  NeRFmm is the Neural Radiance Fields (NeRF) that deal with Joint Optimization
tasks, i.e., reconstructing real-world scenes and registering camera parameters
simultaneously. Despite NeRFmm producing precise scene synthesis and pose
estimations, it still struggles to outperform the full-annotated baseline on
challenging scenes. In this work, we identify that there exists a systematic
sub-optimality in joint optimization and further identify multiple potential
sources for it. To diminish the impacts of potential sources, we propose
Sinusoidal Neural Radiance Fields (SiNeRF) that leverage sinusoidal activations
for radiance mapping and a novel Mixed Region Sampling (MRS) for selecting ray
batch efficiently. Quantitative and qualitative results show that compared to
NeRFmm, SiNeRF achieves comprehensive significant improvements in image
synthesis quality and pose estimation accuracy. Codes are available at
https://github.com/yitongx/sinerf.
