feature
2D FM
SAM Segments based
Voting
Propagation
Feature Propagation
3D Labels
Labels
Labels
Label Aggregation
(b) 3D Feature Aggregation
(c) Distillation
(d)
Figure 1: Architecture comparisons of different methods. (a) 3D label aggregation- based methods 6
18
I 12 71, (b) 3D feature aggregation- based methods [16 [7]. (c) distillation-baal -baeed methods [13
14 [5] to distill 2D knowledge from foundation models, and (d) ours with an emphasis on SAM
segments and SAM-segments-based feature propagation.
render a 3D shape into multi-view images, then leverage 2D foundation models such as GLIP g or Diffusion [ for image segmentation, and finally aggregate 2D labels to the 3D domain with voting (see Fig. La). During the process, segmentation foundation models, e.g., SAM [TO], can
further enhance the semantic awareness for finer part segmentation [II] 12 77. However, simply
aggregating 2D labels to 3D domains overlooks the geometric structures of 3D inputs, resulting in seder-ssementationn of shape parts and inconsistency among neighbor points.
3D distillation from foundation models 14 15 and 3D aggregation-based feature learning [16 17 take a step forward and learn 3D features with geometry awareness (see Fig. [[p.). Distillation- based methods typically learn geometric features by transferring knowledge from foundation models but rely on large-scale 3D shapes for good performance. 3D feature aggregation- aggregate multi-view image features from foundation models and propagate fused features via KNN- based point structures. However, simple KNN propagation struggles to encode complex geometric patterns and does not take into account grouping clues from SAM. Moreover, these two schemes involve downsampling 3D points (about 100 times for PartNet-E) or using voxel-based 3D feature encoding, leads ambiguous features for boundary points and small components.
methods
We propose novel framework, called SegGraph, for part segmentation of 3D point clouds. We develop the method based on three key insights. First, SAM's high-quality segmentation provides consistent intra-segment grouping cues for 3D points, effectively reducing misclassification errors near boundaries. These segments can efficiently encode 3D geometric structures with less than 1000 SAM segments for each shape, avoiding input downsampling. Second, spatial relationships among segments offers two critical priors for part segmentation: (1) overlapping segments across different views typically share the same part label, (2) adjacent segments naturally preserve part boundary information. Third, the quality of SAM segments correlates with the viewing direction of an image, exhibiting under-segmenttttin of small parts in challenging views.
To implement these three observations, we introduce a segment graph propagation- based part learning framework, as illustrated in Fig. 2 First, we encode 3D point features through multi-view feature pooling from 2D foundation model outputs. Simultaneously. we generate view-consistent 3D segments by aggregating SAM-based 2D segmentations across views. We then model both the spatial relationships everlapping/adjaceen between segments and the constituent relationships between points and segments. The segment graph takes 3D segments as nodes and represent spatial relationships nodes. Segment features encoded from its constituent points' aggregated features propagated via a graph neural network for geometric structure-aware feature encoding. Thereafter, enhance 3D point features with segment features with view quality-aware feature unpooling, where points with a normal off a view direction for obtaining the SAM segment are assigned with lower importance.
Extensive experiments demonstrate that SegGraph can significantly outperform competing methods by large margin of 6.9% mloU on the PartNet-E dataset 66. SegGraph is quite extensible and can leverage different kinds of foundation models with an improvement of at least 4% on the PartNet-F dataset.

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2 Related Work
Supervised 3D Segmentation. Supervised 3D segmentation has been widely studied in recent years, facilitated by the availability of annotated datasets [19] 20
21
22
23
24] Prior works
have proposed variety of architectures tailored to 3D data, including point-based models (e.g..
PointNet [25) and its variants (26 25 27 28 ), volumetric CNNs [29]
30 31, graph convolutional
networks 32
33 M
and Transformer-bacee methods [35] 36 37 These methods are typically
trained with large amounts of manually annotated 3D data. A particularly challenging sub-task is 3D part segmentation that predicts semantic part labels for a shape. PartNet (23] and DeepGCNs [38] achieves part segmentation via graph convolutional network (GCN) based model to learn local geometric features. CSN [39] improves part segmentation by introducing a cross-shape attention mechanism that captures interactions among different shapes in 3D point clouds. However, most of these methods rely on dense part annotations for good performance. Some works explore multi- prototype networks with attention [40] and learning part-specifie probability spaces via template morphing and density estimation [1 for few-shot part segmentation.
3D Part Segmentation with Foundation Models. Recent advancements of foundation models 42 I0 43 have shown remarkable capabilities across diverse tasks. simple yet effective approach to exploit 2D foundation models for 3D tasks is to render multi-view projections of the 3D data, feed the rendered images into 2D foundation model, and subsequently aggregate the model outputs back to 3D via voting. Pioneering efforts like PartSLIP [6] and SATR [18] follow this scheme and utilize GLIP 8 for vocabbooyyylary segmentation. PartSLIP++ [ and PartSTAD [1] further utilize detections of GLIP the visual prompts for SAM [10] for better part masks, while 3-by-2 (7] constructs multiview labels based on DIFT [9] and aggregates 2D labels to 3D based on multiview masks from SAM [00. However, aggregating 2D segmentation into 3D entirely overlooks the inherent geometric structure of the 3D data and consistent point segmentations among neighbors, leading to under-segmentattion and noisy parts. COPS 44 and 17 address this problem by directly aggregating pooling
2D features from foundation models to 3D and propagating 3D features via superpoint- and KNN-based feature smoothing for superpoints. Despite better results, feature aggregation with simple KNN smoothing disables complex 3D context learning and geometric superpoints are often inconsistent with part grouping. Another line of work [13 [4 15] leverages a iistillation-basel framework. For example, PartDistill [13] presents a bidirectional distillation between predicted 2D labels and 3D labels from a native 3D network. SAMPart3D [14 and PartField [15 construct training pairs for contrastive learning based on SAM segmentation. These distillation methods require large amount of 3D data to learn generalizable 3D features. This work combines the benefits of both SAM segmentations and light-weight 3D feature aggregation with shape structural context encoding for easy adaption to few-shot novel shapes.
Methodology
Our goal is to predict a part label for each point of given point cloud with only low-shot training samples for novel shapes. We approach this problem with a graph-based feature aggregation network as illustrated in Fig. 2 The method encodes point-wise features using an off-the-shelf foundation model (Sec. 3.I and generates a list of segment masks based on SAM segmentation (Sec. (.2p. Then we construct SAM-segmentt-bsed graph to encode different kinds of relations among different
segments and 3D points (Sec. 3.3)
3.1 Feature Encoding for 3D Points
To harness the power of image foundation models, such as DINOv2 43 and CLIP [42]. our approach renders a point cloud input into multi-view images from M predefined camera views. We denote rendered images as { E H W Jm=1. The rendering process follows PartSLIP 6). incorporating occlusion culling 45 to eliminate obscured points. Next, we feed each view image to an image foundation model (DINOv2 in our implementation) to extract image features. However, the 114 is usually lower than the input image. Therefore, image feature maps are upsampled to the original image resolution using bicubic interpolation and
resolution of output image feature maps (WW x H
are mapped from 768 channels to 96 channels with a linear layer. We can obtain the 3D feature F? for each point by taking the average of its projected image features at views where the point is not occluded.
3

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Segment Encoding
Point Feature Encoding
meay Aoneraan
Graph Propagation
SAM
Part Prediction
$
Input Point
Multi-view 2D Rendering
Segment Generation
Unpooling
Figure 2: The overview of the pipeline. Given 3D point cloud, we render it into multi-view images. We extract individual point features by pooling foundation model features (DINOv2) of rendering views. At the same time, multi-view images are fed to SAM for segment generation. Point features and segments are sent to segment graph to learn geometric features and segment relations for part prediction.
3.2 Segment Generation
In light of the high-quality segmentation masks of SAM [T0] we further leverage these masks to enhance the quality part segmentation. While the semantic granularity of SAM's predictions may vary across views, these masks nevertheless provide reliable grouping clues (see the colored segments in Fig. 3. Our method processes each rendered image using SAM produce an initial set of segmentation masks. Following the approach in [7]. we subsequently decompose overlapping masks under single view into non-overlapping over-segmented regions. This decomposition effectively resolves semantic confusions for 3D points whose projections fall within multiple overlapping masks of varying gramularity and semantics. Afterwards, all 3D points that project within the same 2D segmentation mask form corresponding segment. The set of segments is denoted as S.
