3D shape reconstruction typically requires identifying object features or
textures in multiple images of a subject. This approach is not viable when the
subject is semi-transparent and moving in and out of focus. Here we overcome
these challenges by rendering a candidate shape with adaptive blurring and
transparency for comparison with the images. We use the microscopic nematode
Caenorhabditis elegans as a case study as it freely explores a 3D complex fluid
with constantly changing optical properties. We model the slender worm as a 3D
curve using an intrinsic parametrisation that naturally admits
biologically-informed constraints and regularisation. To account for the
changing optics we develop a novel differentiable renderer to construct images
from 2D projections and compare against raw images to generate a pixel-wise
error to jointly update the curve, camera and renderer parameters using
gradient descent. The method is robust to interference such as bubbles and dirt
trapped in the fluid, stays consistent through complex sequences of postures,
recovers reliable estimates from blurry images and provides a significant
improvement on previous attempts to track C. elegans in 3D. Our results
demonstrate the potential of direct approaches to shape estimation in complex
physical environments in the absence of ground-truth data.

本文提出一种使用自适应模糊和透明度来对比图像进行候选形状渲染的方法，以追踪 3D 复杂流体中自由运动的像微生物一样的动物 Caenorhabditis elegans 的形状。通过使用本文提出的不同 iable renderer，结合梯度下降算法同时更新样条曲线、摄像机和渲染参数，生成像素级误差和可靠估计。该方法表现出了在物理环境复杂的情况下进行形状估计的潜力。