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Related Experiment Video

Updated: Jun 16, 2025

Real-time Video Projection in an MRI for Characterization of Neural Correlates Associated with Mirror Therapy for Phantom Limb Pain

Real-time Video Projection in an MRI for Characterization of Neural Correlates Associated with Mirror Therapy for Phantom Limb Pain

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PSDF: Prior-Driven Neural Implicit Surface Learning for Multi-View Reconstruction.

Wanjuan Su, Chen Zhang, Qingshan Xu

IEEE Transactions on Visualization and Computer Graphics

|August 15, 2024

Summary

This summary is machine-generated.

The PSDF framework enhances neural implicit surface reconstruction by integrating external Multi-View Stereo (MVS) priors with internal neural implicit surface reconstruction (NISR) model priors. This approach improves geometric accuracy and detail in complex scenes.

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Area of Science:

Computer Vision
Computer Graphics
Geometric Deep Learning

Background:

Traditional Multi-View Stereo (MVS) pipelines struggle with noisy and incomplete surface geometry, particularly in low-texture or unevenly lit areas.
Neural Implicit Surface Reconstruction (NISR) offers a promising alternative but faces challenges in accurately reconstructing delicate geometry from uncontrolled real-world scenes due to underconstrained optimization.

Purpose of the Study:

To develop a novel framework (PSDF) that enhances neural implicit surface reconstruction quality.
To leverage both external geometric priors from pre-trained MVS networks and internal priors from NISR models.
To improve the accuracy and fidelity of reconstructed surfaces, especially in complex, uncontrolled environments.

Main Methods:

Introduced a framework (PSDF) combining external geometric priors from MVS and internal priors from NISR.
Implemented a visibility-aware feature consistency loss and depth prior-assisted sampling using external MVS priors.
Utilized internal prior-guided importance rendering to mitigate biased rendering issues inherent in NISR.

Main Results:

The proposed methods provide strong geometric consistency constraints and improve surface intersection point localization.
PSDF significantly enhances the accuracy and detail of neural implicit surface reconstruction.
Experiments on the Tanks and Temples datasets demonstrate state-of-the-art performance on complex, uncontrolled scenes.

Conclusions:

The PSDF framework effectively addresses the limitations of traditional MVS and basic NISR for surface reconstruction.
Integrating external and internal geometric priors is crucial for high-quality neural implicit surface learning.
PSDF achieves superior results in reconstructing delicate geometry from challenging real-world data.