3.3 Segment-based Feature Refinement via Graph Propagation
Given 3D point features F2 and segments S, we further exploit the grouping cues from individual segments, along with their underlying geometric structures, to enhance 3D features. The key motivations are as follows: 1) points within the same segment typically share the same part label; 2) overlapping segments constructed from different views likely correspond to the same part label: 3) adjacent segments from same or different views encode the holistic 3D structure and delineate possible part boundaries. To compile these ideas into framework, we first encode segment features, then propagate them through overlapping segments and adjacent segments, and finally map segment features to individual point for part segmentation.
Segment Encoding. straightforward way to compute segment features to average the features of their member points. However, simple pooling fails to account for the varying semantic importance of individual points based on their geometric attributes, including their spatial distribution relative to the segment centroid and local surface normal. Inspired by [46,, we instead learn adaptive point contributions with a geometric feature encoding module. For each point Pj within a segment, the module calculates its normalized relative position p/ w.r.t. the centroid c; of segment S; as follows:
Pj C
pJ max Pp)) KES.
min (Pp) kES,
Then we compute the local geometric feature F E RC with a multi-layer perceptron that takes as input the concatenation of point's normal and its normalized position pj.. Afterwards, we obtain the segment feature F I by applying max pooling to the local geometric features F of all points within the segment. The segment feature F* is further refined through an attentively weighted aggregation of point features F3, where attentive weights are computed with an additive attention on F and F

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Ovter
Segment Nodes
Gueclepping Fldges
Figure 3: An illustration of graph construc- tion. Features of 3D points (green dots) first aggregated segment nodes (blue boxes).
Figure 4: Two examples for the impacts of the rendering views on the quality of SAM segments. For each group, left: input, right: SAM segments.
Building a SAM Begment-based Graph. Denote the graph as G = -V,E) with nodes rep- resenting 3D segments and edges & encoding segment relationships. The segment relationships include 3D adjacent relations & and overlapping relations & An overlapping relationship exists between two segment nodes in different views if their corresponding 3D points have significant overlaps (mIoU larger than 10%). An adjacent relation between two segments is constructed if their covered points have rare overlaps but are close enough (with minimal distance between points of two segments less than 0.01 units in the normalized space). Note that overlapping relations and adjacent relations are mutually exclusive. We show example of segment graph in Fig. In this example, overlapping segments (connected by dashed lines) share identical part labels, while adjacent segments (connected by solid lines) demarcate part boundaries. This graph structure is analogous to an atlas in differentiable manifold mapping the 3D shape consistently. Specifically, each segment corresponds a chart, and transitions between charts are edges derived from the adjacency and overlap relationships among segments; the collection of all charts and their connecting edges constitutes an atlas. In this way, our segment graph provides structure for analyzing the semantics of object shapes with geometric consistency.
Feature Propagation via the Segment Graph. After building the segment graph, we propagate segment features through the edges using GATv2 48). We feed the segment feature F to three GATv2 network layer. Given the distinct nature of overlapping and adjacent relationships, we implement two GATv2 networks with separate weight parameters to model these relationships, respectively. Then we concatenate the output features of two GATv2 at each layer and feed them to MLP as the node features for the next layer.
Viewing Quality-Aware Feature Unpooling. Subsequently, we propagate segment features to member 3D points to enforce label consistency within segments. This propagation requires careful feature fusion since points usually belong to multiple segments with varying reliability. A naive averaging approach is suboptimal segments from some challenging views usually of low quality, As shown in Fig. 4 under certain challenging viewpoints, parts such as laptop keyboards and clock components are not well segmented by SAM. In contrast, under more favorable viewpoints, SAM is able to segment these parts accurately. As the quality segment masks often depends on the viewing angle, we devise viewing quality-awan aware unpooling module for this purpose. This module estimates the quality based on the point normal n, and the camera view direction:
P3 P, - c
: n;
where e; is the camera position for extracting segment and n, is the normal direction of point The quality score Wi, is further refined with learnable MLP with softmax to reweighing the quality scores. The final 3D point features combine the original point features and weighted fusion of segment features. and then the features are fed to the segmentation head with a two-layer MLP to produce the logits? Y:
Fd = F
+
:.
F
Y MLP(FF.).
(3)
iES
5

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For the network training, we use cross-entropy as the objective to train the network with few-shot examples.
4 Experimental Results
We evaluate the effectiveness of our method on two benchmark datasets: PartNet-Ensemble (PartNet- E) [6] and NeapeN 49 PartNet-F, proposed by PartLLI [6], consists of 1,906 shapes across 45 categories with RGB colors. The dataset splits follow that of [1]. ShapeNetPart contains 31,963 shapes spanning 16 categories but lacks color information. We adopt the mloU as the evaluation metric. We evaluate our method on the official testing set using a few-shot setting, where 8-shot examples are sampled from the training set for all experiments. We trained our method three times and reported the average mloU and the standard deviations to reflect the influence of training variances using different seeds.
4.1 Comparisons Part Segmentation
Baselines.
On the PartNet-E dataset, we compare SegGraph with fully supervised approaches 37
26 50) well state-of-the-ar few-shot methods based on 2D foundation models including
label aggregation-based methods [6 12 77, and distillation-based methods [33 15355. The fully
supervised methods were trained on 28K objects from 17 overlapping categories in PartNet 23 that
overlap with PartNet-E, along with the few-shot training shapes. The few-shot baselines were trained solely on the few-shot set. For the zero-shot method PartField. which successfully distills part-level 3D representations from SAM, we fine-tune an MLP on its extracted features to adapt the model to provide testing codes and weight checkpoints for comparisons, while 7 does not have available sources. For methods without sources and that we fail to reproduce, we use their reported results and mark them with However, as few-shot methods, e.g.. 66 12 13 require prompt tuning and do not have training code, we instead compare with PointCLIP 511 and PointCLIPv2 [52) with support of few-shot learning on the ShapeNetPart. Specifically, we replace text embedding for classification in PointCLIIP and PointCLIPv2 with trainable MLP classifier for few-shot example training.
the few-shot setting. Among the above-mentioned methods, 66
12 [3]
Results on PartNet-E. Tab. presents the comparison results on PartNet-E (see the supplementary for per-category results). All foundation model-based methods achieve superior performance to supervised ones (the top three rows), even with only few-shot training. This suggests the strong capability of foundation models on downstream tasks. We also observe that label aggregation- based methods 66 [I 12 71. performs worse than feature aggregation- based methods 7 and distillation- based methods 13 15), because label aggregation- based methods rely solely on 2D images to obtain labels, completely ignoring the geometric information crucial for 3D part segmentation. Among all methods, our approach outperforms the current state-of-the-are few-shot part segmentation method, PartDistill, by significant margin with more than 6% gains in mloU across all 45 categories, achiev- ing the highest performance in 32 of them. Notably, we observe over 10% absolute improvements on categories such as Door and Lamp, which, based on observations, can be attributed to our method's 'ss superior ability to segment relatively small parts such as door handles and light bulbs.
Despite the overall improvement being substantial, different training runs of our method produce varying performance because of random initial seeds. When training the model three times with the same setting, the average standard deviation across the 45 categories is about 1.09%.
Since most of these foundation model-based methods use GLIP, to ensure fairer comparison, in addition to employing DINOv2, we also transformed GLIP into a feature extractor for comparison, as detailed in Sec. 4.2 The results show that when using GLIP as the feature extractor, our method still achieves performance that is only slightly lower than that obtained with DINOv2, as shown in Tab.
Further inspection of segmentation results with large variances reveals that semantically ambiguous 3D segments are prone to errors in the few-shot setting, leading to the variance in different training runs with random initial seeds.
