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October 12, 2024Journal of Computer-Aided Design & Computer Graphics

Object Detection-Driven 3D Indoor Scene Reconstruction

Xiaojuan Ning Haogang Zhao Liang Gong Haiyan Jin Zhenghao Shi Yinghui Wang

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Key Points

The aim is to improve 3D indoor scene reconstruction accuracy by leveraging object detection techniques.
Utilized VoteNet as a base for enhanced local feature extraction using a convolution pooling module.
Designed a voting weight module to focus on foreground features and learn spatial relationship features.
Optimized model pose based on geometric constraints of the environment using instance and retrieval information.
Achieved an average accuracy of 63.8% on the ScanNet dataset, an improvement of 7.0 percentage points over VoteNet.
Demonstrated robustness and accuracy, especially in handling low-matching precision due to object occlusions.

Implication

Demonstrates improved indoor scene reconstruction accuracy using object detection in complex environments.

Abstract

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Cite This Study

Ning et al. (2024) studied this question.

synapsesocial.com/papers/6a0d4f19f03e14405aa9a51b https://doi.org/10.3724/sp.j.1089.2024-00240

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Keep reading

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

1Hybrid 3D Reconstruction of Indoor Scenes Integrating Object Recognition2024 · 2 citations
2Three-Dimensional Reconstruction of Indoor Scenes Based on Implicit Neural Representation2024 · 1 citations
3A Refinement Reconstruction Method for Indoor Structures Based on 3D Point Cloud Template Matching2025
4Research on Model Reconstruction Methods for Indoor Complex Scenes2025
5Research on Model Reconstruction Methods for Indoor Complex Scenes2025

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