Multi-Label Semantic 3D Reconstruction Using Voxel Blocks

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ScanNet: Richly-Annotated 3D Reconstructions of Indoor Scenes (2017, methodology): “We use these tasks to demonstrate that Scan-Net enables the use of deep learning methods for 3D scene understanding tasks with supervised training, and compare performance to that using data from other existing datasets.”
Computer Vision for Autonomous Vehicles: Problems, Datasets and State-of-the-Art (2017, background): “Cherabier et al. (2016) propose to divide the scene into blocks in which only a set of relevant labels is active, since absence of many semantic classes from a specific block can be determined early on.”
Image2Mesh: A Learning Framework for Single Image 3D Reconstruction (2017, background): “Several papers have proposed a 3D volumetric representation [3, 4, 10, 11, 20, 24, 27, 30, 33, 35, 38] so they can feed deep neural networks applying 3D convolutions, pooling, and other techniques that have been successfully applied to 2D images for the learning process.”