We further inspect the improvements on different sized parts. Benefiting from the over-segmented segments and the segment graph, our method demonstrates significantly improved performance (more than +20%) in segmenting small parts. illustrated Tab. we select six representative categories that include small parts such as buttons (in Coffee Machine, Remote), knobs (in Coffee Machine),

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Table 1: Few-shot part segmentation results on PartNet-E. The mloU metric is measured in percent- age. The number in the top row bracket counts the number of shape categories. SD is for Stable Diffusion [53]. The methods in the top three rows are trained with both few-shot shapes and PartNet shapes that share overlapping categories (17) with PartNet-F.
Overlapping Categories (17)
None Overlapping Categories (28)
(45)
Methods
FM
Bottle
Chair Door Knife Lump Overall Cumeru
Dispe.
Kettle Oven Suitca, Overall Overall
PointNet++
26
48.8
84.7
45.7
35.4
68.0
55.3
6,5
12.1
20.9
34.4
40.7
25.0
36.5
SoftGroup
41.4
88.3
53.1
31.3
82.2
50.4
23.6
18.9
57.4
13.7
18.3
31.3
38.5
PointNext
3
68.4
91.8
43,8
58.7
64.9
59.1
33.2
26.0
45.1
37.8
13.6
45.5
50.6
ParSSLIP
GLIP
83.4
85.3
40.8
65.2
66.1
56.3
58.3
73.8
77.0
73.5
70.4
61.3
59.4
PanSLIP++
11
GLIP SAM
85.5
85.3
45.1
64.3
68.0
59.7
63.2
72.0
85.6
70.3
70.0
63.5
62.1
PartSTAD
12
GLIP & SAM
83.6
85.3
61.4
63.8
68.4
61.4
64.44
73.7
84.2
71.9
68.3
67.1
65.0
3-By-2
7
SD & SAM
80,9
84.4
54.4
75.1
59.5
60.4
62.6
78.2
81.5
60.0
65.2
66.5
64.2
PariDistill
[3]
GLIP & SAM
84.6
88.4
55.5
71.4
69.2
64.6
60.1
74.7
78.6
72.8
73.4
66.7
65.9
PartField
[5
SAM
75.9
87.6
65.4
72.9
73.8
65.7
52.0
73.9
82.4
50.2
72.0
66.7
66.3
GLIP & SAM
87.5
88.5
69.5
79.3
84.2
69.1
66.7
74.8
88.2
70.2
72.8
71.5
70.4
SegGrapph (ours)
12:44
(1 140
1::
DINOv2 & SAM
90.0
89.5
73.6
77.3
78.5
70.1
70.8
77.6
87.8
75.7
77.3
74.4
72.8
12 866;
12 B4R
12 839)
(1
886
t2 631)
(1 6000
(8 L01)
(2 0.86
(L) (uT)
(1 1.k
(1) 1:30
(1 k.y
12: 1.9
Table 2: Results on shapes with small components of PartNet-E. The mIoU metric measured in
percentage.
Coffee Machine Small
Door
Laptop
Lamp
Safe
Remate Large
Overall (6)
Lunge
Large
Small
Large
Small
Large
Small
Lurge
Small
Small
Large
Small
PanSLIP 6
57.1
17.4
44,1
47.3
62.0
10.7
84.1
13.1
68.0
19.4
36.5
56.5
24.8
PanSLIP++ 11
59.2
18.4
43.6
48.5
62.7
7.6
86.1
13.5
71.6
20.1
36.4
58.5
25.1
PanSTAD 12
52.8
18.9
67.7
48.9
71.2
10.2
86.9
12.7
66.5
22.0
53.4
62.1
28.9
Ours
55.5
28.7
81.7
55.4
76.5
34,5
84.8
39.3
81.6
54.6
82.0
64.1
49,8
Table 3: Few-shot part segmentation results on ShapeNetPart. The mIoU metric is in percentage.
Methods
VLM
Airplane
Cap
Car
Knife
Laptop
Mug
Pistol
Table
Overall(16)
PointCLIP [51
CLIP CLIP
27.2
61.1
30.2
72.2
89.9
59.6
55.6
53.0
52.1
PointCLIPv2 52
37.1
66.8
38.3
73.3
89.1
75.7
68.4
56.3
57.0
SegGraph
37.81
73.96
48.19
74.8
90.98
80.08
70.6
72.72
62.6
CLIP SAM
(ours)
(.73
0.3)
1.52)
0,30)
(4:
0,0)
1.60
1.20
1.00)
and cameras (in Laptop). Both the quantitative results in the table and the qualitative visualizations in indicate that our method exhibits clear advantages in accurately segmenting small parts.
Fig. 5
In Fig.5 we compare the predictions of SegGraph with those of previous approaches. Benefiting from the high-quality over-segmentation provided by SAM, our method achieves superior segmentation performance on small-sized parts compared to the 2D label aggregation methods used in the second and third rows. Notable examples include the hands of clocks, buttons on phones and remote controls, and handles on pot lids. Compared to the fourth row, which uses 3D feature-based method, our method exhibits greater advantages along the boundaries of small objects, such as the handle of a bucket and the clock hands. Overall, our approach yields better segmentation performance for small-size parts.
Results on ShapeNetPart. Tab. 3 presents the evaluation results on ShapeNetPart. Since only sparse point clouds without color are available for ShapeNetPart (2048 points), we follow PointCI.IP-v2 to densify and smooth point clouds in preprocessing. Therefore, the rendering depth images of Seape are of significantly lower quality compared the rendered images of PartNet-E, leading to worse SAM segmentation and pretrained image features. However, our method still prevails against baseline methods with 5.6% improvement in mloU.
4.2 Ablation Studies
We ablate each module in our method. As the vanilla baseline, we aggregate multi-view 2D features extracted by DINOv2 4 onto the 3D space using average pooling, resulting in 3D feature represen- tation. MLP is then employed as classifier to perform point-wise segmentation on the aggregated 3D features without using SAM segments. The performance of this vanilla baseline is shown in the first row of Tab. 4 All experiments are conducted on all shape categories of PartNet-E with an 8-shot setting. We leave hyperparameter analysis and more results to the supplementary.
7

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Kitchen Pot
Clock
Phone
Remote
Trash Can
Input
PartSLIP
PartSTAD
PartField
SegGraph (ours)
GT
Figure 5: Qualitative comparison of part segmentation results on the PartNet-E dataset under the few-shot setting.
Table 5: Results of using different founda- Table Ablation experiments. SE is for segment en- tion models and feature propagation (Seg- coding and AVE for mean pooling or unpooling. SAM Graph MLP).
means using 3D segments from SAM segmentation.
Bottle Door Suitea. Overall (45)
SAM AVE-SE AVE-Eq
Bottle
Door Suitca. Overall(45)
MLP
63.2
35.0
44.0
39.3
(
83,8
62.2
70.3
65.2
CLIP
42
SegGraph
(3)
(
88.7
89.0
72.7
72.8
67.7
70.5
70.5
70.7
Diffusion
SegGraph
MLP
85.0
78.8
59.0
$4.0
59.1
69.4
56.8
64.3
72.5
(5)
87.9
87.8
70.5
72.3
66.9
71.0
GLIP X
SeyGraph
87.4
69.5
62.1
72.8
61.1
70.4
(7)
89.0
73.3
74.4
72.2
PartField 15
MLP
75.9
65.4
72.0
66.3
SegGraph
76.6
70.9
75.9
70.3
<1 90.0
73.6
77.3
72.8
DINOv2
33
SegGraph
MLP
82.0
62.8
77.3
68.5
72.8
65.2
The Role of Segment Encoding (SE) and Viewing Quality-Awarenee check marks for AVE-SS use average pooling, while those without check marks for AVE removes viewing quality awareness and uses average unpooling, instead. In rows 3-4 of Tab. applying either SE or (2) individually results only marginal even no improvement. However, using both modules together (row 5 row 2) leads to slight improvement (0.5%).
The settings without
The Role of Different Segment Relations. In rows 2,6 Tab. even without segment encoding and viewing quality awareness, incorporating segment graph leads to segment improvement (+0.9%), suggesting the positive role of graph propagation with GATv2. We conjecture these two graph edges encode geometric features and segment relations, leading to more consistent part segmentation.
