通过免训练自适应响应几何提升度量深度补全

中文摘要 AI 辅助

深度补全旨在从稀疏传感器测量中恢复稠密度量深度，日益利用视觉基础模型作为几何先验。然而，将这些先验对齐到真实度量尺度通常依赖于预定义坐标系中的刚性仿射假设，留下系统性校准误差。深度校准的线性取决于响应坐标。我们引入自适应响应几何，将深度、对数深度或视差的固定选择变为图像级别的未知量。一个连续响应族统一了这些坐标，并定义了显式的深度相关增益。我们推导了响应-梯度关系，并在度量空间中估计响应参数。硬狄利克雷残差重建完成了校准先验。在刻意不完整的度量观测下，免训练流程实现了宏观AbsRel 0.0301和宏观NMed 14.04°，在总体指标上优于PriorDA、LDCM和Any2Full。线性诊断检验了所选响应如何改变深度关系及其度量误差。

英文摘要

Depth completion aims to recover dense metric depth from sparse sensor measurements, increasingly leveraging visual foundation models as geometric priors. However, aligning these priors to true metric scale typically relies on rigid affine assumptions in predefined coordinate systems, leaving systematic calibration errors. Linearity in depth calibration depends on the response coordinate. We introduce adaptive response geometry, which makes the fixed choice of depth, log depth, or disparity an image-level unknown. A continuous response family unifies these coordinates and defines an explicit depth-dependent gain. We derive the response-gradient relation and estimate the response parameters in metric space. Hard-Dirichlet residual reconstruction completes the calibrated prior. Under deliberately incomplete metric observations, the training-free pipeline achieves macro AbsRel 0.0301 and macro NMed 14.04°, improving both aggregate measures over PriorDA, LDCM, and Any2Full. Linearity diagnostics examine how the selected response changes the depth relation and its metric error.

发表机构

• dConstruct Robotics
• University of Waterloo（滑铁卢大学）

机构由 AI 辅助整理，请以论文原文为准。