NoPose-NeuS: Jointly Optimizing Camera Poses with Neural Implicit
  Surfaces for Multi-view Reconstruction
Mohamed Shawky Sabae Hoda Anis Baraka Mayada Mansour Hadhoud
Learning neural implicit surfaces from volume rendering has become popular
for multi-view reconstruction. Neural surface reconstruction approaches can
recover complex 3D geometry that are difficult for classical Multi-view Stereo
(MVS) approaches, such as non-Lambertian surfaces and thin structures. However,
one key assumption for these methods is knowing accurate camera parameters for
the input multi-view images, which are not always available. In this paper, we
present NoPose-NeuS, a neural implicit surface reconstruction method that
extends NeuS to jointly optimize camera poses with the geometry and color
networks. We encode the camera poses as a multi-layer perceptron (MLP) and
introduce two additional losses, which are multi-view feature consistency and
rendered depth losses, to constrain the learned geometry for better estimated
camera poses and scene surfaces. Extensive experiments on the DTU dataset show
that the proposed method can estimate relatively accurate camera poses, while
maintaining a high surface reconstruction quality with 0.89 mean Chamfer
distance.