Efficient Few-Shot Novel View Synthesis with Stable Surface Regularization

We propose a novel Annealing Signed Distance Function (ASDF) loss that enables stable and efficient few-shot NeRF optimization by enforcing adaptive geometric smoothing, allowing the network to first learn the overall structure and then progressively recover detailed geometry.