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Updated: May 21, 2025

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PAPRec: 3D Point Cloud Reconstruction Based on Prior-Guided Adaptive Probabilistic Network.

Caixia Liu1, Minhong Zhu1, Yali Chen1

  • 1Beijing Key Laboratory of Big Data Technology for Food Safety, School of Computer and Artificial Intelligence, Beijing Technology and Business University, No.33, Fucheng Road, Haidian District, Beijing 100048, China.

Sensors (Basel, Switzerland)
|March 17, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces PAPRec, a novel network for 3D shape reconstruction from single images. PAPRec significantly improves accuracy by integrating 3D prior guidance and an adaptive probabilistic network.

Keywords:
3D reconstructionadaptive probabilistic modelprior featuresingle-view image

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Area of Science:

  • Computer Vision
  • 3D Reconstruction
  • Deep Learning

Background:

  • Single-view 3D shape reconstruction is challenging due to inherent ambiguity and ill-posed nature.
  • Existing methods struggle with feature expression, training stability, and limited constraints, leading to inaccurate and ambiguous results.

Purpose of the Study:

  • To develop a robust and accurate method for inferring complete 3D shapes from single-view images.
  • To overcome limitations of current approaches by incorporating 3D prior knowledge and probabilistic modeling.

Main Methods:

  • Proposed PAPRec (prior-guided adaptive probabilistic network) for single-view 3D reconstruction.
  • Employed latent normalizing flow to fit image and 3D prior feature distributions.
  • Utilized an adaptive probabilistic network with shape normalizing flow and a diffusion model for decoding.

Main Results:

  • PAPRec demonstrated superior performance on the ShapeNet dataset.
  • Achieved average improvements of 2.62% in Chamfer Distance (CD), 5.99% in Earth Mover's Distance (EMD), and 4.41% in F1 score.
  • Outperformed several state-of-the-art methods in 3D reconstruction accuracy.

Conclusions:

  • PAPRec effectively learns global and local object features by integrating 3D prior guidance and adaptive probabilistic networks.
  • The proposed method offers a significant advancement in single-view 3D shape reconstruction accuracy and robustness.