SOTAVerified

Scene Change Detection

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.

Papers

Showing 110 of 25 papers

TitleStatusHype
EMPLACE: Self-Supervised Urban Scene Change DetectionCode0
Towards Generalizable Scene Change Detection0
Robust Scene Change Detection Using Visual Foundation Models and Cross-Attention MechanismsCode1
ZeroSCD: Zero-Shot Street Scene Change Detection0
Towards Generalizable Scene Change DetectionCode2
Zero-Shot Scene Change DetectionCode2
A Category Agnostic Model for Visual Rearrangment0
Unsupervised Change Detection for Space Habitats Using 3D Point CloudsCode1
SeaDSC: A video-based unsupervised method for dynamic scene change detection in unmanned surface vehicles0
Industrial Scene Change Detection using Deep Convolutional Neural Networks0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1C-3POCategory mIoU27.8Unverified
2RTABMAP+CSCDNetCategory mIoU26.1Unverified
3RTABMAP+ChangeNetCategory mIoU23Unverified