通过置信度融合的伪视图增强用于未定位稀疏视图重建
Pseudo-View Enhancement via Confidence Fusion for Unposed Sparse-View Reconstruction
AI总结：
本文提出通过置信度融合的伪视图增强方法，解决稀疏视角下户外场景重建中的几何不一致和伪影问题，提升重建质量和稳定性。
摘要：
在未定位稀疏视角下进行3D场景重建是一个极具挑战性但实际应用重要的问题，尤其是在户外场景中，由于复杂的光照和尺度变化而更加困难。由于输入视角极有限，直接使用扩散模型合成伪帧会引入不合理的几何结构，从而影响最终的重建质量。为了解决这些问题，我们提出了一种新的框架，用于稀疏视图户外重建，通过双向伪帧恢复和场景感知高斯管理实现高质量的结果。具体来说，我们引入了一种双向伪帧恢复方法，通过基于相邻帧的扩散合成进行缺失内容的恢复，采用轻量级伪视图去模糊模型和置信度掩码推断算法。然后我们提出了一种场景感知高斯管理策略，基于联合深度密度信息优化高斯。这些设计显著提高了重建完整性，抑制了漂浮伪影，并在极端视角稀疏情况下提高了整体几何一致性。在户外基准测试中，实验结果表明在保真度和稳定性方面均显著优于现有方法。
英文摘要：
3D scene reconstruction under unposed sparse viewpoints is a highly challenging yet practically important problem, especially in outdoor scenes due to complex lighting and scale variation. With extremely limited input views, directly utilizing diffusion model to synthesize pseudo frames will introduce unreasonable geometry, which will harm the final reconstruction quality. To address these issues, we propose a novel framework for sparse-view outdoor reconstruction that achieves high-quality results through bidirectional pseudo frame restoration and scene perception Gaussian management. Specifically, we introduce a bidirectional pseudo frame restoration method that restores missing content by diffusion-based synthesis guided by adjacent frames with a lightweight pseudo-view deblur model and confidence mask inference algorithm. Then we propose a scene perception Gaussian management strategy that optimize Gaussians based on joint depth-density information. These designs significantly enhance reconstruction completeness, suppress floating artifacts and improve overall geometric consistency under extreme view sparsity. Experiments on outdoor benchmarks demonstrate substantial gains over existing methods in both fidelity and stability.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
