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Related Experiment Video

Updated: Sep 27, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Bidirectional Hybrid LSTM Based Recurrent Neural Network for Multi-View Stereo.

Zizhuang Wei, Qingtian Zhu, Chen Min

IEEE Transactions on Visualization and Computer Graphics

|April 8, 2022

Summary

This study introduces a novel recurrent neural network (RNN) for high-quality 3D point cloud reconstruction. The method uses a bidirectional LSTM for efficient cost volume regularization, achieving state-of-the-art results with reduced memory usage.

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Last Updated: Sep 27, 2025

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Area of Science:

Computer Vision
Deep Learning
3D Reconstruction

Background:

Deep learning Multi-view Stereo (MVS) networks show strong performance.
Previous MVS methods often use 3D CNNs or unidirectional RNNs for cost volume regularization.

Purpose of the Study:

To develop an effective and efficient recurrent neural network (RNN) for accurate and complete dense point cloud reconstruction.
To improve cost volume regularization in MVS networks.

Main Methods:

Utilized a bidirectional hybrid Long Short-Term Memory (LSTM) network for cost volume regularization.
Implemented a visibility-based approach for depth map refinement.
Evaluated on DTU, Tanks and Temples, and ETH3D datasets.

Main Results:

The bidirectional recurrent regularization captures full-space context similar to 3D CNNs.
Achieved state-of-the-art performance on benchmark datasets.
Demonstrated high memory efficiency during runtime.

Conclusions:

The proposed bidirectional recurrent regularization method enhances dense point cloud reconstruction accuracy and completeness.
The approach offers a memory-efficient alternative to existing MVS methods.
This work advances the field of deep learning-based 3D reconstruction.