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Related Experiment Video

Updated: Mar 22, 2026

Author Spotlight: Assessment of Visual Acuity in Central Vision Loss Through Motion-Based Peripheral Vision Testing

Author Spotlight: Assessment of Visual Acuity in Central Vision Loss Through Motion-Based Peripheral Vision Testing

Published on: February 23, 2024

Shape Completion from a Single RGBD Image.

Dongping Li, Tianjia Shao, Hongzhi Wu

IEEE Transactions on Visualization and Computer Graphics

|April 20, 2016

Summary

This summary is machine-generated.

This study introduces a new method to create full 3D models from single RGBD images. It refines initial models using optimization for detailed and accurate 3D object reconstruction.

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Last Updated: Mar 22, 2026

Author Spotlight: Assessment of Visual Acuity in Central Vision Loss Through Motion-Based Peripheral Vision Testing

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Published on: February 23, 2024

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Published on: November 28, 2025

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

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Published on: December 3, 2013

Area of Science:

Computer Vision
3D Reconstruction
Geometric Modeling

Background:

Generating complete 3D models from single images is challenging.
Existing methods often struggle with geometric detail and global consistency.

Purpose of the Study:

To develop a novel approach for constructing complete 3D models from a single RGBD image.
To improve the accuracy and geometric detail of 3D object reconstruction.

Main Methods:

Non-rigidly aligning existing 3D models to input depth data for an initial estimate.
Employing a volumetric-patch-based optimization algorithm for refinement.
Utilizing high-level constraints like visibility, surface confidence, and symmetry.

Main Results:

Achieved globally consistent 3D models with fine geometric details from single RGBD inputs.
Demonstrated robust and accurate 3D model completion compared to state-of-the-art techniques.
Successfully reconstructed diverse object categories including busts, chairs, bikes, toys, vases, and tables.

Conclusions:

The proposed method offers an efficient and robust solution for single-image 3D model construction.
The use of optimization with high-level constraints enhances the quality of 3D reconstructions.
This approach advances the field of 3D computer vision and object modeling.