Unleashing Network Potentials for Semantic Scene Completion: AMMNet Framework
The author proposes the Adversarial Modality Modulation Network (AMMNet) to address ineffective feature learning and overfitting in semantic scene completion, achieving significant performance improvements.
SLCF-Net: Semantic Scene Completion with LiDAR-Camera Fusion
SLCF-Net introduces a novel approach for Semantic Scene Completion by fusing LiDAR and camera data to estimate missing geometry and semantics in urban driving scenarios.
SLCF-Net: Semantic Scene Completion with LiDAR-Camera Fusion
SLCF-Net introduces a novel approach for Semantic Scene Completion by fusing LiDAR and camera data, achieving superior performance in SSC metrics.
Hardness-Aware Semantic Scene Completion with Self-Distillation: Improving Accuracy in Challenging Regions
The core message of this paper is to propose a hardness-aware semantic scene completion (HASSC) approach that can effectively improve the accuracy of existing models in challenging regions without incurring extra inference cost. The key innovations are the hard voxel mining (HVM) head that leverages both global and local hardness to focus on hard voxels, and the self-distillation training strategy that enhances the stability and consistency of the model.
Bridging Stereo Geometry and Bird's-Eye-View Representation for Reliable Semantic Scene Completion
The proposed BRGScene framework effectively bridges stereo geometry and bird's-eye-view (BEV) representation to achieve reliable and accurate semantic scene completion solely from stereo RGB images.
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