GNeRP: Gaussian-guided Neural Reconstruction of Reflective Objects with Noisy Polarization Priors

arXiv:2403.11899v1 Announce Type: new
Abstract: Learning surfaces from neural radiance field (NeRF) became a rising topic in Multi-View Stereo (MVS). Recent Signed Distance Function (SDF)-based methods demonstrated their ability to reconstruct accurate 3D shapes of Lambertian scenes. However, their results on reflective scenes are unsatisfactory due to the entanglement of specular radiance and complicated geometry. To address the challenges, we propose a Gaussian-based representation of normals in SDF fields. Supervised by polarization priors, this representation guides the learning of geometry …

abstract arxiv cs.cv entanglement function however nerf neural radiance field objects results type view