PXDepth: New Monocular Depth Estimation Model Preserves Fine Structures via Pixel-Space Modeling
A recent paper on arXiv (2608.16984v1) presents PXDepth, a monocular depth estimation model that distinguishes between global context modeling and pixel-level depth prediction. This innovative method tackles a frequent issue seen in contemporary estimators, which tend to overlook intricate structures and object edges due to the use of large-patch ViT encoders alongside convolutional decoders. PXDepth employs a large-patch ViT for capturing the overall scene context and a pixel-space predictor constructed from Context-Modulated Pixel Transformer blocks to uphold high-resolution spatial details. This architecture effectively retains fine structures and clear boundaries while ensuring global depth consistency. The model exhibits impressive zero-shot generalization across various benchmarks, merging accurate local geometry with competitive global depth precision.
Key facts
- arXiv:2608.16984v1 announces PXDepth, a new monocular depth estimation model.
- PXDepth separates global context modeling from pixel-level depth prediction.
- The model uses a large-patch ViT for global scene context.
- A pixel-space predictor with Context-Modulated Pixel Transformer blocks maintains high-resolution representations.
- The design preserves fine structures and sharp boundaries without sacrificing global depth consistency.
- Recent monocular depth estimators often struggle with fine-grained structures and object boundaries.
- PXDepth achieves strong zero-shot generalization across diverse benchmarks.
- The method combines faithful local geometry with competitive global depth accuracy.
Entities
Institutions
- arXiv