Estimating Extreme 3D Image Rotations using Cascaded Attention

Estimating large, extreme inter-image rotations is critical for numerous computer vision domains involving images related by limited or non-overlapping fields of view. In this work, we propose an attention-based approach with a pipeline of novel algorithmic components. First, as rotation estimation pertains to image pairs, we introduce an inter-image distillation scheme using Decoders to improve embeddings. Second, whereas contemporary methods compute a 4D correlation volume (4DCV) encoding inter-image relationships, we propose an Encoder-based cross-attention approach between activation maps to compute an enhanced equivalent of the 4DCV. Finally, we present a cascaded Decoder-based technique for alternately refining both the cross-attention and the rotation query. Our approach outperforms current state-of-the-art methods on extreme rotation estimation. We make our code publicly available.