Comparing rows 5,7.8 of Tab. 4 using either kind of graph edges for feature propagation with segment encoding and viewing quality awareness exhibits significant performance improvements (1.2% for E, and 1. 1.3% for E,, respectively). Using both edges simultaneously lead performance gain of 1.8% (see rows 5,9).
Robustness Different Foundation Model Features. We conducted experiments by replacing
DINOv2 43 with pretrained features of three different foundation models: CLIP [42] Diffusion [9] GLIP and PartField [15). Among them, PartField a 3D model distilled from a 2D foundation model. Since most comparison methods in Table aabopt GLIP, for fair comparison, we also modified GL.PP into a feature extractor for our experiments. For GLIP, we utilized its multi-scale visual features fused with text features, and further applied Feature Pyramid Network (FPN) structure to integrate these multi-scale representations, thereby transforming GLIP into feature extractor. comparison, also replaced the graph propagation module with MLP to assess the role of structural

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Table 6: Detailed runtime analysis of each component in the data Table 7: Training and in- preprocessing stage in our method compared with PartSLIP++. All ference time comparison per shape.
runtime measured on a per-shape basis.
Method
Ronder (5)
GLIP44AM//AN SAM
Voting Preprocess/ Build Graph
Total (6)
Method
Train (s)
Inference (s)
PunSLIP++
Dun
16.33 (GLIPSSAM)
(SAN)
92.38 (Nating Preprocoss)
(Build Graph)
110.30
PanSLIP++
5.83
Oun
2.12
L300FiSAAM)
10.40 (Build Gruph)
13.82
Ours
2.25
1.46
reasoning. As shown in Tab. when replacing the ML. with our proposed graph propagation module, we observe consistent and significant performance improvements across all foundation models. This demonstrates that our method is not only well-suited for 2ID-to-3ID knowledge transfer, but also effectively enhances the performance of 3D segmentation models. These results validate the strong generalizability of our approach across different types of feature representations.
4.3 Runtime Analysis
Tab. 6 and Tab. 7 present the runtime analysis of each component and comparison with PartSLIP+ on the PartNet-E dataset. All measurements are on a per-shape basis, with training time referring to one epoch for single shape, using single NVIDIA V100 GPU.
For PartSLIP++, most preprocessing time is spent on superpoint generation via L0-cut pursuit and voting weight computation (92.38 s). Its weighted voting during inference also results in high latency (5.8 s).
For our method, the main overhead stems from SAM segmentation on ten multi-view images. As the SamnAutomaaieeMssSGGnnnnnd processes one image a time, this step is relatively slow but still faster than PartSLIP++ (64.9 s vs. 10.8 s). Employing faster variants such as FastSAM (54) further reduces the time but slightly degrades accuracy (72.8 + 70.9 mloU).
During inference, our method is more efficient (1.46 s vs. 5.83 s). Since PartSLIP++'s training code reasonably expect that
unavailable, its training time is omitted. However, is much faster than PartSLIP++'s 's based on the inference time comparison. Note that both the "Train" and "Inference" time measurements include the time for DINOv2 feature encoding.
our training time
4.4 More Analysis
Feature Visualization To more clearly demonstrate the capability of our method in learning 3D structural information, we visualize extracted 3D features in Fig.
6 As a baseline for comparison,
we use 3D features obtained by directly averaging DINOv2 [43 features of multi-view rendered
images, without any graph-basee corresponding the RGB color channels for visualization. Fig. 6 suggests that the features processed by SegGraph exhibit clear separability among different parts. This strong discriminative ability arises from aspects. First, the few-shot fine-tuning process enables the model learn meaningful part-level distinctions even the number of shots is quite low (8-shot). Second, it benefits from the proposed graph design encode 3D structural information. For instance, regions such the screen area of the phone and the supporting bracket at the top of the coffee machine on the far right-both of which lack annotations in the training set-still exhibit clear part-level separability in the feature visualization.
propagation. We apply PCA to reduce the feature channels to 3,
Feature Similarities Across Shapes. Given the strong part-level separability observed in Fig. 6 we further explore feature similarities in Fig. 7 Specifically, after selecting anchor point within a shape (indicated by the red arrow), we compute the Euclidean distance between the feature of each point and that of the anchor point, either within the same shape or across different shapes (as shown in the second row). The distances are then normalized, with a distance of 0 mapped to blue and distance of I mapped to gray. As demonstrated in the figure, the selected anchor point yields consistent part-level feature similarity, both within the same shape and across different shapes.
Failure Cases. We examined the visual results and observed an interesting failure case the USB category (Fig. M). The USB model contains two disconnected parts-the cap and the body-each each with a visually and geometrically similar connection subpart. While SAM correctly distinguishes these subparts, DINOv2 features for them are nearly identical despite their spatial separation. Consequently,

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SegGraph misclassifies the two as belonging to the same category. This case indicates that SegGraph can be confused by repeated or similar sub- sarb-pats even when they are spatially distant.
Point Cloud
DINOV2
Ours
Figure 6: Feature Visualization. Points with similar colors are likely to share the same part label.
Figure 7: Feature similarities between an anchor point (red arrows) and points in the same shape (top row) and points in different shape (bottom row). We use colors to encode feature similarities.
Figure 8: Failure cases on the USB category
Conclusion and Discussion
We presented SegGraph, a SAM segment-based graph propagation method for utilizing 2D features of foundation models for 3D part segmentation. Based multi-view segmentation masks generated by SAM, we construct a graph to encode geometric features with segments as nodes and spatial relationships between nodes edges. Through feature propagation effectively adapts features from 2D foundation models to the 3D domain. SegGraph achieves state- of-the-art performance 3D part semantic segmentation, especially for part boundaries and small components.
this graph, method
While our method demonstrates strong performance, there some limitations. First, the rendering- view based feature aggregation paradigm fundamentally limits our ability to handle occluded or internal structures of 3D models. Second, the current framework operates at a fixed segmentation scale, without accommodating multi-scale part granularity. Future work could explore constructing hierarchical semantic representations of shapes, facilitating more versatile applications.
Acknowledgement. We thank the anonymous reviewers for their insightful comments. This work was partially supported by the National Natural Science Foundation of China (62271467 62476262, 62206263, 62306297, 62306296), Beijing Nova Program, and Beijing Natural Science Foundation (4242053, L242096).
