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description=Semantic Complete Scene Forecasting from a 4D Dynamic Point Cloud Sequence;
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semantic complete scene forecasting from a 4d dynamic point cloud sequence semantic complete scene forecasting from a 4d dynamic point cloud sequence zifan wang 1 3 zhuorui ye 1 3 haoran wu 1 3 junyu chen 1 3 li yi 1 2 3 1 tsinghua university 2 shanghai artificial intelligence laboratory 3 shanghai qi zhi institute paper arxiv video code data semantic complete scene forecasting the scsf task consumes a 4d point cloud sequence here rgb is just to intuitively show the scene contents and forecasts the complete scene with semantic labels in the whole space in the next frame abstract we study a new problem of semantic complete scene forecasting scsf in this work given a 4d dynamic point cloud sequence our goal is to forecast the complete scene corresponding to the future next frame along with its semantic labels to tackle this challenging problem we properly model the synergetic relationship between future forecasting and semantic scene completion through a novel network named scsfnet scsfnet leverages a hybrid geometric representation for high resolution complete scene forecasting to leverage multi frame observation as well as the understanding of scene dynamics to ease the completion task scsfnet introduces an attention based skip connection scheme to ease the need to model occlusion variations and to better focus on the occluded part scsfnet utilizes auxiliary visibility grids to guide the forecasting task to evaluate the effectiveness of scsfnet we conduct experiments on various benchmarks including two large scale indoor benchmarks we contributed and the outdoor semantickitti benchmark extensive experiments show scsfnet outperforms baseline methods on multiple metrics by a large margin and also prove the synergy between future forecasting and semantic scene completion video igplay dataset the igplay dataset contains 1 000 scenes lasting 10 timesteps each where a viewer interacts with various toys among the furniture each scene in igplay contains 10 semantic classes in a great variety ignav dataset the ignav dataset contains 600 scenes lasting 10 timesteps each where several active robots move and navigate in the scene each scene in ignav contains 9 semantic classes in a great variety scsfnet scsfnet uses an egocentric 4d point cloud sequence with n frames to predict future scenes with semantic information it employs an encoder decoder structure with hybrid geometric representations attention based skip connections and a visibility grid for high resolution forecasting experiments our scsfnet outperforms baselines by a large margin on two synthetic indoor datasets igplay and ignav and one real world dataset semantickitti in different settings dataset download coming soon contact if you have any questions please contact zifan wang wzf22 mails tsinghua edu cn zhuorui ye yezr21 mails tsinghua edu cn haoran wu wuhr20 mails tsinghua edu cn bibtex article wang2023semantic title semantic complete scene forecasting from a 4d dynamic point cloud sequence author wang zifan and ye zhuorui and wu haoran and chen junyu and yi li journal arxiv preprint arxiv 2312 08054 year 2023 the template is borrowed from nerfies