NieR: Normal-Based Lighting Scene Rendering

• URL: http://arxiv.org/abs/2405.13097v1
• Date: Tue, 21 May 2024 14:24:43 GMT
• Title: NieR: Normal-Based Lighting Scene Rendering
• Authors: Hongsheng Wang, Yang Wang, Yalan Liu, Fayuan Hu, Shengyu Zhang, Fei Wu, Feng Lin,
• Abstract summary: NieR (Normal-Based Lighting Scene Rendering) is a novel framework that takes into account the nuances of light reflection on diverse material surfaces. We present the LD (Light Decomposition) module, which captures the lighting reflection characteristics on surfaces. We also propose the HNGD (Hierarchical Normal Gradient Densification) module to overcome the limitations of sparse Gaussian representation.
• Score: 17.421326290704844
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 https://wanghongsheng01.github.io/NieR/.

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