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Reviews:
[{"review_id": "qfGObGBOaZ", "reviewer": "Reviewer_URb2", "summary": "This paper presents SegGraph, a novel framework for few-shot 3D part segmentation that leverages SAM segments organized in a graph structure. The key innovation is constructing a segment graph where nodes represent 3D segments from multi-view SAM segmentation, and edges encode spatial relationships (overlap/adjacency). Features are propagated through this graph using GATv2, followed by view-quality-aware unpooling to map segment features back to 3D points. The method achieves state-of-the-art results on PartNet-E and ShapeNetPart datasets, showing particular strength in segmenting small parts and boundaries.", "questions": "1. How does the method perform on objects with significantly more complex part structures than those in PartNet-E?\n2. What is the computational overhead compared to simpler baselines, particularly for inference?\n3. How sensitive is the method to the number of views used for rendering?", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "# Strengths\n1. Well-motivated technical approach: The use of SAM segments as building blocks for 3D understanding is intuitive and well-justified. The observation that SAM provides high-quality grouping cues that can be leveraged for 3D part segmentation is valuable, and the graph-based propagation effectively captures geometric relationships.\n2. Technical soundness: The segment graph construction with overlap and adjacency relationships is well-designed. The view-quality-aware unpooling module addresses the practical issue of varying SAM segment quality across viewpoints.\n3. Clear presentation: The paper is well-written with clear motivation, good visual illustrations, and comprehensive experimental analysis.\n# Weaknesses\n1. Limited technical novelty: While the combination is effective, the individual components (multi-view rendering, SAM segmentation, graph neural networks) are not novel. The main contribution is in their combination, which limits the technical depth.\n2. Computational overhead not adequately addressed: The method requires SAM inference on multiple views plus graph neural network computation. While Table 5 shows training time comparisons, inference time analysis is missing. The computational cost compared to simpler baselines should be more thoroughly analyzed.\n3. Dependence on view quality: The method's performance is inherently limited by the quality of rendered views and SAM segmentation. While the view-quality-aware module partially addresses this, fundamental limitations remain for occluded or poorly-rendered regions.\n4. Scalability concerns: The evaluation is limited to relatively small datasets and objects with moderate complexity. It's unclear how the method would scale to larger, more complex objects or datasets, like ScanNet [21], Objaverse [A], UDA-Part [B], or ABC [C] datasets.\n\n[A] Deitke, Matt, et al. \"Objaverse: A universe of annotated 3d objects.\" Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2023.\n\n[B] Liu, Qing, et al. \"Learning part segmentation through unsupervised domain adaptation from synthetic vehicles.\" Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2022.\n\n[C] Koch, Sebastian, et al. \"Abc: A big cad model dataset for geometric deep learning.\" Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2019.", "was_revised": true, "final_justification": "The author addresses my questions, so I increase the score.", "rebuttal": "Thank you for your valuable comments and kind words to our work. Below we address specific questions.\n\n**Q1: Limited technical novelty: While the combination is effective, the individual components (multi-view rendering, SAM segmentation, graph neural networks) are not novel. The main contribution is in their combination, which limits the technical depth.**  \n**ANS:** As illustrated in Figure 1, the novelty of this work lies in the construction of a structured graph based on SAM segments as an efficient 3D representation to aggregate foundation model features. The graph effectively encodes the adjacency and overlapping relationships of SAM segments within a single view and across views. Moreover, this graph-based representation helps mitigate the label imbalance between small parts and large parts that have very different numbers of labeled points, by using a more uniform number of SAM segment nodes. For example, in the Laptop category, both the large screen and the small individual keys are represented as single nodes after SAM segmentation. Therefore, our method achieves better results on small parts.  \nBesides, both you and Reviewer V5oZ acknowledge the novelty of View Quality-Aware Unpooling that is potentially beneficial for other tasks.\n\n**Q2:  The computational cost compared to simpler baselines should be more thoroughly analyzed.**  \n**ANS:** The table below presents a detailed runtime analysis of each component in our method, alongside a comparison with the classical baseline PartSLIP++. All runtime is measured on a per-shape basis. The training time is counted for the optimization of one epoch of a shape.  \nFor PartSLIP++, the main computational cost during preprocessing lies in generating superpoints from the point cloud using L0-cut pursuit and computing voting weights for each superpoint (vote\\_preprocess: 92.38 seconds). During inference, its complex weighted voting mechanism leads to a relatively high inference time (5.8 seconds).  \nFor our method, the main preprocessing cost arises from SAM segmentation for 10 multi-view images. Because the adopted SamAutomaticMaskGenerator processes only one image each time, this step is relatively time-consuming. However, faster models could be adopted in the future to accelerate this process. Even so, our preprocessing time is significantly lower than that of PartSLIP++ (64.921s vs. 110.83s).   \nIn the inference stage, our method achieves higher efficiency (1.46s vs 5.83s for PartSLIP++). It is worth noting that since the training code of PartSLIP++ is not publicly available, its training time is not reported in the table. However, we can reasonably expect that our training time is much faster than PartSLIP++'s based on the inference time comparison. Note that both the \"Train\" and \"Inference\" time measurements include the time for DINOv2 feature encoding.\n\nTime spent on preprocessing：\n\n| Method     | Render (s) | GLIP+SAM / SAM (s) | Voting Preprocess / Build Graph (s) | Total (s) |\n| :--------- | :--------- | :----------------- | :---------------------------------- | :-------- |\n| PartSLIP++ | 2.12       | 16.33 (GLIP+SAM)   | 92.38 (Voting Preprocess)           | 110.30    |\n| Ours       | 2.12       | 52.40 (SAM)        | 10.40 (Build Graph)                 | 64.92     |\n\nTime spent on training and Inference:\n\n| Method     | Train (s) | Inference (s) |\n| :--------- | :-------- | :------------ |\n| PartSLIP++ | —         | 5.83          |\n| Ours       | 2.25      | 1.46          |\n\n**Q3： How does the method perform on objects with significantly more complex part structures than those in PartNet-E?**  \n**ANS:**  Our method is not designed for scene-level part segmentation (ScanNet) and image-based part segmentation (UDA-Part).  Extending the method to ScanNet requires both scene-level part annotations and non-trivial designs to handle thousands of frames all at once, which is out of the scope of this work and needs substantial efforts.  Below, we test on 3D part segmentation on ABC primitives and more complex shapes.   \n1\\.  We selected 8 shapes for training and 100 shapes for evaluation from the ABC-Primitive dataset. We compared our method with PartSLIP++. However, as PartSLIP++ has not released its training code, we evaluated it in a zero-shot setting instead. Since the ABC dataset does not contain color information, we followed the approach used in PointCLIP-v2 by rendering the point clouds into depth images for processing. However, depth images are not a native input modality for models like GLIP, DINOv2, and SAM, leading to varying degrees of performance degradation for both PartSLIP++ and our method.\n\n| Method                 | mIoU  |\n| :--------------------- | :---- |\n| PartSLIP++ (zero-shot) | 3.68  |\n| Ours (few-shot)        | 40.13 |\n\n2\\. We note that the part annotations in Objaverse are quite inconsistent, and evaluate on the 3DComPat++\\[1\\] dataset for complex shapes with fine-grained part segmentation annotations. Due to limitations in time and computational resources, we selected three categories—**airplane**, **bicycle**, and **chair**—for evaluation. The number of fine-grained part labels for each of these categories is as follows: the airplane category contains 14 parts, the bicycle category contains 26, and the chair category contains 22, reflecting the complexity of these shapes\\.  \nWe use the mesh data provided by 3DComPat++ and sample 100,000 points from each object as the experimental data. We compare our method with the representative baseline PartSLIP++. Since the training code of PartSLIP++ is not fully open-sourced, we evaluate it under the zero-shot setting. In contrast, our method is evaluated under both 1-shot and 8-shot settings. The evaluation metric reported in the table below is the mean Intersection-over-Union (mIoU).\n\n| Method                 | Airplane | Bicycle | Chair |\n| :--------------------- | :------- | :------ | :---- |\n| PartSLIP++ (zero-shot) | 9.48     | 2.60    | 4.87  |\n| Ours (1-shot)          | 45.34    | 26.26   | 27.21 |\n| Ours (8-shot)          | 65.38    | 54.17   | 42.61 |\n\nAs shown in the table, our method performs well even when faced with more complex part structures, demonstrating strong generalization capability.