View-Adaptive Renderer for View-Consistent 2D-to-3D Generation

2026-08-10

AI Summary

This work addresses the challenges of view inconsistency and geometric distortion in single-image 3D reconstruction, which often arise from projection ambiguities during multi-view synthesis. To mitigate these issues, the authors propose a view-adaptive neural rendering framework that employs a shared feature backbone to capture global structure while enabling per-view independent correction of rendering errors. A lightweight self-attention fusion module is introduced to integrate multi-view information and enhance geometric consistency without relying on supervision from diffusion models such as SDS. The method optimizes solely with photometric loss, achieving near state-of-the-art reconstruction fidelity while maintaining computational efficiency and significantly improving view consistency and practical performance.

Abstract

Reconstructing 3D shapes from a single image remains a fundamental yet challenging problem in computer vision. Traditional monocular 3D generation pipelines typically synthesize multiple views from a single input image before applying Neural Radiance Field (NeRF)-based reconstruction. However, inherent projective ambiguities often produce visual discontinuities across generated viewpoints, leading to inaccuracies in reconstructed 3D models. Current solutions either incur significant additional computational burdens or fail to adequately resolve practical inconsistencies between synthesized views. To address these limitations, we propose a novel viewpoint-adaptive neural rendering framework that enables robust 3D reconstruction even when given partially inconsistent multi-view inputs. Our approach introduces view-adaptive neural renderers that independently correct viewpoint-dependent errors while simultaneously sharing a global feature backbone to preserve structural coherence. Furthermore, we propose a self-attention fusion module that adaptively integrates multi-view information, ensuring geometric consistency without relying heavily on indirect regularizations or computationally intensive methods. Through extensive experiments, we demonstrate that our method consistently improves 3D reconstruction fidelity. Importantly, our approach achieves near state-of-the-art performance without diffusion-based SDS supervision, relying primarily on photometric rendering loss with lightweight attention regularizers. This balance between accuracy and efficiency makes the proposed framework highly practical for real-world applications.

Problem

• 3D reconstruction
• view consistency
• monocular 3D generation
• Neural Radiance Field
• projective ambiguity

Innovation

• view-adaptive rendering
• Neural Radiance Fields
• multi-view consistency
• self-attention fusion
• monocular 3D reconstruction