统一颜色和亮度校正与视图自适应曲线调整以实现鲁棒的3D新视角合成
Unifying Color and Lightness Correction with View-Adaptive Curve Adjustment for Robust 3D Novel View Synthesis
AI总结：
Luminance-GS++通过结合全局视图自适应亮度调整和局部像素级残差细化，实现鲁棒的3D新视角合成，提升重建保真度并保持实时渲染效率。
摘要：
现实环境中高质量图像采集仍面临挑战，由于复杂的光照变化和相机成像管道的固有限制。这些问题在多视角采集中尤为突出，其中光照差异、传感器响应和图像信号处理器（ISP）配置的差异引入了光度和色度不一致，这违反了现代3D新视角合成（NVS）方法（包括神经辐射场（NeRF）和3D高斯点绘制（3DGS））所依赖的光度一致性假设，导致重建和渲染质量下降。我们提出Luminance-GS++，一种基于3DGS的框架，用于在多样的光照条件下实现鲁棒的NVS。我们的方法结合了全局视图自适应的亮度调整和局部像素级残差细化以实现精确的颜色校正。我们进一步设计了无监督的目标，共同强制执行亮度校正和多视角几何和光度一致性。广泛的实验表明，该方法在具有挑战性的场景中表现出最先进的性能，包括低光、过曝以及复杂的亮度和色度变化。与先前方法不同，我们的方法保留了显式的3DGS公式，提高了重建保真度，同时保持了实时渲染效率。
英文摘要：
High-quality image acquisition in real-world environments remains challenging due to complex illumination variations and inherent limitations of camera imaging pipelines. These issues are exacerbated in multi-view capture, where differences in lighting, sensor responses, and image signal processor (ISP) configurations introduce photometric and chromatic inconsistencies that violate the assumptions of photometric consistency underlying modern 3D novel view synthesis (NVS) methods, including Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS), leading to degraded reconstruction and rendering quality. We propose Luminance-GS++, a 3DGS-based framework for robust NVS under diverse illumination conditions. Our method combines a globally view-adaptive lightness adjustment with a local pixel-wise residual refinement for precise color correction. We further design unsupervised objectives that jointly enforce lightness correction and multi-view geometric and photometric consistency. Extensive experiments demonstrate state-of-the-art performance across challenging scenarios, including low-light, overexposure, and complex luminance and chromatic variations. Unlike prior approaches that modify the underlying representation, our method preserves the explicit 3DGS formulation, improving reconstruction fidelity while maintaining real-time rendering efficiency.
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