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Name: Adam Goodge
Type: User
Company: National University of Singapore
Name: Adam Goodge
Type: User
Company: National University of Singapore
Official Implementation for the "Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detection" paper.
Website
Robustness of Autoencoders for Anomaly Detection Under Adversarial Impact
ARES: Locally Adaptive Reconstruction-based Anomaly Scoring
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.
:thought_balloon: Diffusion Probabilistic Models for 3D Point Cloud Generation (CVPR 2021)
Implementation code for the paper "Graph Neural Network-Based Anomaly Detection in Multivariate Time Series" (AAAI 2021)
[CVPR 2023] Learning 3D Representations from 2D Pre-trained Models via Image-to-Point Masked Autoencoders
Latent Point Diffusion Models for 3D Shape Generation
LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networks
PyTorch implementation of MAE https//arxiv.org/abs/2111.06377
Repo for "Benchmarking Robustness of 3D Point Cloud Recognition against Common Corruptions" https://arxiv.org/abs/2201.12296
[CVPR 2022] Pre-Training 3D Point Cloud Transformers with Masked Point Modeling
[NeurIPS 2022] Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training
[ECCV2022] Masked Autoencoders for Point Cloud Self-supervised Learning
[NeurIPS 2023] Offical code for <Real3D-AD: A Dataset of Point Cloud Anomaly Detection>. A 3D point cloud anomaly detection dataset and benchmark.
(CVPR 2023) Revisiting Reverse Distillation for Anomaly Detection
Code for Revisiting Point Cloud Classification: A New Benchmark Dataset and Classification Model on Real-World Data, ICCV 2019
A pytorch implementation for paper 'Exploring Simple Siamese Representation Learning'
The codes for the work "Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation"
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