Monocular depth estimation via a detail semantic collaborative network for indoor scenes.

Area of Science:

• Computer Vision
• Artificial Intelligence
• 3D Reconstruction

Background:

• Monocular depth estimation is vital for indoor scene reconstruction, impacting energy efficiency, environmental modeling, and smart design.
• Indoor scenes present challenges due to low depth variability, complex object correlations, and diverse object types, hindering model robustness.

Purpose of the Study:

• To propose a novel network, the detail-semantic collaborative network (DSCNet), for robust and accurate monocular depth estimation in indoor environments.
• To address limitations in detail feature extraction and semantic correlation understanding in existing indoor depth estimation models.

Main Methods:

• Utilized a hierarchical transformer structure to capture comprehensive contextual image features.
• Developed a detail-semantic collaborative structure with selective attention feature maps to extract and fuse both detail and semantic information.
• Aggregated multi-level semantic and detailed features to model complex inter-object correlations.

Main Results:

• Achieved state-of-the-art performance on the NYU and SUN indoor depth estimation datasets, outperforming 14 recent optimal methods.
• Demonstrated improved perception ability and model accuracy without increasing parameter count.
• Validated the model's stability, robustness, and practical availability in indoor scenes through comprehensive analysis and ablation experiments.

Conclusions:

• The DSCNet effectively enhances monocular depth estimation for indoor scenes by synergistically leveraging detail and semantic information.
• The proposed approach offers a robust and accurate solution for applications requiring precise indoor 3D understanding.
• The method provides a significant advancement in handling the complexities of indoor environments for depth estimation tasks.