用双失焦与立体视觉联合估计深度，实现小体积高精度测距。
Depth from Dual Differential Defocus and Stereo Consensus
- 基于双失焦理论与立体匹配的耦合算法，统一深度估计
- 4毫米基线下实现0.3-1.64米范围、1厘米误差的深度图
- 适合小型化被动双目测距设备，如移动机器人或穿戴设备
我们提出D^3S Consensus，一种基于物理模型的闭式算法，融合深度从失焦（DfD）与立体视觉，在超出相机景深范围的长工作距离内实现高精度深度估计。给定一对双失焦立体图像，该方法利用新型双差分失焦（D^3）理论与立体信息，联合估计过约束深度集，并通过物理独立线索间的共识机制筛选最可信结果，剔除不可靠估计。分析表明，在相同误差容忍度下，该方法仅需传统三角测量系统1/10的基线。我们实现了首个原型系统，基线仅4毫米，等效焦距12毫米，可生成高达900×1800像素的深度图，在0.3至1.64米范围内平均绝对误差为1厘米，单次拍摄完成。其精度已超越部分商用大体积立体相机。
原文摘要 · Abstract (English)
We introduce D^3S Consensus, a physics-based, closed-form algorithm that unifies depth-from-defocus (DfD) and stereo to achieve highly accurate depth estimation throughout an extended working range beyond the depth-of-field (DoF) of cameras. Given a pair of dual-defocus stereo images, the method estimates an overdetermined set of depth using a novel DfD theory, Dual Differential Defocus (D^3), and (S)tereo in a coupled fashion. It then picks the most confident depth prediction from the set by enforcing consensus between these physically independent cues to reject unreliable estimates. Analysis shows that D^3S achieves a comparable working range under the same error tolerance with 10x smaller baseline than previous triangulation-based depth estimation systems. This enables compact passive binocular rangefinders with substantially smaller form factors than conventional stereo and DfD designs. We demonstrate the first D^3S prototype with only 4 mm baseline and 12 mm EFL. It generates up to 900 x 1800-pixel depth maps with 1-cm mean absolute error over 0.3-1.64 m from a snapshot acquisition. This has surpassed the reported accuracy of certain commercially available stereo cameras with much larger form factors.
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