# Scalable Change Retrieval Using Deep 3D Neural Codes

## TL;DR

This paper introduces a scalable, efficient framework for image change detection from 3D imagery by replacing traditional alignment with an invariant coordinate system and leveraging deep 3D features trained unsupervised, improving cross-season ICD performance.

## Contribution

It extends image change detection to an image retrieval paradigm using invariant coordinates and deep 3D features trained unsupervised, enabling faster and more scalable change detection.

## Key findings

- Effective cross-season ICD demonstrated on public dataset.
- Unsupervised training of deep 3D features improves robustness.
- Invariant coordinate system reduces alignment time.

## Abstract

We present a novel scalable framework for image change detection (ICD) from an on-board 3D imagery system. We argue that existing ICD systems are constrained by the time required to align a given query image with individual reference image coordinates. We utilize an invariant coordinate system (ICS) to replace the time-consuming image alignment with an offline pre-processing procedure. Our key contribution is an extension of the traditional image comparison-based ICD tasks to setups of the image retrieval (IR) task. We replace each component of the 3D ICD system, i.e., (1) image modeling, (2) image alignment, and (3) image differencing, with significantly efficient variants from the bag-of-words (BoW) IR paradigm. Further, we train a deep 3D feature extractor in an unsupervised manner using an unsupervised Siamese network and automatically collected training data. We conducted experiments on a challenging cross-season ICD task using a publicly available dataset and thereby validate the efficacy of the proposed approach.