WHTMix: Efficient Stereo Depth Estimation via Walsh-Hadamard Token Mixing

WHTMix replaces the global self‑attention stage in stereo transformers with a data‑independent Walsh‑Hadamard token mixer, achieving the same end‑point error on synthetic driving data while cutting model compute by 2.46× and single‑image inference latency by 2.65×. The log‑linear cost of the mixer is especially advantageous for high‑resolution stereo matching, as shown by a complexity analysis that links benefits to the ratio of sequence length to channel width. Additionally, a hybrid log‑disparity loss is introduced to up‑weight small‑disparity pixels, improving accuracy on distant objects without extra computational cost.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

• llms
• computer-vision
• robotics
• efficiency
• benchmarks