# Towards High-Resolution 3D Anomaly Detection: A Scalable Dataset and Real-Time Framework for Subtle Industrial Defects

arXiv: https://arxiv.org/abs/2507.07435 · HTML: https://mlsanity.com/paper/2507.07435/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 23 references that are in the map.
- First posted on arXiv 10 Jul 2025.

## Builds on (most cited first, 19 of 19)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Point Transformer](https://mlsanity.com/paper/2011.00931/) (2020)
- [Towards Total Recall in Industrial Anomaly Detection](https://mlsanity.com/paper/2106.08265/) (2021)
- [Open3D: A Modern Library for 3D Data Processing](https://mlsanity.com/paper/1801.09847/) (2018)
- [Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modeling](https://mlsanity.com/paper/2111.14819/) (2021)
- [Multimodal Industrial Anomaly Detection via Hybrid Fusion](https://mlsanity.com/paper/2303.00601/) (2023)
- [AdaCLIP: Adapting CLIP with Hybrid Learnable Prompts for Zero-Shot Anomaly Detection](https://mlsanity.com/paper/2407.15795/) (2024)
- [Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detection](https://mlsanity.com/paper/2203.05550/) (2022)
- [Real3D-AD: A Dataset of Point Cloud Anomaly Detection](https://mlsanity.com/paper/2309.13226/) (2023)
- [Complementary Pseudo Multimodal Feature for Point Cloud Anomaly Detection](https://mlsanity.com/paper/2303.13194/) (2023)
- [Towards Scalable 3D Anomaly Detection and Localization: A Benchmark via 3D Anomaly Synthesis and A Self-Supervised Learning Network](https://mlsanity.com/paper/2311.14897/) (2023)
- [R3D-AD: Reconstruction via Diffusion for 3D Anomaly Detection](https://mlsanity.com/paper/2407.10862/) (2024)
- [PO3AD: Predicting Point Offsets toward Better 3D Point Cloud Anomaly Detection](https://mlsanity.com/paper/2412.12617/) (2024)
- [Look Inside for More: Internal Spatial Modality Perception for 3D Anomaly Detection](https://mlsanity.com/paper/2412.13461/) (2024)
- [Multi-Sensor Object Anomaly Detection: Unifying Appearance, Geometry, and Internal Properties](https://mlsanity.com/paper/2412.14592/) (2024)
- [Towards High-resolution 3D Anomaly Detection via Group-Level Feature Contrastive Learning](https://mlsanity.com/paper/2408.04604/) (2024)
- [MC3D-AD: A Unified Geometry-aware Reconstruction Model for Multi-category 3D Anomaly Detection](https://mlsanity.com/paper/2505.01969/) (2025)
- [Boosting Global-Local Feature Matching via Anomaly Synthesis for Multi-Class Point Cloud Anomaly Detection](https://mlsanity.com/paper/2505.07375/) (2025)
- [Towards Zero-shot Point Cloud Anomaly Detection: A Multi-View Projection Framework](https://mlsanity.com/paper/2409.13162/) (2024)

## Built on by (most cited first, 15 of 15)

- [A Lightweight 3D Anomaly Detection Method with Rotationally Invariant Features](https://mlsanity.com/paper/2511.13115/) (2025)
- [A Comprehensive Survey for Real-World Industrial Defect Detection: Challenges, Approaches, and Prospects](https://mlsanity.com/paper/2507.13378/) (2025)
- [VTFusion: A Vision-Text Multimodal Fusion Network for Few-Shot Anomaly Detection](https://mlsanity.com/paper/2601.16381/) (2026)
- [Anomaly Factory 3D: A Modular Framework for Diverse Pseudo-Anomaly Synthesis in Unsupervised 3D Anomaly Detection](https://mlsanity.com/paper/2606.29181/) (2026)
- [Physics-inspired Pseudo Anomaly Generation and Prototype Feature Guidance for 3D Anomaly Detection](https://mlsanity.com/paper/2607.10544/) (2026)
- [Multi-View Reconstruction with Global Context for 3D Anomaly Detection](https://mlsanity.com/paper/2507.21555/) (2025)
- [Open-Set Supervised 3D Anomaly Detection: An Industrial Dataset and a Generalisable Framework for Unknown Defects](https://mlsanity.com/paper/2604.01171/) (2026)
- [Synthesis4AD: Synthetic Anomalies are All You Need for 3D Anomaly Detection](https://mlsanity.com/paper/2604.04658/) (2026)
- [Breaking the Rigid Prior: Towards Articulated 3D Anomaly Detection](https://mlsanity.com/paper/2604.26868/) (2026)
- [Learning Discriminative Signed Distance Functions from Multi-scale Level-of-detail Features for 3D Anomaly Detection](https://mlsanity.com/paper/2605.03437/) (2026)
- [CoGeoAD: Hierarchical Color-Geometric Fusion with Multi-View Attention for Zero-Shot 3D Anomaly Detection](https://mlsanity.com/paper/2606.25273/) (2026)
- [M2P-AD: Memory-to-Prototype Learning with Boundary-aware Score Refinement for 3D Anomaly Detection](https://mlsanity.com/paper/2607.13499/) (2026)
- [PC2-AD: Point Cloud Upsampling to Safeguard 3D Anomaly Detection with Resolution-constrained Edge Devices](https://mlsanity.com/paper/2609.14722/) (2026)
- [AT3D-AD: Anomaly Type-Aware 3D Anomaly Detection via Hierarchical Point-Language Alignment](https://mlsanity.com/paper/2609.25930/) (2026)
- [Beyond Geometry: Benchmarking and Consistency Reasoning for 3D Logical Anomaly Detection](https://mlsanity.com/paper/2609.34143/) (2026)

## Abstract (arXiv)

In industrial point cloud analysis, detecting subtle anomalies demands high-resolution spatial data, yet prevailing benchmarks emphasize low-resolution inputs. To address this disparity, we propose a scalable pipeline for generating realistic and subtle 3D anomalies. Employing this pipeline, we developed MiniShift, the inaugural high-resolution 3D anomaly detection dataset, encompassing 2,577 point clouds, each with 500,000 points and anomalies occupying less than 1% of the total. We further introduce Simple3D, an efficient framework integrating Multi-scale Neighborhood Descriptors (MSND) and Local Feature Spatial Aggregation (LFSA) to capture intricate geometric details with minimal computational overhead, achieving real-time inference exceeding 20 fps. Extensive evaluations on MiniShift and established benchmarks demonstrate that Simple3D surpasses state-of-the-art methods in both accuracy and speed, highlighting the pivotal role of high-resolution data and effective feature aggregation in advancing practical 3D anomaly detection.