\n\n**Q4:  How sensitive is the method to the number of views used for rendering?**  \n**ANS:**  Both PartSLIP and 3-by-2 show that when the number of views exceeds 10, the performance gains begin to plateau. Based on this observation, we also adopt 10 views as the default configuration in our experiments.  \nOur results are shown in the table below. When the number of views exceeds 10, the improvement in model performance significantly diminishes as the number of views increases. Conversely, when the number of views is fewer than 10, reducing the number of views leads to a substantial decline in model performance, highlighting the importance of sufficient multi-view information for the model’s effectiveness.\n\n| Category   |  15   |  12   |  10   |   9   |   8   |   7   |\n| :--------- | :---: | :---: | :---: | :---: | :---: | :---: |\n| KitchenPot | 85.25 | 85.61 | 85.20 | 80.15 | 79.85 | 73.49 |\n| Bottle     | 92.51 | 90.85 | 90.00 | 88.70 | 81.07 | 80.64 |\n| Chair      | 89.97 | 89.83 | 89.50 | 84.07 | 82.35 | 81.36 |\n\n\n\\[1\\] 3DCoMPaT^{++}: An improved Large-scale 3D Vision Dataset for Compositional Recognition\n\n**If you have any further question, please let us know. Thank you very much!**"}, {"review_id": "2iz0xW0Vep", "reviewer": "Reviewer_v5oZ", "summary": "This paper proposes SegGraph, a method for few-shot 3D part segmentation that leverages 2D foundation models and a graph-based segmentation module. The approach begins by rendering multi-view images of a 3D point cloud shape and extracting segmentation masks using SAM, along with visual features from DINOv2. The 2D segments are collected across all rendered views from SAM. An embedding is learnt using the local and semantic features, which forms nodes in a segment graph. The graph captures both overlapping and adjacent relationships between segments and is processed using a GATv2 network. These features are then reweighted via a view quality-aware mechanism and unpooled back to individual 3D points for final part segmentation. The model is trained in a few-shot setting and evaluated on two benchmark datasets, demonstrating strong performance, especially on small parts.", "questions": "Questions (considered for score change):\n1. Baseline and foundation model justification: Could the authors provide results or justification for using consistent baselines across all datasets, and for isolating gains from the choice of foundation model?\n2.  Significance of thresholds used for graph edges: The paper defines overlapping segments as those with mIoU greater than 10%, and adjacent segments as those whose closest points are less than 0.01 units apart in normalized space. Could the authors justify these thresholds and provide an analysis of how sensitive the model is to these values?\n\nClarifying questions:\n1. What is the number of shapes per category in the few-shot experiments? Reporting the range (e.g., min/max examples per class) would help interpret the model's generalization performance across both frequent and rare classes.\n2. The paper highlights significant improvements on small parts, but does not define how \"small\" is quantified. Are small parts determined based on point count, spatial volume, or proportion of the object? \n3. Clarification of “w/o SAM” setting in ablation: In Table 4, the model is evaluated without SAM. How are 3D segments generated in this setting, and how is the pipeline modified to function without SAM-derived masks?", "limitations": "While the method performs well overall, there is little to no qualitative or quantitative discussion of where it fails (e.g., occluded parts, inconsistent multi-view alignment).", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "### Strengths\n\n1. Clarity: The paper is well-written and clearly structured. It presents a well-motivated approach, thoroughly discusses prior work, and explains its contributions clearly.\n2. Empirical results: The method demonstrates strong performance in the few-shot 3D part segmentation setting on both PartNet-Ensemble and ShapeNetPart datasets.\n3. View Quality-Aware Unpooling: It is a novel contribution and can be useful for other tasks as well.\n4. Robustness of results: The authors report mean and standard deviation over multiple training runs, highlighting the method’s stability and reproducibility. It's a good experimental practice.\n\n### Weaknesses:\n\n1. Ambiguity in key definitions and notation: The paper occasionally introduces parameters without a formal definition. For example, the mathematical representation of segment nodes $V$ and $F^{p^{'}}_i$ used in equation 3. Clearer definitions would improve readability. Adding equations to explain L148-153 mathematically will also improve clarity. \n\n2. Certain technical statements are imprecise. For instance, SATR does not use a voting-based aggregation scheme as implied in L31–34. Similarly, referring PartField as a zero-shot method (L205) is confusing as it requires part-level supervision during training. \n\n3. Writing clarity and specificity: Some claims lack necessary specificity. For example, the phrase “an improvement of at least 4%” in L69 does not clarify the metric, baseline, or dataset. The abstract also omits the fact that the method operates on 3D point clouds. Additionally, Equation 1 would benefit from a clarifying sentence noting that the denominator is computed per axis and that the normalisation is element-wise.\n\n4. Baseline and choice of foundation model: Different baselines are reported on different datasets. The authors can either explain these omissions (for the models which have public code that can be applied to untextured shapes) or provide consistent baselines across datasets to allow for fairer comparisons. In Table 1, different methods use different foundation models (e.g., GLIP, DINOv2, SAM), which complicates the interpretation of performance gains. If feasible, providing comparisons with a fixed foundation model would more clearly isolate the contribution of the proposed 3D pipeline.\n\n[Optional] Figure 2: The figure is general and does not precisely capture the complexity of the pipeline.\n\n[Minor] Line 59: “all views” is ambiguous—suggest replacing with “all training views,” as the number of possible renderings is theoretically unlimited. Figure 3 shows adjacency (solid lines) only within the same view, but the text does not clarify if the adjacency edges can exist across views as well.", "was_revised": true, "final_justification": "The paper provides valuable contribution in the setting of 3D part segmentation. It is well written, it covers recent baselines and provides a thorough comparison. During the initial review there were concerns about where the gains in the pipeline were coming from, inadequate discussion on the failure cases / setting with complex shapes, and the time cost analysis. The authors engaged during the rebuttal to clarify these concerns and therefore, I have increased my rating to Accept.", "rebuttal": "Thank you for your valuable comments and kind words to our work. Below we address specific questions.\n\n**Q1: Baseline and foundation model justification: Could the authors provide results or justification for using consistent baselines across all datasets, and for isolating gains from the choice of foundation model?**\n\n**ANS:** We select DINOv2 as the base foundation model due to its good generalization and high-quality output features, achieved through large-scale self-supervised learning. Most baseline methods rely on foundation models with 2D label output, e.g., GLIP, and do not support the use of DINOv2. Moreover, the training codes of  PartSLIP, PartSLIP++, PartSTAD, 3-By-2, and PartDistill are not fully available, making it difficult to change their base foundation model for alignment.\n\nTo isolate the influence of foundation models, we evaluate the results of using GLIP as the 2D feature extractor in place of DINOv2 for our method. Specifically, we used the multi-scale visual features from GLIP after fusion with text features, and employed a Feature Pyramid Network (FPN) structure to further fuse these multi-scale features. The resulting fused features were then used as the GLIP features for SegGraph aggregation. However, we need to notice that this design is inferior as it ignores the most important bounding box outputs. The results in the table below show that even when using GLIP, our method still achieves competitive performance, second only to our results using DINOv2.\n\n| Method | PartSLIP | PartSLIP++ | PartSTAD | PartDistill | 3-By-2 | Ours-DINOv2 | Ours-GLIP |\n| :----- | :------- | :--------- | :------- | :---------- | :----- | :---------- | :-------- |\n| mIoU   | 59.4     | 62.1       | 65.0     | 65.9        | 64.2   | 72.8        | 70.4      |\n\n**Q2: Significance of thresholds used for graph edges**  \n**ANS:**  We validated the influence of these thresholds in Figures A2 and A3 in the supplementary material. Basically, the method is quite robust to these hyperparameters owing to the good quality of SAM segments. \n\n**Q3: What is the number of shapes per category in the few-shot experiments? Reporting the range (e.g., min/max examples per class) would help interpret the model's generalization performance across both frequent and rare classes.**  \n**ANS:** Thank you for pointing out the omission regarding the number of shots used in our experiments. All experiments in the paper were conducted using an 8-shot per shape configuration. Additionally, we provide an analysis of the model’s generalization performance under different numbers of shots in Appendix C and Figure A1.\n\n**Q4：The paper highlights significant improvements on small parts, but does not define how \"small\" is quantified. Are small parts determined based on point count, spatial volume, or proportion of the object?