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BlenderProc
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BlenderProc is a modular procedural pipeline, which helps in generating real looking images for the training of convolutional neural networks. These can be used in a variety of use cases including segmentation, depth, normal and pose estimation and many others. A key feature of our extension of blender is the simple to use modular pipeline, which was designed to be easily extendable. By offering standard modules, which cover a variety of scenarios, we provide a starting point on which new modules can be created.
Forward citations
Cited by 2 Pith papers
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InSpace: Structure-Aware 3D Indoor Scene Generation from a Single 360{\deg} Image
InSpace generates complete structure-aware 3D indoor scenes (layout plus textured assets) from a single equirectangular 360° image via three-stage flow matching with view- and asset-selective attention.
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IDCNet: Guided Video Diffusion for Metric-Consistent RGBD Scene Generation with Precise Camera Control
The claimed IDC-Net framework is absent; the body text is an unrelated instance-segmentation paper.
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