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SG-GAN: Fine Stereoscopic-Aware Generation for 3D Brain Point Cloud Up-sampling from a Single Image

arXiv 2305.12646 ↗First posted 22 May 2023eess.IV cs.CVcited by 0

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Abstract (arXiv)

In minimally-invasive brain surgeries with indirect and narrow operating environments, 3D brain reconstruction is crucial. However, as requirements of accuracy for some new minimally-invasive surgeries (such as brain-computer interface surgery) are higher and higher, the outputs of conventional 3D reconstruction, such as point cloud (PC), are facing the challenges that sample points are too sparse and the precision is insufficient. On the other hand, there is a scarcity of high-density point cloud datasets, which makes it challenging to train models for direct reconstruction of high-density brain point clouds. In this work, a novel model named stereoscopic-aware graph generative adversarial network (SG-GAN) with two stages is proposed to generate fine high-density PC conditioned on a single image. The Stage-I GAN sketches the primitive shape and basic structure of the organ based on the given image, yielding Stage-I point clouds. The Stage-II GAN takes the results from Stage-I and generates high-density point clouds with detailed features. The Stage-II GAN is capable of correcting defects and restoring the detailed features of the region of interest (ROI) through the up-sampling process. Furthermore, a parameter-free-attention-based free-transforming module is developed to learn the efficient features of input, while upholding a promising performance. Comparing with the existing methods, the SG-GAN model shows superior performance in terms of visual quality, objective measurements, and performance in classification, as demonstrated by comprehensive results measured by several evaluation metrics including PC-to-PC error and Chamfer distance.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 30 of its 61 references that are in the map. First posted on arXiv 22 May 2023.

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30 of 61 references.

References · 30

  1. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,223
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18 more
  1. 2018PU-Net: Point Cloud Upsampling Networkcited by 208
  2. 20193D Point Cloud Generative Adversarial Network Based on Tree Structured Graph Convolutionscited by 173
  3. 2019PU-GAN: a Point Cloud Upsampling Adversarial Networkcited by 162
  4. 2015Deep Sliding Shapes for Amodal 3D Object Detection in RGB-D Imagescited by 111
  5. 2018Patch-based Progressive 3D Point Set Upsamplingcited by 101
  6. 2019PU-GCN: Point Cloud Upsampling using Graph Convolutional Networkscited by 100
  7. 2020Bidirectional Mapping Generative Adversarial Networks for Brain MR to PET Synthesiscited by 38
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  10. 2020Tensorizing GAN with High-Order Pooling for Alzheimer's Disease Assessmentcited by 21
  11. 2020Improving Semantic Analysis on Point Clouds via Auxiliary Supervision of Local Geometric Priorscited by 11
  12. 2019Parts4Feature: Learning 3D Global Features from Generally Semantic Parts in Multiple Viewscited by 10
  13. 20213D Brain Reconstruction by Hierarchical Shape-Perception Network from a Single Incomplete Imagecited by 10
  14. 2019One-stage Shape Instantiation from a Single 2D Image to 3D Point Cloudcited by 9
  15. 2021A Point Cloud Generative Model via Tree-Structured Graph Convolutions for 3D Brain Shape Reconstructioncited by 8
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  17. 2019Probabilistic Point Cloud Reconstructions for Vertebral Shape Analysiscited by 5
  18. 2019Instantiation-Net: 3D Mesh Reconstruction from Single 2D Image for Right Ventriclecited by 3

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