用自监督方法让单目深度图边界更清晰，减少模糊错误点。
Towards Sharper Object Boundaries in Self-Supervised Depth Estimation
- 将每个像素深度建模为混合分布，把不确定性转移到权重上。
- 在KITTI和VKITTIv2上边界清晰度提升35%，点云质量更高。
- 无需精细标注，可无缝接入现有深度估计流程，适合自动驾驶场景。
准确的单目深度估计对三维场景理解至关重要，但现有方法常在物体边界处产生模糊，引入虚假的中间三维点。虽然清晰边缘通常需要细粒度监督，但本文方法仅使用自监督即可生成锐利的深度不连续。具体地，我们对每像素深度建模为混合分布，捕捉多个可能的深度值，并将不确定性从直接回归转移到混合权重。该框架可通过方差感知损失函数和不确定性传播，无缝集成到现有流程中。在KITTI和VKITTIv2上的大量实验表明，本方法相比最先进基线，边界清晰度最高提升35%，点云质量显著改善。
原文摘要 · Abstract (English)
Accurate monocular depth estimation is crucial for 3D scene understanding, but existing methods often blur depth at object boundaries, introducing spurious intermediate 3D points. While achieving sharp edges usually requires very fine-grained supervision, our method produces crisp depth discontinuities using only self-supervision. Specifically, we model per-pixel depth as a mixture distribution, capturing multiple plausible depths and shifting uncertainty from direct regression to the mixture weights. This formulation integrates seamlessly into existing pipelines via variance-aware loss functions and uncertainty propagation. Extensive evaluations on KITTI and VKITTIv2 show that our method achieves up to 35% higher boundary sharpness and improves point cloud quality compared to state-of-the-art baselines.
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