Accuracy Does Not Guarantee Human-Likeness: Cross-Domain Human-Centered Benchmark in Monocular Depth Estimation

Deep neural networks (DNNs) are increasingly used as functional models of human vision, yet standard monocular depth estimation (MDE) benchmarks largely evaluate physical accuracy rather than behavioral alignment with humans. We introduce a cross-domain behavioral benchmark that aligns newly collected human absolute-distance judgments, physical ground truth, and model predictions at 16 locations in each of 652 KITTI and 654 NYU Depth V2 scenes. Human-model similarity was measured image-wise by partial correlation between scale-and-shift-aligned estimates while controlling for physical depth, and reference results were obtained for 69 diverse MDE models. Human error patterns were highly reliable, and most models showed positive but substantially lower similarity to humans. Human similarity, however, did not increase monotonically with metric accuracy: within each dataset, high-accuracy models converged toward similar residual error structures that remained distinct from humans, while both human-similarity scores and error-space organization were only weakly preserved across datasets. Thus, physical accuracy and human-like behavior are complementary, domain-sensitive dimensions of MDE performance. Our benchmark will provide a reusable basis for assessing future models along both dimensions.