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We introduce ScanComplete, a novel data-driven approach for taking anincomplete 3D scan of a scene as input and predicting a complete 3D model alongwith per-voxel semantic labels. The key contribution of our method is itsability to handle large scenes with varying spatial extent, managing the cubicgrowth in data size as scene size increases. To this end, we devise afully-convolutional generative 3D CNN model whose filter kernels are invariantto the overall scene size. The model can be trained on scene subvolumes butdeployed on arbitrarily large scenes at test time. In addition, we propose acoarse-to-fine inference strategy in order to produce high-resolution outputwhile also leveraging large input context sizes. In an extensive series ofexperiments, we carefully evaluate different model design choices, consideringboth deterministic and probabilistic models for completion and semanticinference. Our results show that we outperform other methods not only in thesize of the environments handled and processing efficiency, but also withregard to completion quality and semantic segmentation performance by asignificant margin.

ScanComplete: Large-Scale Scene Completion and Semantic Segmentation for 3D Scans