Unleashing Network Potentials for Semantic Scene Completion

arXiv:2403.07560v2 Announce Type: replace
Abstract: Semantic scene completion (SSC) aims to predict complete 3D voxel occupancy and semantics from a single-view RGB-D image, and recent SSC methods commonly adopt multi-modal inputs. However, our investigation reveals two limitations: ineffective feature learning from single modalities and overfitting to limited datasets. To address these issues, this paper proposes a novel SSC framework - Adversarial Modality Modulation Network (AMMNet) - with a fresh perspective of optimizing gradient updates. The proposed AMMNet introduces two core …

abstract arxiv cs.cv datasets feature however image inputs investigation limitations modal multi-modal network overfitting rgb-d semantic semantics type view voxel