mlsanityData to 7 Oct 2026

DMF-Net: Image-Guided Point Cloud Completion with Dual-Channel Modality Fusion and Shape-Aware Upsampling Transformer

arXiv 2406.17319 ↗First posted 25 Jun 2024cs.CVcited by 5

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Abstract (arXiv)

In this paper we study the task of a single-view image-guided point cloud completion. Existing methods have got promising results by fusing the information of image into point cloud explicitly or implicitly. However, given that the image has global shape information and the partial point cloud has rich local details, We believe that both modalities need to be given equal attention when performing modality fusion. To this end, we propose a novel dual-channel modality fusion network for image-guided point cloud completion(named DMF-Net), in a coarse-to-fine manner. In the first stage, DMF-Net takes a partial point cloud and corresponding image as input to recover a coarse point cloud. In the second stage, the coarse point cloud will be upsampled twice with shape-aware upsampling transformer to get the dense and complete point cloud. Extensive quantitative and qualitative experimental results show that DMF-Net outperforms the state-of-the-art unimodal and multimodal point cloud completion works on ShapeNet-ViPC dataset.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 30 of its 53 references that are in the map. First posted on arXiv 25 Jun 2024.

Builds on

30 of 53 references.

References · 30

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18 more
  1. 2022ULIP: Learning a Unified Representation of Language, Images, and Point Clouds for 3D Understandingcited by 262
  2. 2016Shape Completion using 3D-Encoder-Predictor CNNs and Shape Synthesiscited by 219
  3. 20183D-SIS: 3D Semantic Instance Segmentation of RGB-D Scanscited by 216
  4. 2018PU-Net: Point Cloud Upsampling Networkcited by 208
  5. 2020GRNet: Gridding Residual Network for Dense Point Cloud Completioncited by 174
  6. 2021SnowflakeNet: Point Cloud Completion by Snowflake Point Deconvolution with Skip-Transformercited by 162
  7. 2020PF-Net: Point Fractal Network for 3D Point Cloud Completioncited by 146
  8. 2019Morphing and Sampling Network for Dense Point Cloud Completioncited by 132
  9. 2020Cascaded Refinement Network for Point Cloud Completioncited by 112
  10. 2020Deep Learning for Image and Point Cloud Fusion in Autonomous Driving: A Reviewcited by 107
  11. 2021Variational Relational Point Completion Networkcited by 103
  12. 2020PMP-Net: Point Cloud Completion by Learning Multi-step Point Moving Pathscited by 97
  13. 2017High-Resolution Shape Completion Using Deep Neural Networks for Global Structure and Local Geometry Inferencecited by 87
  14. 2022SeedFormer: Patch Seeds based Point Cloud Completion with Upsample Transformercited by 83
  15. 2022Cross-modal Learning for Image-Guided Point Cloud Shape Completioncited by 36
  16. 2019Dense 3D Point Cloud Reconstruction Using a Deep Pyramid Networkcited by 33
  17. 2021PU-Flow: a Point Cloud Upsampling Network with Normalizing Flowscited by 17
  18. 2023Fine-grained Text and Image Guided Point Cloud Completion with CLIP Modelcited by 5

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 3; 2026: 2.

5 papers.

  1. 2025Diffusion Models are Open-World Affordance Learners: Leveraging Generative Priors for 3D Affordance Learningcited by 5
  2. 2025Rethinking Multimodal Point Cloud Completion: A Completion-by-Correction Perspectivecited by 4
  3. 2026MGPC: Multimodal Network for Generalizable Point Cloud Completion With Modality Dropout and Progressive Decodingcited by 2
  4. 2025A Strong View-Free Baseline Approach for Single-View Image Guided Point Cloud Completioncited by 0
  5. 2026UBone3D: Physics-Rectified Conditional Flow Matching for Anatomical 3D Shape Completion from Ultrasoundcited by 0