Reproduce “Depth Matters: Exploring Deep Interactions of RGB-D for Semantic Segmentation in Traffic Scenes” on OpenResearch
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About this paper
Siyu Chen, Ting Han, Changshe Zhang, Weiquan Liu, Jinhe Su, Zongyue Wang, Guorong Cai
DiPFormer, a novel Transformer architecture, integrates depth information more deeply into RGB-D semantic segmentation for traffic scenes by generating spatially-aware position embeddings and enhancing cross-modal interaction. It achieves new state-of-the-art performance on challenging road detection and semantic segmentation benchmarks like KITTI Road, Cityscapes, and KITTI-360.
Key insights
- Deeply integrating the intrinsic spatial properties of depth maps is crucial for resolving attention shift issues and enhancing robustness in multi-modal semantic segmentation.
- Learnable position embeddings derived directly from depth features (Depth SAO) offer superior spatial guidance for attention mechanisms compared to generic positional encodings.
- Structuring cross-attention with depth features as queries for RGB features (Depth LCA) effectively identifies and leverages latent spatial differences, improving cross-modal interaction and semantic accuracy.
Summary by alphaXiv.
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