Kavli Affiliate: Cheng Peng
| First 5 Authors: Kaiwen Jiang, Venkataram Sivaram, Cheng Peng, Ravi Ramamoorthi,
| Summary:
Geometric reconstruction of opaque surfaces from images is a longstanding
challenge in computer vision, with renewed interest from volumetric view
synthesis algorithms using radiance fields. We leverage the geometry field
proposed in recent work for stochastic opaque surfaces, which can then be
converted to volume densities. We adapt Gaussian kernels or surfels to splat
the geometry field rather than the volume, enabling precise reconstruction of
opaque solids. Our first contribution is to derive an efficient and almost
exact differentiable rendering algorithm for geometry fields parameterized by
Gaussian surfels, while removing current approximations involving Taylor series
and no self-attenuation. Next, we address the discontinuous loss landscape when
surfels cluster near geometry, showing how to guarantee that the rendered color
is a continuous function of the colors of the kernels, irrespective of
ordering. Finally, we use latent representations with spherical harmonics
encoded reflection vectors rather than spherical harmonics encoded colors to
better address specular surfaces. We demonstrate significant improvement in the
quality of reconstructed 3D surfaces on widely-used datasets.
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