PXDepth: Pixel-Space Modeling for Structure Preserving Monocular Depth Estimation

PXDepth is a new monocular depth model built around a simple but practical idea: keep global context and pixel-level detail separate. The authors argue that common ViT-plus-convolution pipelines lose fine structure early, and that no amount of upsampling fully restores those cues. For game teams using depth for reconstruction, scene understanding, or camera-driven effects, that tradeoff matters because object edges, props, and thin geometry are exactly where depth maps tend to fall apart.

The model uses a large-patch ViT to capture broad scene context, then hands off to a pixel-space predictor built from Context-Modulated Pixel Transformer blocks. That keeps high-resolution spatial representation alive through the prediction stage instead of collapsing everything into coarse tokens. The goal is cleaner local geometry and sharper boundaries while still preserving global depth consistency.

PXDepth is being positioned as a zero-shot monocular depth estimator, with results reported across multiple benchmarks and an emphasis on inference efficiency. The code and model are available, which makes it easier for teams to test whether the architecture holds up in production-style pipelines rather than just benchmark settings. For graphics and tools work, the interesting question is whether this kind of structure-preserving depth can improve downstream tasks like mesh generation, occlusion handling, or AI-assisted content workflows without adding too much latency.