# NeRF-Based defect detection

arXiv: https://arxiv.org/abs/2504.00270 · HTML: https://mlsanity.com/paper/2504.00270/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 23 references that are in the map.
- First posted on arXiv 31 Mar 2025.

## Builds on (most cited first, 13 of 13)

- [Contextual Hourglass Network for Semantic Segmentation of High Resolution Aerial Imagery](https://mlsanity.com/paper/1810.12813/) (2018)
- [A Comparative Study on Enhancing Prediction in Social Network Advertisement through Data Augmentation](https://mlsanity.com/paper/2404.13812/) (2024)
- [Evaluating Modern Approaches in 3D Scene Reconstruction: NeRF vs Gaussian-Based Methods](https://mlsanity.com/paper/2408.04268/) (2024)
- [Development and Application of a Monte Carlo Tree Search Algorithm for Simulating Da Vinci Code Game Strategies](https://mlsanity.com/paper/2403.10720/) (2024)
- [Deep Learning for Weather Forecasting: A CNN-LSTM Hybrid Model for Predicting Historical Temperature Data](https://mlsanity.com/paper/2410.14963/) (2024)
- [Self-Adaptive Robust Motion Planning for High DoF Robot Manipulator using Deep MPC](https://mlsanity.com/paper/2407.12887/) (2024)
- [Style Transfer: From Stitching to Neural Networks](https://mlsanity.com/paper/2409.00606/) (2024)
- [Comprehensive Overview of Artificial Intelligence Applications in Modern Industries](https://mlsanity.com/paper/2409.13059/) (2024)
- [Enhancing Diffusion-based Point Cloud Generation with Smoothness Constraint](https://mlsanity.com/paper/2404.02396/) (2024)
- [Collaborative Optimization in Financial Data Mining Through Deep Learning and ResNeXt](https://mlsanity.com/paper/2412.17314/) (2024)
- [A Review of Electromagnetic Elimination Methods for low-field portable MRI scanner](https://mlsanity.com/paper/2406.17804/) (2024)
- [Optimized Coordination Strategy for Multi-Aerospace Systems in Pick-and-Place Tasks By Deep Neural Network](https://mlsanity.com/paper/2412.09877/) (2024)
- [V2F-Net: Explicit Decomposition of Occluded Pedestrian Detection](https://mlsanity.com/paper/2104.03106/) (2021)

## Built on by (most cited first, 7 of 7)

- [JanusVLN: Decoupling Semantics and Spatiality with Dual Implicit Memory for Vision-Language Navigation](https://mlsanity.com/paper/2509.22548/) (2025)
- [SwiftVLA: Unlocking Spatiotemporal Dynamics for Lightweight VLA Models at Minimal Overhead](https://mlsanity.com/paper/2512.00903/) (2025)
- [Evaluating Supervised Learning Models for Fraud Detection: A Comparative Study of Classical and Deep Architectures on Imbalanced Transaction Data](https://mlsanity.com/paper/2505.22521/) (2025)
- [Research on Personalized Financial Product Recommendation by Integrating Large Language Models and Graph Neural Networks](https://mlsanity.com/paper/2506.05873/) (2025)
- [Instruction Tuning and CoT Prompting for Contextual Medical QA with LLMs](https://mlsanity.com/paper/2506.12182/) (2025)
- [A Machine Learning-Based Study on the Synergistic Optimization of Supply Chain Management and Financial Supply Chains from an Economic Perspective](https://mlsanity.com/paper/2509.03673/) (2025)
- [Modality-Aware Infrared and Visible Image Fusion with Target-Aware Supervision](https://mlsanity.com/paper/2509.11476/) (2025)

## Abstract (arXiv)

The rapid growth of industrial automation has highlighted the need for precise and efficient defect detection in large-scale machinery. Traditional inspection techniques, involving manual procedures such as scaling tall structures for visual evaluation, are labor-intensive, subjective, and often hazardous. To overcome these challenges, this paper introduces an automated defect detection framework built on Neural Radiance Fields (NeRF) and the concept of digital twins. The system utilizes UAVs to capture images and reconstruct 3D models of machinery, producing both a standard reference model and a current-state model for comparison. Alignment of the models is achieved through the Iterative Closest Point (ICP) algorithm, enabling precise point cloud analysis to detect deviations that signify potential defects. By eliminating manual inspection, this method improves accuracy, enhances operational safety, and offers a scalable solution for defect detection. The proposed approach demonstrates great promise for reliable and efficient industrial applications.