  Implicit neural rendering, which uses signed distance function (SDF)
representation with geometric priors (such as depth or surface normal), has led
to impressive progress in the surface reconstruction of large-scale scenes.
However, applying this method to reconstruct a room-level scene from images may
miss structures in low-intensity areas or small and thin objects. We conducted
experiments on three datasets to identify limitations of the original color
rendering loss and priors-embedded SDF scene representation.
  We found that the color rendering loss results in optimization bias against
low-intensity areas, causing gradient vanishing and leaving these areas
unoptimized. To address this issue, we propose a feature-based color rendering
loss that utilizes non-zero feature values to bring back optimization signals.
Additionally, the SDF representation can be influenced by objects along a ray
path, disrupting the monotonic change of SDF values when a single object is
present. To counteract this, we explore using the occupancy representation,
which encodes each point separately and is unaffected by objects along a
querying ray. Our experimental results demonstrate that the joint forces of the
feature-based rendering loss and Occ-SDF hybrid representation scheme can
provide high-quality reconstruction results, especially in challenging
room-level scenarios. The code would be released.
