Scene Change Detection
9 papers with code • 4 benchmarks • 4 datasets
Scene change detection (SCD) refers to the task of localizing changes and identifying change-categories given two scenes. A scene can be either an RGB (+D) image or a 3D reconstruction (point cloud). If the scene is an image, SCD is a form of pixel-level prediction because each pixel in the image is classified according to a category. On the other hand, if the scene is point cloud, SCD is a form of point-level prediction because each point in the cloud is classified according to a category.
Some example benchmarks for this task are VL-CMU-CD, PCD, and CD2014. Recently, more complicated benchmarks such as ChangeSim, HDMap, and Mallscape are released.
Models are usually evaluated with the Mean Intersection-Over-Union (Mean IoU), Pixel Accuracy, or F1 metrics.
Benchmarks
These leaderboards are used to track progress in Scene Change Detection
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3RScan
MSU Shot Boundary Detection Benchmark
ChangeSim
PCD
Latest papers
Unsupervised Change Detection for Space Habitats Using 3D Point Clouds
This work presents an algorithm for scene change detection from point clouds to enable autonomous robotic caretaking in future space habitats.
SARAS-Net: Scale and Relation Aware Siamese Network for Change Detection
Change detection (CD) aims to find the difference between two images at different times and outputs a change map to represent whether the region has changed or not.
Differencing based Self-supervised pretraining for Scene Change Detection
neurai-lab/dsp
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SCD is challenging due to noisy changes in illumination, seasonal variations, and perspective differences across a pair of views.
How to Reduce Change Detection to Semantic Segmentation
DoctorKey/C-3PO
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And most segmentation networks can be adapted to solve the CD problems with our MTF module.
ChangeSim: Towards End-to-End Online Scene Change Detection in Industrial Indoor Environments
We present a challenging dataset, ChangeSim, aimed at online scene change detection (SCD) and more.
DR-TANet: Dynamic Receptive Temporal Attention Network for Street Scene Change Detection
Herrccc/DR-TANet
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Street scene change detection continues to capture researchers' interests in the computer vision community.
Multi-Temporal Scene Classification and Scene Change Detection with Correlation based Fusion
In this work, we proposed a CorrFusion module that fuses the highly correlated components in bi-temporal feature embeddings.
Weakly Supervised Silhouette-based Semantic Scene Change Detection
A straightforward approach for this task is to train a semantic change detection network directly from a large-scale dataset in an end-to-end manner.
Learning to Measure Change: Fully Convolutional Siamese Metric Networks for Scene Change Detection
gmayday1997/SceneChangeDet
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A critical challenge problem of scene change detection is that noisy changes generated by varying illumination, shadows and camera viewpoint make variances of a scene difficult to define and measure since the noisy changes and semantic ones are entangled.