**  \n**ANS:** For the categorization of \"small parts,\" we follow the approach used in PartSTAD, which defines small parts based on empirical knowledge of object part sizes. In the PartNetE dataset, the following categories are considered small parts: \\[\"handle\", \"button\", \"wheel\", \"knob\", \"switch\", \"bulb\", \"shaft\", \"touchpad\", \"camera\", \"screw\", \"handlebar\", \"trigger\"\\].\n\n**Q5: Clarification of “w/o SAM” setting in ablation: In Table 4?**  \n**ANS:** We describe the configuration without using SAM in Lines 253–257. In this setting, we first aggregate multi-view features into the 3D space via average pooling to obtain $F^p$, and then directly apply an MLP to predict the category of each point. The graph structure and SAM segments are completely discarded in this configuration.\n\n**Q6: While the method performs well overall, there is little to no qualitative or quantitative discussion of where it fails (e.g., occluded parts, inconsistent multi-view alignment).**\n\n**ANS:** We expect the method will fail on shapes with deep concave parts and inner structures because of occlusions. We acknowledge this is a fundamental limitation of multi-view rendering-based methods.\n\n**Q7: Writing and clarity**  \n**ANS:** Thank you for your valuable suggestions regarding the writing and clarity of the paper. We will revise the relevant sections to improve the overall clarity. More explanations are as follows. \n\n1. **SATR does not use a voting-based aggregation scheme as implied in L31–34. Similarly, referring PartField as a zero-shot method (L205) is confusing as it requires part-level supervision during training.**  \n   The Per Face Score Aggregation module in SATR can be described as follows. Each 3D face receives initial votes from multiple 2D views via a 2D detector. These votes are then adjusted through a weighted refinement process based on 3D topology (geodesic distance) and neighborhood information (visibility). The final aggregated score determines the label of each face based on the highest vote score. Therefore, the overall approach still aligns with the description of \"aggregating 2D labels to the 3D domain with voting\".  \n   \n   Regarding PartField, we acknowledge that our original description was not fully accurate. PartField indeed requires training on data from Objaverse and PartNet with SAM-generated pseudo labels and contrastive losses. Though the method can work without ground truth annotations, referring to it as a zero-shot method is not entirely precise, as it still requires training.  \n2. **The phrase “an improvement of at least 4%” in L69 does not clarify the metric, baseline, or dataset:**   \n   The reported \"at least 4% improvement\"  in L69 refers specifically to the performance on the PartNet-E dataset. It indicates that, under various configurations using different foundation models as image feature extractors, our method consistently yields at least a 4% improvement compared to not using SegGraph. We will actively integrate the suggestions into our revision.  \n3. **The mathematical representation of segment nodes $V$ and $F_i^{p'}$ in equation 3\\. Adding equations to explain L148-153 mathematically will also improve clarity.**  \n   Thank you. We will clarify these two parts.   \n4. **Figure 2: The figure is general and does not precisely capture the complexity of the pipeline.**  \n   Thank you. We will incorporate the illustration for graph construction and view pooling in the revision.  \n5. **Figure 3 shows adjacency (solid lines) only within the same view, but the text does not clarify if the adjacency edges can exist across views as well.**  \n   Thank you for the suggestion. We will add cross-view adjacency edges in Figure 2 to better illustrate the adjacency relationships across views.\n\n**If you have any further question, please let us know. Thank you very much!**"}, {"review_id": "zMqJdU5AHK", "reviewer": "Reviewer_epFc", "summary": "The paper tackles the task of few-shot 3D part segmentation through the approach of 2D-3D knowledge transfer. The paper proposes SegGraph. It constructs a graph, with nodes as 3D segments and edges as 3D segments spatial relationship (overlap or adjacency). It refines the back-projected DINOv2 feature through a Graph Attention Network and conducts \"viewing quality-aware feature unpooling\" to reweigh features from each view. The idea is to break SAM segmented masks into independent pieces and reconnect them through a graph structure. Experiments show that the number and visual quality outperform the SOTA.", "questions": "* The method was mainly evaluated on clean datasets with reliable geometry and colors. How does the method perform for real-world data? What about its performance on raw and noisy point clouds from real world. For example, the algorithm reconstructed 3D objects, 3D assets represented by 3DGS.  Can the graph be correctly built and maintain the performance?  \n\n* Concerns of performance contributions. See weakness #2\n\n* How does the method compare with semantic mask-based approaches? They may not focus on the same task in their papers, but exhibit capabilities in SegGraph's tackled task. Some of them even have performance advantages. \n\nRefs: \n[1] OpenMask3D: Open-Vocabulary 3D Instance Segmentation. NeurIPS 2023. [2] SATR: Zero-Shot Semantic Segmentation of 3D Shapes. ICCV 2023. [3] Open-Vocabulary Semantic Part Segmentation of 3D Human. 3DV 2025. [4] Find Any Part in 3D. Arxiv in 2024.", "limitations": "yes", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths\n* The paper has valid motivation: to improve the 2D-3D knowledge transfer. \n* Clear writing. The method is easy to follow, and the experiments done have comprehensive results provided. \n* Visualization looks promising. The experiment table shows performance improvements over SOTA, such as Partslip and Partslip++. \n\nWeaknesses\n* As stated, the granularity problem. Currently, it operates at a fixed granularity scale. The dataset is also labeled on a consistent scale. If the few-shot examples are under various granularity scales, the performance of the graph formulation is questionable. \n* The overall performance increase may be attributed to the application of powerful DINOv2 rather than the proposed method. Tab.5 shows the efficacy of SegGraph, but also shows that when replacing it with CLIP, the SegGraph only has 49.5 mIOU, lower than most of GLIP+SAM methods in Tab.1, which is basically CLIP+SAM. \n* The proposed method seems not be able to handle the semantically different but geometrically similar parts.", "was_revised": true, "final_justification": "The rebuttal addresses most of my concerns. Please incorporate these additional evaluations (& anything incomplete due to time constraint of rebuttal) in the final version.", "rebuttal": "Thank you for your valuable comments and kind words to our work. Below we address specific questions.\n\n**Q1： If the few-shot examples are under various granularity scales, the performance of the graph formulation is questionable.**  \n**ANS:** Our method can be extended to multi-granularity once the annotation is available. For example, 3DComPat++ \\[1\\]  provides coarse-grained annotations (at most 5 part categories) and fine-grained annotations (10-20 part categories). Due to limitations in time and computational resources, we selected three categories—**airplane**, **bicycle**, and **chair**—for evaluation. Airplanes have 14 fine-grained and 3 coarse part labels; bicycles have 26 fine-grained ones and 3 coarse ones; chairs have 22 fine-grained labels and 4 coarse labels.  \nWe extend to multi-grained segmentation by tuning separate networks. In the table below, our method demonstrates significant advantages across different levels of part annotation granularity.\n\n|                       |  Coarse  | Coarse  | Coarse |   Fine   |  Fine   | Fine  |\n| :------ | :------ | :------ | :------ | :------ | :------ | :------ |\n|                       | airplane | bicycle | chair  | airplane | bicycle | chair |\n| PartSLIP++(zero-shot) |  11.56   |  60.52  | 16.59  |   9.48   |  2.60   | 4.87  |\n| Ours(1-shot)          |  93.31   |  91.21  | 41.41  |  45.34   |  26.26  | 27.21 |\n| Ours(8-shot)          |  94.64   |  95.46  | 69.26  |  65.38   |  54.17  | 42.61 |\n\n**Q2： The overall performance increase may be attributed to the application of powerful DINOv2 rather than the proposed method. Tab.5 shows the efficacy of SegGraph, but also shows that when replacing it with CLIP, the SegGraph only has 49.5 mIOU, lower than most of GLIP+SAM methods in Tab.1, which is basically CLIP+SAM.**  \n**ANS:** We would like to point out that GLIP is generally more powerful than CLIP. CLIP primarily focuses on global semantic features at the image level and falls short in localization tasks, such as detections and part segmentation, as illustrated in Figure 5 of the paper \"*Foundational Models Defining a New Era in Vision: A Survey and Outlook\".* In contrast, GLIP is designed for object detection and is trained using large-scale detection datasets. Therefore, GLIP demonstrates advantages in fine-grained localization and facilitates much better part localization than CLIP, leading the performance gaps when using different backbone features.\n\nTo isolate the influence of foundation models, we evaluate the results of using GLIP as the 2D feature extractor in place of DINOv2 on the PartNetE dataset. Specifically, we used the multi-scale visual features from GLIP after fusion with text features, and employed a Feature Pyramid Network (FPN) structure to further fuse these multi-scale features. The resulting fused features were then used as the GLIP features for SegGraph aggregation. However, we need to notice that this design is inferior as it ignores the bounding box outputs. The results in the table below show that even when using GLIP, our method still achieves competitive performance, second only to our results using DINOv2. This demonstrates that the overall performance increase is not solely attributed to the application of the powerful DINOv2.