URNet: A Unified Reparameterized Network for Efficient RGB-D Semantic Segmentation
Previous RGB-D semantic segmentation methods commonly employ dual encoders to separately process RGB and depth inputs, followed by dedicated modules for cross-modal feature fusion. However, such designs often inadequately capture depth representations and consequently limit effective cross-modal interaction, while the additional encoder branch introduces redundant computation that hinders lightweight execution. To tackle these challenges, we propose URNet, a Unified Reparameterized RGB-D Network that performs simultaneous multi-modal feature extraction and cross-modal fusion within a single encoder. Specifically, we adopt a reparameterization strategy to compact the network architecture a...
| Authors | Guoan Xu, Zhengxue Wang, Yang Xiao et al. |
|---|---|
| Primary category | cs.CV |
| Published | 6 August 2026 |
| Last updated at source | 6 August 2026 |
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