Neural Image Space Tessellation

2026-02-27

AI Summary

This work proposes a lightweight screen-space neural post-processing method to address the high computational cost of traditional geometric subdivision in real-time rendering of large-scale low-polygon models, which struggles to efficiently produce smooth silhouettes. By reformulating subdivision as an image-domain task—an approach novel to this domain—the method progressively deforms object contours through multi-scale convolutional operations. It further preserves visual consistency by analyzing discrepancies between geometric and shading normals and applying implicit texture remapping. Crucially, the technique is fully decoupled from the original mesh complexity, enabling it to generate smooth, coherent silhouettes comparable to those achieved by geometric subdivision at a constant per-frame computational cost, thereby significantly enhancing real-time rendering efficiency for large-scale scenes.

Technology Category

• Computer Vision: Low Level & Physics-based Vision
• Search and Optimization: Mixed Discrete/Continuous Search
• Machine Learning: Learning with Manifolds

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

We present Neural Image-Space Tessellation (NIST), a lightweight screen-space post-processing approach that produces the visual effect of tessellated geometry while rendering only the original low-polygon meshes. Inspired by our observation from Phong tessellation, NIST leverages the discrepancy between geometric normals and shading normals as a minimal, view-dependent cue for silhouette refinement. At its core, NIST performs multi-scale neural tessellation by progressively deforming image-space contours with convolutional operators, while jointly reassigning appearance information through an implicit warping mechanism to preserve texture coherence and visual fidelity. Experiments demonstrate that our approach produces smooth, visually coherent silhouettes comparable to geometric tessellation, while incurring a constant per-frame cost and fully decoupled from geometric complexity, making it well-suited for large-scale real-time rendering scenarios. To the best of our knowledge, our NIST is the first work to reformulate tessellation as a post-processing operation, shifting it from a pre-rendering geometry pipeline to a screen space neural post-processing stage.

Problem

• tessellation
• real-time rendering
• screen-space post-processing
• low-polygon meshes
• visual fidelity

Innovation

• Neural Image-Space Tessellation
• screen-space post-processing
• normal discrepancy
• implicit warping
• real-time rendering