Reconstruction of Partial Facades

Project Timeline & Milestones

Timeframe	Task	Completion
Week 4	Understanding the DiffPMAE paper
Week 5	Customising MAE model for 3D input
Week 6	Understanding DiffPMAE model from GitHub repo
Week 7	Setting up environment for DiffPMAE model
Week 8
Week 9
Week 10
Week 11
Week 12
Week 13
Week 14

Introduction

Motivation

Venice's facades represent a remarkable heritage of artistic and architectural ingenuity, reflecting centuries of cultural evolution. However, despite advancements in digital documentation, many scanned images of these facades are incomplete or improperly captured, leading to gaps in their visual representation. This limits the potential for accurate digital analysis, visualization, and preservation of these iconic structures.

To address this challenge, this project explores the application of different models for reconstruction of incomplete facade images. Firstly, we tried to implement a Masked Autoencoder (MAE). MAEs are powerful tools for self-supervised learning, aiming at reconstructing missing portions of data by leveraging patterns learned from complete examples. By training the model on a dataset of complete Venetian facade images, we aim to develop a system capable of accurately filling in the missing regions of improperly scanned images. The second model we tried to implement was an NMF,...