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We study a new problem of semantic complete scene forecasting (SCSF) in thiswork. Given a 4D dynamic point cloud sequence, our goal is to forecast thecomplete scene corresponding to the future next frame along with its semanticlabels. To tackle this challenging problem, we properly model the synergeticrelationship between future forecasting and semantic scene completion through anovel network named SCSFNet. SCSFNet leverages a hybrid geometricrepresentation for high-resolution complete scene forecasting. To leveragemulti-frame observation as well as the understanding of scene dynamics to easethe completion task, SCSFNet introduces an attention-based skip connectionscheme. To ease the need to model occlusion variations and to better focus onthe occluded part, SCSFNet utilizes auxiliary visibility grids to guide theforecasting task. To evaluate the effectiveness of SCSFNet, we conductexperiments on various benchmarks including two large-scale indoor benchmarkswe contributed and the outdoor SemanticKITTI benchmark. Extensive experimentsshow SCSFNet outperforms baseline methods on multiple metrics by a largemargin, and also prove the synergy between future forecasting and semanticscene completion.

Semantic Complete Scene Forecasting from a 4D Dynamic Point Cloud Sequence