MD-NeRF: Enhancing Large-Scale Scene Rendering and Synthesis With Hybrid Point Sampling and Adaptive Scene Decomposition

Yichen Zhang;Zhi Gao;Wenbo Sun;Yao Lu;Yuhan Zhu
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Abstract

Neural radiance fields (NeRFs) have gained great success in 3-D representation and novel-view synthesis, which attracted great efforts devoted to this area. However, when rendering large-scale scenes from a drone perspective, existing NeRF methods exhibit pronounced distortions in scene detail including absent textures and blurring of small objects. In this letter, we propose MD-NeRF to mitigate such distortions by integrating a hybrid sampling strategy and an adaptive scene decomposition method. Specifically, an anti-aliasing sampling method combining spiral sampling and sampling along rays is presented to address rendering anomalies. In addition, we decompose a large scene into multiple subscenes using a mixture of expert (MoE) modules. A shared expert is introduced to capture common features and reduce redundancy across the specialized experts. Consequently, the combination of these two methods effectively minimizes distortions when rendering large-scale scenes and enables our model to produce finer textures and more coherent details. We have conducted extensive experiments on several large-scale unbounded scene datasets, and the results demonstrate that our approach has achieved state-of-the-art performance on all datasets, most notably evidenced by a 1-dB enhancement in PSNR metrics on the Mill19 dataset.
MD-NeRF:利用混合点采样和自适应场景分解增强大规模场景渲染和合成功能
神经辐射场(NeRF)在三维表示和新颖视角合成方面取得了巨大成功,吸引了人们对这一领域的极大关注。然而,当从无人机视角渲染大型场景时,现有的神经辐射场方法会对场景细节产生明显的扭曲,包括纹理缺失和小物体模糊。在这封信中,我们提出了 MD-NeRF 方法,通过整合混合采样策略和自适应场景分解方法来减轻这种失真。具体来说,我们提出了一种结合螺旋采样和沿射线采样的抗锯齿采样方法,以解决渲染异常问题。此外,我们还使用混合专家(MoE)模块将大型场景分解为多个子场景。我们引入了共享专家来捕捉共同特征,减少各专业专家之间的冗余。因此,这两种方法的结合能有效减少大型场景渲染时的失真,并使我们的模型能生成更精细的纹理和更连贯的细节。我们在多个大规模无边界场景数据集上进行了广泛的实验,结果表明我们的方法在所有数据集上都取得了最先进的性能,最显著的表现是在 Mill19 数据集上的 PSNR 指标提高了 1 分贝。
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