  We introduce pixelSplat, a feed-forward model that learns to reconstruct 3D
radiance fields parameterized by 3D Gaussian primitives from pairs of images.
Our model features real-time and memory-efficient rendering for scalable
training as well as fast 3D reconstruction at inference time. To overcome local
minima inherent to sparse and locally supported representations, we predict a
dense probability distribution over 3D and sample Gaussian means from that
probability distribution. We make this sampling operation differentiable via a
reparameterization trick, allowing us to back-propagate gradients through the
Gaussian splatting representation. We benchmark our method on wide-baseline
novel view synthesis on the real-world RealEstate10k and ACID datasets, where
we outperform state-of-the-art light field transformers and accelerate
rendering by 2.5 orders of magnitude while reconstructing an interpretable and
editable 3D radiance field.
