Object Detection In Indoor Scenes
4 papers with code • 1 benchmarks • 10 datasets
Object detection in indoor scenes is the task of performing object detection within an indoor environment.
( Image credit: Faster Bounding Box Annotation for Object Detection in Indoor Scenes )
Benchmarks
These leaderboards are used to track progress in Object Detection In Indoor Scenes
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Datasets
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SUN RGB-D
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Kitchen Scenes
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ISOD
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Transparent Object Images | Indoor Object Dataset
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Stairs Image Dataset | Parts of House | Indoor
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Mobile Phone Dataset | Smartphone & Feature Phone
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Suitcase/Luggage Dataset Indoor Object Image
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Masks Dataset | Unattended Mask Images
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Electronics Object Image Dataset | Computer Parts
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Bottles and Cups Dataset | Household Objects
Latest papers with no code
Frustum VoxNet for 3D object detection from RGB-D or Depth images
Recently, there have been a plethora of classification and detection systems from RGB as well as 3D images.
The effect of changing training data on a fixed deep learning detection model
Within the lack of accurate data, for some computer vision applications, researchers usually use other pictures collected from different sources for the training.
Faster Bounding Box Annotation for Object Detection in Indoor Scenes
The procedure consists of two stages: The first step is to annotate a part of the dataset manually, and the second step proposes annotations for the remaining samples using a model trained with the first stage annotations.
2D-Driven 3D Object Detection in RGB-D Images
We then use the 3D information to orient, place, and score bounding boxes around objects.
Synthesizing Training Data for Object Detection in Indoor Scenes
In this work we explore the ability of using synthetically generated composite images for training state-of-the-art object detectors, especially for object instance detection.
