Spectral Probing of Feature Upsamplers in 2D-to-3D Scene Reconstruction

2026-03-06

AI Summary

This study addresses the lack of systematic evaluation regarding how existing learnable upsampling methods affect 3D perception while enhancing spatial details, leaving their efficacy in 2D-to-3D reconstruction unclear. To bridge this gap, the authors propose a spectral diagnostic framework that quantifies the impact of upsamplers on geometric consistency and texture fidelity through six complementary metrics, establishing—for the first time—a quantitative link between spectral consistency and novel view synthesis quality. Leveraging analyses of magnitude redistribution, structural spectral alignment, and directional stability, the framework evaluates various upsampling strategies on CLIP and DINO backbones. The findings reveal that reconstruction performance critically depends on structural spectral consistency rather than high-frequency detail enhancement; although learnable upsampling improves sharpness, it generally fails to surpass classical interpolation methods.

Technology Category

• Computer Vision: 3D Computer Vision
• Machine Learning: Multi-instance/Multi-view Learning
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

A typical 2D-to-3D pipeline takes multi-view images as input, where a Vision Foundation Model (VFM) extracts features that are spatially upsampled to dense representations for 3D reconstruction. If dense features across views preserve geometric consistency, differentiable rendering can recover an accurate 3D representation, making the feature upsampler a critical component. Recent learnable upsampling methods mainly aim to enhance spatial details, such as sharper geometry or richer textures, yet their impact on 3D awareness remains underexplored. To address this gap, we introduce a spectral diagnostic framework with six complementary metrics that characterize amplitude redistribution, structural spectral alignment, and directional stability. Across classical interpolation and learnable upsampling methods on CLIP and DINO backbones, we observe three key findings. First, structural spectral consistency (SSC/CSC) is the strongest predictor of NVS quality, whereas High-Frequency Spectral Slope Drift (HFSS) often correlates negatively with reconstruction performance, indicating that emphasizing high-frequency details alone does not necessarily improve 3D reconstruction. Second, geometry and texture respond to different spectral properties: Angular Energy Consistency (ADC) correlates more strongly with geometry-related metrics, while SSC/CSC influence texture fidelity slightly more than geometric accuracy. Third, although learnable upsamplers often produce sharper spatial features, they rarely outperform classical interpolation in reconstruction quality, and their effectiveness depends on the reconstruction model. Overall, our results indicate that reconstruction quality is more closely related to preserving spectral structure than to enhancing spatial detail, highlighting spectral consistency as an important principle for designing upsampling strategies in 2D-to-3D pipelines.

Problem

• feature upsampling
• 2D-to-3D reconstruction
• spectral consistency
• geometric consistency
• Vision Foundation Model

Innovation

• spectral probing
• feature upsampling
• 3D reconstruction
• spectral consistency
• Vision Foundation Model