X-Lens: Real-Time Metric Depth Estimation with Heterogeneous Cameras

We present X-Lens, a compact feed-forward model for metric depth estimation from a variable number of distorted and pinhole views. To support real-time downstream perception, X-Lens is built around a geometry-aware heterogeneous camera formulation with two key components. Learnable calibration tokens provide a coarse alignment between fisheye and pinhole projective spaces, while a Jacobian-parameterized distortion bias injected into cross-attention models local projection changes and promotes cross-camera consistency, enabling robust generalization with only 0.04B parameters and up to 41 FPS. The model predicts dense depth together with a global metric scale, avoiding auxiliary reconstruction targets that increase computation and optimization complexity. To facilitate cross-camera generalization at scale and depth, X-Lens is trained on multiple public datasets and OmniScene, our newly curated large-scale synthetic dataset containing 266K synchronized six-view frames, around 1.6M individual images, and 126 indoor and outdoor scenes. Extensive experiments demonstrate superior heterogeneous-camera metric depth accuracy on our OmniScene dataset, alongside optimal or competitive performance on real-world fisheye and pinhole-only benchmarks. On OmniScene-Full, X-Lens reduces AbsRel by 25.4% over the strongest baseline while using 88.9% fewer parameters.