SAP-Depth: Scene-Adaptive Probability Framework for Self-Supervised Monocular Depth Estimation
Self-supervised monocular depth estimation remains challenging due to the loss of fine-grained details and limited adaptability to varying scene structures. In this paper, we propose SAP-Depth, a novel architecture based on Scene-Adaptive Probabilistic. Unlike traditional methods, we introduce a Dynamic Bins mechanism powered by a Learnable Parameter Predictor, which adaptively estimates scene-specific depth distributions conditioned on the input image context. Furthermore, a Bin-wise Probability Distribution Decoder is designed to effectively fuse multi-scale representations to predict dense depth probability distributions, significantly enhancing boundary sharpness and structural recove...
| Authors | Bowen He, Li Zhang, Yujie Chen et al. |
|---|---|
| Primary category | cs.CV |
| Published | 17 October 2026 |
| Last updated at source | 17 October 2026 |
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