# Learning Discriminative Signed Distance Functions from Multi-scale Level-of-detail Features for 3D Anomaly Detection

arXiv: https://arxiv.org/abs/2605.03437 · HTML: https://mlsanity.com/paper/2605.03437/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 23 of its 39 references that are in the map.
- First posted on arXiv 5 May 2026.

## Builds on (most cited first, 23 of 23)

- [DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation](https://mlsanity.com/paper/1901.05103/) (2019)
- [Multimodal Industrial Anomaly Detection via Hybrid Fusion](https://mlsanity.com/paper/2303.00601/) (2023)
- [Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detection](https://mlsanity.com/paper/2203.05550/) (2022)
- [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)
- [Anomaly Detection in 3D Point Clouds using Deep Geometric Descriptors](https://mlsanity.com/paper/2202.11660/) (2022)
- [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)
- [PointAD: Comprehending 3D Anomalies from Points and Pixels for Zero-shot 3D Anomaly Detection](https://mlsanity.com/paper/2410.00320/) (2024)
- [SplatPose & Detect: Pose-Agnostic 3D Anomaly Detection](https://mlsanity.com/paper/2404.06832/) (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)
- [Exploring the Potential of Encoder-free Architectures in 3D LMMs](https://mlsanity.com/paper/2502.09620/) (2025)
- [Towards High-Resolution 3D Anomaly Detection: A Scalable Dataset and Real-Time Framework for Subtle Industrial Defects](https://mlsanity.com/paper/2507.07435/) (2025)
- [Looking 3D: Anomaly Detection with 2D-3D Alignment](https://mlsanity.com/paper/2406.19393/) (2024)
- [SplatPose+: Real-time Image-Based Pose-Agnostic 3D Anomaly Detection](https://mlsanity.com/paper/2410.12080/) (2024)
- [Taming Anomalies with Down-Up Sampling Networks: Group Center Preserving Reconstruction for 3D Anomaly Detection](https://mlsanity.com/paper/2507.03903/) (2025)
- [CASL: Curvature-Augmented Self-supervised Learning for 3D Anomaly Detection](https://mlsanity.com/paper/2511.12909/) (2025)
- [A Lightweight 3D Anomaly Detection Method with Rotationally Invariant Features](https://mlsanity.com/paper/2511.13115/) (2025)
- [Odd-One-Out: Anomaly Detection by Comparing with Neighbors](https://mlsanity.com/paper/2406.20099/) (2024)
- [CORENet: Cross-Modal 4D Radar Denoising Network with LiDAR Supervision for Autonomous Driving](https://mlsanity.com/paper/2508.13485/) (2025)
- [GS-CLIP: Zero-shot 3D Anomaly Detection by Geometry-Aware Prompt and Synergistic View Representation Learning](https://mlsanity.com/paper/2602.19206/) (2026)
- [C3D-AD: Toward Continual 3D Anomaly Detection via Kernel Attention with Learnable Advisor](https://mlsanity.com/paper/2508.01311/) (2025)

## Abstract (arXiv)

Detecting anomalies from 3D point clouds has received increasing attention in the field of computer vision, with some group-based or point-based methods achieving impressive results in recent years. However, learning accurate point-wise representations for 3D anomaly detection faces great challenges due to the large scale and sparsity of point clouds. In this study, a surface-based method is proposed for 3D anomaly detection, which learns a discriminative signed distance function using multi-scale level-of-detail features. We first present a Noisy Points Generation (NPG) module to generate different types of noise, thereby facilitating the learning of discriminative features by exposing abnormal points. Then, we introduce a Multi-scale Level-of-detail Feature (MLF) module to capture multi-scale information from a point cloud, which provides both fine-grained local and coarse-grained global feature information. Finally, we design an Implicit Surface Discrimination (ISD) module that leverages the extracted multi-scale features to learn an implicit surface representation of point clouds, which effectively trains a signed distance function to distinguish between abnormal and normal points. Experimental results demonstrate that the proposed method achieves an average object-level AUROC of 92.1% and 85.9% on the Anomaly-ShapeNet and Real3D-AD datasets, outperforming the current best approach by 2.1% and 3.6%, respectively. Codes are available at https://anonymous.4open.science/r/DLF-3AD-DA61.