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arXiv Computer Vision By Zain Ul Abidin, George Dimas, Dimitris K. Iakovidis

Self-Supervised Perceptually Interpretable Monocular Depth Estimation

Read the original on arXiv Computer Vision →

The paper introduces PIMDE, a self‑supervised monocular depth estimation framework that decomposes input images into perceptual feature maps, each encoding a specific visual cue. Separate depth branches process these maps to produce individual depth estimates, which are then fused explicitly. Experiments on the KITTI benchmark show that PIMDE matches the accuracy of existing self‑supervised methods while offering clearer insight into how each perceptual cue contributes to depth prediction.

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.

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