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 with no code
SeaDSC: A video-based unsupervised method for dynamic scene change detection in unmanned surface vehicles
To the best of our understanding, this work represents the first investigation of scene change detection in the maritime vision application.
Industrial Scene Change Detection using Deep Convolutional Neural Networks
Finding and localizing the conceptual changes in two scenes in terms of the presence or removal of objects in two images belonging to the same scene at different times in special care applications is of great significance.
Scene Change Detection Using Multiscale Cascade Residual Convolutional Neural Networks
Such architecture design directly impacts on the quality of the detection results, and also in the device resources capacity, like memory.
Crowd Source Scene Change Detection and Local Map Update
To circumvent this problems, we propose an approach based on point-clouds descriptors comparison: 1) Based on VPS poses select close query and map images pairs, 2) Registration of query images to map image descriptors, 3) Use segmentation to filter out dynamic or short term temporal changes, 4) Compare the descriptors between corresponding segments.
Shot boundary detection method based on a new extensive dataset and mixed features
Shot boundary detection in video is one of the key stages of video data processing.
City-scale Scene Change Detection using Point Clouds
We propose a method for detecting structural changes in a city using images captured from vehicular mounted cameras over traversals at two different times.
Hierarchical Paired Channel Fusion Network for Street Scene Change Detection
Based on the MPFL strategy, our framework achieves a novel approach to adapt to the scale and location diversities of the scene change regions.
Epipolar-Guided Deep Object Matching for Scene Change Detection
To cope with the difficulty, we introduce a deep graph matching network that establishes object correspondence between an image pair.
Topological map construction and scene recognition for vehicle localization
This paper presents a vehicle localization method to assist vehicle navigation based on topological map construction and scene recognition.
