Unleashing Network Potentials for Semantic Scene Completion

Fengyun Wang, Qianru Sun, Dong Zhang, Jinhui Tang; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 10314-10323

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 modules: a cross-modal modulation enabling the interdependence of gradient flows between modalities and a customized adversarial training scheme leveraging dynamic gradient competition. Specifically the cross-modal modulation adaptively re-calibrates the features to better excite representation potentials from each single modality. The adversarial training employs a minimax game of evolving gradients with customized guidance to strengthen the generator's perception of visual fidelity from both geometric completeness and semantic correctness. Extensive experimental results demonstrate that AMMNet outperforms state-of-the-art SSC methods by a large margin providing a promising direction for improving the effectiveness and generalization of SSC methods.