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Adam Goodge's Projects

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Official Implementation for the "Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detection" paper.

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Robustness of Autoencoders for Anomaly Detection Under Adversarial Impact

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ARES: Locally Adaptive Reconstruction-based Anomaly Scoring

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This is the code to the WACV 2023 paper "Asymmetric Student-Teacher Networks for Industrial Anomaly Detection" by Marco Rudolph, Tom Wehrbein, Bodo Rosenhahn and Bastian Wandt.

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Implementation code for the paper "Graph Neural Network-Based Anomaly Detection in Multivariate Time Series" (AAAI 2021)

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[CVPR 2023] Learning 3D Representations from 2D Pre-trained Models via Image-to-Point Masked Autoencoders

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Latent Point Diffusion Models for 3D Shape Generation

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LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networks

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PyTorch implementation of MAE https//arxiv.org/abs/2111.06377

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Repo for "Benchmarking Robustness of 3D Point Cloud Recognition against Common Corruptions" https://arxiv.org/abs/2201.12296

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[CVPR 2022] Pre-Training 3D Point Cloud Transformers with Masked Point Modeling

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[NeurIPS 2022] Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training

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[ECCV2022] Masked Autoencoders for Point Cloud Self-supervised Learning

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[NeurIPS 2023] Offical code for <Real3D-AD: A Dataset of Point Cloud Anomaly Detection>. A 3D point cloud anomaly detection dataset and benchmark.

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Code for Revisiting Point Cloud Classification: A New Benchmark Dataset and Classification Model on Real-World Data, ICCV 2019

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A pytorch implementation for paper 'Exploring Simple Siamese Representation Learning'

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The codes for the work "Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation"

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