Unleashing Network Potentials for Semantic Scene Completion
CVPR 2024(2024)
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 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.
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