# Irregularity Inspection using Neural Radiance Field

arXiv: https://arxiv.org/abs/2408.11251 · HTML: https://mlsanity.com/paper/2408.11251/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 11 references that are in the map.
- First posted on arXiv 21 Aug 2024.

## Builds on (most cited first, 5 of 5)

- [Nerfstudio: A Modular Framework for Neural Radiance Field Development](https://mlsanity.com/paper/2302.04264/) (2023)
- [Neural Fields in Visual Computing and Beyond](https://mlsanity.com/paper/2111.11426/) (2021)
- [Neural Volume Rendering: NeRF And Beyond](https://mlsanity.com/paper/2101.05204/) (2020)
- [Enhancing Diffusion-based Point Cloud Generation with Smoothness Constraint](https://mlsanity.com/paper/2404.02396/) (2024)
- [ICP Algorithm: Theory, Practice And Its SLAM-oriented Taxonomy](https://mlsanity.com/paper/2206.06435/) (2022)

## Built on by (most cited first, 2 of 2)

- [Generating Multimodal Images with GAN: Integrating Text, Image, and Style](https://mlsanity.com/paper/2501.02167/) (2025)
- [Hierarchical Multi-Stage BERT Fusion Framework with Dual Attention for Enhanced Cyberbullying Detection in Social Media](https://mlsanity.com/paper/2503.00342/) (2025)

## Abstract (arXiv)

With the increasing growth of industrialization, more and more industries are relying on machine automation for production. However, defect detection in large-scale production machinery is becoming increasingly important. Due to their large size and height, it is often challenging for professionals to conduct defect inspections on such large machinery. For example, the inspection of aging and misalignment of components on tall machinery like towers requires companies to assign dedicated personnel. Employees need to climb the towers and either visually inspect or take photos to detect safety hazards in these large machines. Direct visual inspection is limited by its low level of automation, lack of precision, and safety concerns associated with personnel climbing the towers. Therefore, in this paper, we propose a system based on neural network modeling (NeRF) of 3D twin models. By comparing two digital models, this system enables defect detection at the 3D interface of an object.