  In recent years, deep neural networks have shown remarkable progress in dense
disparity estimation from dynamic scenes in monocular structured light systems.
However, their performance significantly drops when applied in unseen
environments. To address this issue, self-supervised online adaptation has been
proposed as a solution to bridge this performance gap. Unlike traditional
fine-tuning processes, online adaptation performs test-time optimization to
adapt networks to new domains. Therefore, achieving fast convergence during the
adaptation process is critical for attaining satisfactory accuracy. In this
paper, we propose an unsupervised loss function based on long sequential
inputs. It ensures better gradient directions and faster convergence. Our loss
function is designed using a multi-frame pattern flow, which comprises a set of
sparse trajectories of the projected pattern along the sequence. We estimate
the sparse pseudo ground truth with a confidence mask using a filter-based
method, which guides the online adaptation process. Our proposed framework
significantly improves the online adaptation speed and achieves superior
performance on unseen data.
