Traditional methods for biological shape inference, such as deep learning (DL) and active contour models, face limitations in 3D. DL requires large labeled datasets, which are difficult to obtain, while active contour models rely on fine-tuned hyperparameters for intensity attraction and regularization. We introduce deltaMic, a novel 3D differentiable renderer for fluorescence microscopy. By leveraging differentiable Fourier-space convolution, deltaMic accurately models the image formation process, integrating a parameterized microscope point spread function and a mesh-based object representation. Unlike DL-based segmentation, it directly optimizes shape and microscopy parameters to fit real microscopy data, removing the need for large datasets or heuristic priors. To enhance efficiency, we develop a GPU-accelerated Fourier transform for triangle meshes, significantly improving speed. We demonstrate deltaMic’s ability to reconstruct cellular shapes from synthetic and real microscopy images, providing a robust tool for 3D segmentation and biophysical modeling. This work bridges physics-based rendering with modern optimization techniques, offering a new paradigm for microscopy image analysis and inverse biophysical modeling.