  We present a method to automatically compute correct gradients with respect
to geometric scene parameters in neural SDF renderers. Recent physically-based
differentiable rendering techniques for meshes have used edge-sampling to
handle discontinuities, particularly at object silhouettes, but SDFs do not
have a simple parametric form amenable to sampling. Instead, our approach
builds on area-sampling techniques and develops a continuous warping function
for SDFs to account for these discontinuities. Our method leverages the
distance to surface encoded in an SDF and uses quadrature on sphere tracer
points to compute this warping function. We further show that this can be done
by subsampling the points to make the method tractable for neural SDFs. Our
differentiable renderer can be used to optimize neural shapes from multi-view
images and produces comparable 3D reconstructions to recent SDF-based inverse
rendering methods, without the need for 2D segmentation masks to guide the
geometry optimization and no volumetric approximations to the geometry.
