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

• URL: http://arxiv.org/abs/2608.16984v1
• Date: Mon, 17 Aug 2026 18:00:02 GMT
• Title: PXDepth: Pixel-Space Modeling for Structure Preserving Monocular Depth Estimation
• Abstract summary: We propose PXDepth, a discriminative monocular depth model that separates global context modeling from pixel-level depth prediction.<n>Specifically, a large-patch ViT captures global scene context, while a pixel-space predictor composed of Context-Modulated Pixel Transformer blocks maintains high-resolution spatial representations.<n>This design preserves fine structures and sharp boundaries without sacrificing global depth consistency.
• Score: 102.98521307122452
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent monocular depth estimators achieve strong zero-shot generalization, yet often struggle to preserve fine-grained structures and object boundaries. We attribute this limitation to the prevalent combination of large-patch ViT encoders and convolutional decoders, as coarse tokenization can weaken pixel-level cues that upsampling cannot fully recover. To address this issue, we propose PXDepth, a discriminative monocular depth model that separates global context modeling from pixel-level depth prediction. Specifically, a large-patch ViT captures global scene context, while a pixel-space predictor composed of Context-Modulated Pixel Transformer blocks maintains high-resolution spatial representations throughout depth estimation. This design preserves fine structures and sharp boundaries without sacrificing global depth consistency. Across diverse zero-shot benchmarks, PXDepth combines faithful local geometry with competitive global depth accuracy while remaining efficient at inference. Our code and model are available at https://yuanzhy29.github.io/PXDepth-Page/.

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