SurGe: Improved Surface Geometry in Point Maps

2026-05-29

AI Summary

This work addresses the limitations of existing feedforward monocular 3D reconstruction methods in capturing fine-grained local surface geometry, which conventional evaluation metrics often fail to adequately quantify. To this end, we propose SurGe, a novel framework that introduces a point-wise surface normal-based metric to explicitly assess local geometric orientation errors. SurGe incorporates several key innovations, including a point gradient matching loss, a neighborhood-aware attention decoder, depth-normalized 3D finite differences, and a feature-progressive upsampling strategy. Extensive experiments demonstrate that SurGe achieves state-of-the-art performance on eight zero-shot monocular geometry benchmarks in terms of the global point-wise AbsRel metric, while consistently and significantly improving the fidelity of both local surface geometry and its corresponding normals.

Abstract

Recent feedforward 3D reconstruction methods predict point maps and estimate global 3D geometry remarkably well. However, their predictions still exhibit inaccurate local surface geometry, which is clearly visible qualitatively but only weakly reflected in common metrics. To make these errors more explicit in evaluation, we introduce a point map normal metric that evaluates the local surface orientation induced by neighboring 3D predictions. To reduce these errors, we propose two complementary components: a point gradient matching loss that supervises depth-normalized 3D finite differences, and a Neighborhood Attention Decoder (NAD) that progressively upsamples features and uses Neighborhood Attention for local feature mixing. Across eight zero-shot monocular geometry benchmarks, our model, SurGe, achieves the best average rank for global point map AbsRel and consistently improves local point map and point map normal evaluations.

Problem

• surface geometry
• point maps
• 3D reconstruction
• local accuracy
• normal estimation

Innovation

• point map normal metric
• point gradient matching loss
• Neighborhood Attention Decoder
• local surface geometry
• monocular 3D reconstruction