scsfnet.github.io - Semantic Complete Scene Forecasting from a 4D Dynamic Point Cloud Sequence

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

Example domain paragraphs

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

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 .

Links to scsfnet.github.io (2)

ericyi.github.io Li Yi
cuizhuyefei.github.io Zhuorui Ye