DeMatch++: Two-View Correspondence Learning via Deep Motion Field Decomposition and Respective Local-Context Aggregation

Two-view correspondence learning has increasingly focused on the coherence and smoothness of motion fields between image pairs. Conventional methods either regularize the complexity of the field function at substantial computational expense, or apply local filters that prove ineffective for large scene disparities. In this paper, we present DeMatch++, a novel network drawing inspiration from Fourier decomposition principles that decomposes the motion field to retain its primary "low-frequency" and smooth components. This approach achieves implicit regularization with lower computational overhead while exhibiting inherent piecewise smoothness. Specifically, our method decomposes the noise-contaminated motion field into multiple linearly independent basis vectors, generating smooth sub-fields that preserve the main energy of the original field. These sub-fields facilitate the recovery of a cleaner motion field for precise vector derivation. Within this framework, we aggregate local context within each sub-field while enhancing global information across all sub-fields. We also employ a masked decomposition strategy that mitigates the influence of false matches, and construct a compact representation to suppress redundant sub-fields. The complete pipeline is formulated as a discrete learnable architecture, circumventing the need for dense field computation. Extensive experiments demonstrate that DeMatch++ outperforms state-of-the-art methods while maintaining computational efficiency and piecewise smoothness.