Existing point cloud completion methods struggle to balance high-quality reconstruction with computational efficiency. To address this, we propose PPC-MT, a novel parallel framework for point cloud completion leveraging a hybrid Mamba-Transformer architecture. Our approach introduces an innovative parallel completion strategy guided by Principal Component Analysis (PCA), which imposes a geometrically meaningful structure on unordered point clouds, transforming them into ordered sets and decomposing them into multiple subsets. These subsets are reconstructed in parallel using a multi-head reconstructor. This structured parallel synthesis paradigm significantly enhances the uniformity of point distribution and detail fidelity, while preserving computational efficiency. By integrating Mamba's linear complexity for efficient feature extraction during encoding with the Transformer's capability to model fine-grained multi-sequence relationships during decoding, PPC-MT effectively balances efficiency and reconstruction accuracy. Extensive quantitative and qualitative experiments on benchmark datasets, including PCN, ShapeNet-55/34, and KITTI, demonstrate that PPC-MT outperforms state-of-the-art methods across multiple metrics, validating the efficacy of our proposed framework.
Figures & tables
Fig. 1: Illustration of PCA-Guided Decomposition. Target point clouds are first sorted according to Principal Component Analysis (PCA) and then uniformly decomposed into several structured segments.
Fig. 2: The PPCMT framework consists of two primary components: (a) Predicted Point Cloud Generation and (b) Target Point Cloud Decomposition. The Predicted Point Cloud Generation component includes multiple modules, three of which are illustrated in this figure: (c) Feature Extractor, (d) Seed Generator, and (e) Multi-Head Reconstructor.
Fig. 3: The detailed structure of the Mamba Encoder.
Fig. 4: The detailed structure of the Transformer Decoder.
Method
CD- ℓ1↓
DCD ↓
EMD ↓
F1 ↑
FoldingNet [ 6 ]
14.31
-
-
-
TopNet [ 7 ]
12.15
-
-
-
AtlasNet [ 9 ]
10.85
-
-
0.616
CRN [ 12 ]
8.51
-
-
0.652
PCN [ 10 ]
9.64
-
-
0.695
GRNet [ 19 ]
8.83
0.622
-
0.708
TABLE I: Results on PCN dataset. (CD- ℓ1×103 , EMD ×103 and F1-Score@1%)
Fig. 5: Qualitative comparison of PCN dataset. All methods above take the point clouds in the first column as inputs. We highlight some regions with red bounding box, which clearly show the effectiveness of our method.
Method
CD- ℓ1↓
CD- ℓ2↓
DCD ↓
EMD ↓
F1(mCls) ↑
F1 (Ova) ↑
FoldingNet [ 6 ]
-
3.12
0.082
-
-
0.082
TopNet [ 7 ]
-
2.91
0.126
-
-
0.126
PCN [ 10 ]
-
2.66
0.618
-
-
0.133
GRNet [ 19 ]
-
1.97
0.592
-
-
0.238
PoinTr [ 20 ]
13.95
1.05
0.539
38.86
0.468
0.499
SeedFormer [ 22 ]
13.97
0.92
0.552
37.09
0.444
0.473
TABLE II: Results on ShapeNet55. All reported results are averaged across three difficulty levels. (CD- ℓ1×103 , CD- ℓ2×103 , EMD ×103 and F1-Score@1%)
Method
34 seen categories
21 unseen categories
CD- ℓ1↓
CD- ℓ2↓
DCD ↓
EMD ↓
F1 ↑
CD- ℓ1↓
CD- ℓ2↓
DCD ↓
EMD ↓
F1 (mCls) ↑
F1 (0va) ↑
FoldingNet [ 6 ]
-
2.35
-
-
0.139
-
3.62
-
-
-
0.095
TopNet [ 7 ]
-
2.31
-
-
0.171
-
3.50
-
-
-
0.121
PCN [ 10 ]
-
2.22
0.624
-
0.154
-
3.85
0.644
-
-
0.101
GRNet [ 19 ]
-
1.74
0.600
-
0.251
-
2.99
0.625
-
-
0.216
PoinTr [ 20 ]
15.07
1.09
0.604
42.35
0.402
17.64
1.75
0.629
46.30
0.370
0.360
TABLE III: Results on ShapeNet34. All reported results are averaged across three difficulty levels. (CD- ℓ1×103 , CD- ℓ2×103 , EMD ×103 and F1-Score@1%)
Fig. 6: Qualitative comparison of ShapeNet55. All methods above take the point clouds in the first column as inputs. We highlight some regions with red bounding box, which clearly show the effectiveness of our method.
We present an image-conditioned point cloud completion approach that treats images as the primary geometric source rather than a secondary guide. To this end, we introduce an Image-to-Point (I2P) module that can reconstruct complete point clouds directly from a single RGB image, with no need for 3D inputs. Additionally, we introduce a transformer-based Point-to-Point (P2P) refinement module that uses self- and cross-attention between point tokens and image features to iteratively refine the coarse I2P output. The I2P module enables the image encoder to learn rich geometric representations, while the P2P module progressively recovers fine-grained details. Unlike existing multimodal methods that rely on auxiliary losses or fusion modules, our explicit I2P task provides a strong, geometry-aware prior based on images alone. Extensive experiments on ShapeNet-ViPC demonstrate state-of-the-art completion performance with a 12.3% relative Chamfer Distance improvement over prior methods. Code is available at: https://github.com/AzharSindhi/I2PRef.git
Point cloud completion commonly follows a coarse-to-fine paradigm, where a low-density coarse shape is first predicted and then upsampled to the target resolution. Although recent methods have improved global structure recovery, the fine stage often remains limited by simple upsampling and insufficient interaction with coarse structural features, making local detail reconstruction challenging. We propose SUMI, a diffusion-enhanced refinement module for coarse-to-fine point cloud completion. Unlike prior diffusion-based completion methods that use diffusion as a standalone point generator, SUMI injects noisy geometric features into cross-attention with coarse structural features, enabling reverse denoising to refine local geometry while preserving global consistency. SUMI can also be integrated into existing coarse-to-fine models as a flexible refinement module. Experiments on PCN, ShapeNet-55/34, and MVP demonstrate consistent improvements over strong baselines. SUMI achieves the best overall CD and F1-score on PCN, reduces CD by up to 16.1% on ShapeNet-55, and obtains the best CD across all output densities on MVP.
Medical point cloud completion is important for anatomical reconstruction and downstream clinical workflows, yet generative modeling in this setting remains insufficiently studied. We investigate completion through continuous-time generative modeling and introduce PCFM, a PTv3-backed flow matching approach for medical point cloud completion. We evaluate on SkullFix and SkullBreak, and additionally on the more recent Mandibular Defect dataset. We build strong baselines by adapting PTv3 to a deterministic encoder-decoder completion model and by instantiating diffusion completion (PCDiff) with both PVCNN and PTv3 denoisers. PCFM with PTv3 is competitive with the deterministic PTv3 baseline and achieves state-of-the-art generative performance across datasets, while requiring substantially fewer sampling steps than diffusion. At the best operating points, PTv3 also yields clear throughput gains, providing up to a 7× speed-up for PCFM compared to a PVCNN backbone. Finally, we study empirical scaling trends by varying model size and point cardinality, showing consistent gains with higher point resolution and informative trade-offs across model scales.
Kamil Kwarciak, Marek Wodzinski
Department of Measurement and Electronics, AGH University of Krakow, Krakow, Poland · Sano Centre for Computational Medicine, Krakow, Poland