用雷达数据指导多项式拟合，实现高精度低成本深度图生成
Radar-Guided Polynomial Fitting for Metric Depth Estimation
- 利用雷达信号预测多项式系数，非均匀调整深度图
- 在三个数据集上平均降低24.9% MAE、33.2% RMSE
- 适合需要高精度且低延迟的自动驾驶场景
我们提出POLAR，一种新型雷达引导的深度估计方法，将预训练单目深度估计（MDE）模型输出的无尺度深度预测，通过多项式拟合高效转化为度量深度图。现有方法依赖复杂架构或昂贵传感器，而本方法基于关键洞察：尽管MDE模型在每个物体或局部区域能保留合理深度结构，但各区域间可能存在相对错位，线性缩放与平移（仿射变换）不足以解决三处以上区域的对齐问题。为此，我们利用廉价且普及的雷达数据预测多项式系数，实现深度范围内的自适应非均匀调节，引入拐点以纠正错位。此外，通过一阶导数正则化的新训练目标，保障局部单调性，维持结构一致性。POLAR在三个数据集上达到领先性能，平均MAE降低24.9%，RMSE降低33.2%，同时在延迟和计算成本方面也达到最先进水平。
原文摘要 · Abstract (English)
We propose POLAR, a novel radar-guided depth estimation method that introduces polynomial fitting to efficiently transform scaleless depth predictions from pretrained monocular depth estimation (MDE) models into metric depth maps. Unlike existing approaches that rely on complex architectures or expensive sensors, our method is grounded in a fundamental insight: although MDE models often infer reasonable local depth structure within each object or local region, they may misalign these regions relative to one another, making a linear scale and shift (affine) transformation insufficient given three or more of these regions. To address this limitation, we use polynomial coefficients predicted from cheap, ubiquitous radar data to adaptively adjust predictions non-uniformly across depth ranges. In this way, POLAR generalizes beyond affine transformations and is able to correct such misalignments by introducing inflection points. Importantly, our polynomial fitting framework preserves structural consistency through a novel training objective that enforces local monotonicity via first-derivative regularization. POLAR achieves state-of-the-art performance across three datasets, outperforming existing methods by an average of 24.9% in MAE and 33.2% in RMSE, while also achieving state-of-the-art efficiency in terms of latency and computational cost.
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