WHTMix: Efficient Stereo Depth Estimation via Walsh-Hadamard Token Mixing
Stereo depth estimation for driving, robotics and augmented reality must run at high resolution under tight latency budgets, yet in transformer-based matchers the global self-attention that aggregates scene context grows quadratically with the number of pixels and comes to dominate runtime. We show that the joint self-attention stage of a stereo transformer, whose role is to spread context across both views, can be replaced by a data-independent Walsh-Hadamard token mixer that mixes tokens globally in the transform domain at log-linear cost, while the data-dependent cross-attention that performs left-right correspondence is retained. On synthetic driving data the mixer matches the attenti...
| Authors | Prathyush Sajith, Emadeldeen Hamdan, Ahmet Enis Cetin |
|---|---|
| Primary category | cs.CV |
| Published | 28 July 2026 |
| Last updated at source | 15 October 2026 |
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