In real-world road scenes, diverse material properties lead to complex light
reflection phenomena, making accurate color reproduction crucial for enhancing
the realism and safety of simulated driving environments. However, existing
methods often struggle to capture the full spectrum of lighting effects,
particularly in dynamic scenarios where viewpoint changes induce significant
material color variations. To address this challenge, we introduce NieR
(Normal-Based Lighting Scene Rendering), a novel framework that takes into
account the nuances of light reflection on diverse material surfaces, leading
to more precise rendering. To simulate the lighting synthesis process, we
present the LD (Light Decomposition) module, which captures the lighting
reflection characteristics on surfaces. Furthermore, to address dynamic
lighting scenes, we propose the HNGD (Hierarchical Normal Gradient
Densification) module to overcome the limitations of sparse Gaussian
representation. Specifically, we dynamically adjust the Gaussian density based
on normal gradients. Experimental evaluations demonstrate that our method
outperforms state-of-the-art (SOTA) methods in terms of visual quality and
exhibits significant advantages in performance indicators. Codes are available
at this https URL

在真实世界的道路场景中，各种材料的物性导致光反射现象复杂，准确的颜色再现对于增强模拟驾驶环境的真实性和安全性至关重要。本研究提出了一种新颖的框架 NieR（基于法线的光照场景渲染），以考虑不同材料表面光反射的细微差别，从而实现更精确的渲染。为了模拟光照合成过程，介绍了 LD（光分解）模块，用于捕捉表面上的光反射特性。此外，为了解决动态光照场景的问题，提出了 HNGD（分层法线梯度密度化）模块，以克服稀疏高斯表示法的局限性。实验评估结果表明，我们的方法在视觉质量方面优于最先进方法，并在性能指标上具有显著优势。代码可在此 https URL 处获取。

NieR: 基于法线的光照场景渲染

NieR: Normal-Based Lighting Scene Rendering

Most of the traditional work on intrinsic image decomposition rely on
deriving priors about scene characteristics. On the other hand, recent research
use deep learning models as in-and-out black box and do not consider the
well-established, traditional image formation process as the basis of their
intrinsic learning process. As a consequence, although current deep learning
approaches show superior performance when considering quantitative benchmark
results, traditional approaches are still dominant in achieving high
qualitative results. In this paper, the aim is to exploit the best of the two
worlds. A method is proposed that (1) is empowered by deep learning
capabilities, (2) considers a physics-based reflection model to steer the
learning process, and (3) exploits the traditional approach to obtain intrinsic
images by exploiting reflectance and shading gradient information. The proposed
model is fast to compute and allows for the integration of all intrinsic
components. To train the new model, an object centered large-scale datasets
with intrinsic ground-truth images are created. The evaluation results
demonstrate that the new model outperforms existing methods. Visual inspection
shows that the image formation loss function augments color reproduction and
the use of gradient information produces sharper edges. Datasets, models and
higher resolution images are available at this https URL

该论文提出了一种揉合了深度学习与物理反射模型与渐变信息的新型本质图像分解方法，并通过大量实验证明其优于现有方法，最终形成快速、综合高效的本质图像分解方式。