\n\n| Method | PartSLIP | PartSLIP++ | PartSTAD | PartDistill | 3-By-2 | Ours-DINOv2 | Ours-GLIP |\n| :----- | :--------- | :--------- | :--------- | :--------- | :--------- | :--------- | :--------- |\n| mIoU   | 59.4     | 62.1       | 65.0     | 65.9        | 64.2   | 72.8        | 70.4      |\n\n**Q3： The proposed method seems not be able to handle the semantically different but geometrically similar parts.**  \n**ANS**： Thanks to the semantically rich features extracted from DINOv2, our method is capable of segmenting parts that share similar geometric structures but differ in semantic meaning. For example, the screen and the base of a laptop are both rectangular and flat in shape, exhibiting high similarity in 3D geometry. However, as shown in Figure 6, visualizations of their features reveal that they are highly distinguishable in the feature space.\n\n**Q4：How does the method perform for real-world data? What about its performance on raw and noisy point clouds from real world. For example, the algorithm reconstructed 3D objects, 3D assets represented by 3DGS. Can the graph be correctly built and maintain the performance?**  \n**ANS**： To verify the robustness of our method to noise, we conducted experiments by adding noise in the normalized point cloud space. We evaluate the method against added noises with a standard deviation of {0.005, 0.01, 0.02，0.05}. Extending the method to 3DGS requires substantially changing the pipeline, and we leave it to future work.\n\n|            |  0   | 0.005 | 0.01  | 0.02  | 0.05  |\n| :--------- | :--- | :--- | :--- | :--- | :--- |\n| KitchenPot | 85.2 | 81.83 | 80.29 | 75.01 | 66.68 |\n| Bottle     | 90.0 | 83.98 | 78.99 | 74.88 | 69.04 |\n| Chair      | 89.5 | 87.03 | 82.34 | 78.02 | 67.58 |\n\n**Q5：How does the method compare with semantic mask-based approaches? They may not focus on the same task in their papers, but exhibit capabilities in SegGraph's tackled task. Some of them even have performance advantages.**  \n**ANS：** The following table provides a comparison with the methods you mentioned—OpenMask3D, SATR, and Find Any Part in 3D (Find3D). The method \"Open-Vocabulary Semantic Part Segmentation of 3D Human\" is not included in the comparison because its code is not publicly available. Among these, our approach consistently demonstrates a significant performance advantage. One important factor is that OpenMask3D relies on Mask3D as its 3D feature extractor, which is pre-trained on ScanNet—an indoor scene dataset. This domain gap makes it difficult for OpenMask3D to generalize well to the more diverse and structurally complex 3D shapes in PartNetE. Moreover, as mentioned in the Find3D paper, PartNetE is particularly challenging due to the presence of many small parts that lack distinct geometric or color features (e.g., buttons on a surface of the same color), which significantly hinders the performance of Find3D and SATR. In contrast, our method excels in identifying and segmenting such small, indistinct components, which underscores its robustness in fine-grained part segmentation. \n\n| Setting   | Method         | mIoU   |\n|:---------|:--------------|:------|\n| Zero-shot | OpenMask3D     | 12.54  |\n| Zero-shot | Find3D         | 16.38  |\n| Zero-shot | SATR           | 19.42  |\n| Few-shot  | ours (1-shot)  | 53.36  |\n| Few-shot  | ours (8-shots) | 72.80  |\n\n\n\\[1\\] 3DCoMPaT^{++}: An improved Large-scale 3D Vision Dataset for Compositional Recognition\n\n**If you have any further question, please let us know. Thank you very much!**"}, {"review_id": "lAJ2wBHuFl", "reviewer": "Reviewer_hPPy", "summary": "This paper presents a segment graph for few-shot 3D part segmentation. It encodes 3D point features through multi-view feature pooling from 2D foundation model outputs. Simultaneously, they generate view-consistent 3D segments by aggregating SAM-based 2D segmentations across all views. They then model both the spatial relationships (overlapping/adjacent) between segments and the constituent relationships between points and segments. The 3D point features are combined with segment features, which are sent to MLP for part prediction.", "questions": "1. Is the graph constructed from the segments of multi-views? How large is the graph? How many nodes and edges? \n2. How to compute point normals at eqn 2?", "limitations": "see weakness.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strength:\n1. figure 1 clearly show the difference between theirs and existing methods. The segment graph is novel to me.\n2. achive sota performance.\n3. extensive experiments.\n\nweakness:\n1. Some part is not clear to me, such as line 148-153. There are several different aggregation methods. Can you explain why?\n2. I guess the optimization of the graph is time consuming. Can you provide a optimization time comparison with other models?", "was_revised": true, "final_justification": "The rebuttal is good. I keep my rating positive.", "rebuttal": "Thank you for your valuable comments and kind words to our work. Below we address specific questions.\n\n**Q1: Some part is not clear to me, such as line 148-153. There are several different aggregation methods. Can you explain why?**  \n**ANS:** The use of different aggregation methods in lines 148–153 is intentional and serves distinct yet complementary purposes. The first aggregation (line 150) applies max pooling to encode segment features. However, simple max-pooling ignores the varying contributions of different points within a segment. For example, points close to boundaries are usually more inconsistent with the segment label and should be downweighted. \n\nTo address this problem, the second aggregation (line 152) refines the segment representation by performing a weighted aggregation of point-level DINOv2 features. This weighting mechanism leverages the pooled segment feature to estimate the importance of each point, allowing the model to downweight noisy or ambiguous points—particularly those near segment boundaries that may partially belong to other categories. This design enhances the robustness of segment-level semantic features by mitigating boundary noises and improving within-segment consistency.\n\n**Q2: I guess the optimization of the graph is time consuming. Can you provide a optimization time comparison with other models?**  \n**ANS**： The optimization of the graph is quite efficient. In the table below, we report the optimization time for one epoch, the inference time for a single shape, and the inference time of PartSLIP++. Our method is more efficient than PartSLIP++. Since the training code of PartSLIP++ is not publicly available, we do not report its training optimization time. Nevertheless, the one epoch training time required for our graph optimization is still smaller than the inference time of PartSLIP++.    \nThe preprocess time for the two methods: \n\n| Method     | Render (s) | GLIP+SAM / SAM (s) | Voting Preprocess / Build Graph (s) | Total (s) |\n| :--------- | :--------- | :----------------- | :---------------------------------- | :-------- |\n| PartSLIP++ | 2.12       | 16.33 (GLIP+SAM)   | 92.38 (Voting Preprocess)           | 110.30    |\n| Ours       | 2.12       | 52.40 (SAM)        | 10.40 (Build Graph)                 | 64.92     |\n\nThe training and inference time:\n\n| Method     | Training (s) | Inference (s) |\n| :--------- | :----------- | :------------ |\n| PartSLIP++ | —            | 5.83          |\n| Ours       | 2.25         | 1.46          |\n\n**Q3: Is the graph constructed from the segments of multi-views? How large is the graph? How many nodes and edges?**  \n**ANS**: Yes, the nodes in our graph are directly formed from the segments obtained by applying SAM to multi-view images. To quantitatively illustrate the size of the constructed graphs, we report the average and maximum number of nodes, adjacent edges, and overlapping edges per object on the PartNetE dataset.\n\n|      | Nodes  | AdjEdges | OvlpEdges |\n| :--- | :----- | :------- | :-------- |\n| mean | 136.53 | 310.58   | 2386.84   |\n| max  | 1121   | 1872     | 21024     |\n\nAs shown in the table, the constructed graph (with an average of a few hundred nodes and several thousand edges) is significantly smaller compared to the point clouds of each object in PartNetE, which typically contain hundreds of thousands of points.\n\n**Q4: How to compute point normals at eqn 2?**  \n**ANS**： We estimate the point cloud normals as follows. For each point, we consider a local neighborhood within a radius of 0.02 and employ the built-in function in Open3D to estimate the normals. Specifically, Open3D performs a PCA analysis on the local neighborhood of each point, and the normal vector is taken as the eigenvector corresponding to the smallest eigenvalue of the covariance matrix. Optionally, Open3D applies normal orientation adjustment to ensure consistency in the normal directions.\n\n**If you have any further question, please let us know. Thank you very much!**"}]

Author rebuttal:

Meta-review: This paper studies the few-shot 3D part segmentation problem. In the paper, the authors propose a SAM segment graph-based propagation method to explicitly learn geometric features encoded within SAM's segmentation masks to achieve few-shot 3D part segmentation. It was reviewed by four expert reviewers, and all of them recommended accepting this paper after the rebuttal period. Therefore, it is a clear acceptance.
Decision: Accept (poster)