  We propose Differentiable Stereopsis, a multi-view stereo approach that
reconstructs shape and texture from few input views and noisy cameras. We pair
traditional stereopsis and modern differentiable rendering to build an
end-to-end model which predicts textured 3D meshes of objects with varying
topologies and shape. We frame stereopsis as an optimization problem and
simultaneously update shape and cameras via simple gradient descent. We run an
extensive quantitative analysis and compare to traditional multi-view stereo
techniques and state-of-the-art learning based methods. We show compelling
reconstructions on challenging real-world scenes and for an abundance of object
types with complex shape, topology and texture. Project webpage:
https://shubham-goel.github.io/ds/
