arXiv Machine Learning July 8, 2026 By Yu Wu, Minsik Jeon, Jen-Hao Rick Chang, Oncel Tuzel, Shubham Tulsiani

RayRoPE: Projective Ray Positional Encoding for Multi-view Attention

Read the original on arXiv Machine Learning →

arXiv:2601. 15275v3 Announce Type: replace-cross Abstract: We study positional encodings for multi-view transformers that process tokens from a set of posed input images, and seek a mechanism that encodes patches uniquely, allows SE(3)-invariant attention with multi-frequency similarity, and can adapt to the geometry of the underlying 3D scene.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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DVGT: Driving Visual Geometry Transformer

DPPE: Rethinking Camera-Based Positional Encoding for Scaling Multi-View Transformers

EO-VGGT: Orbital Ray-Conditioned 3D Foundation Models for Satellite Multi-View Reconstruction

BA-T: An Iterative Transformer for Two-View Bundle Adjustment

SAF3R: Dynamic Sparse Attention for Feed-Forward 3D Reconstruction Transformers

LSRM: High-Fidelity Object-Centric Reconstruction via Scaled Context Windows