Despite the recent success of Neural Radiance Field (NeRF), it is still challenging to render large-scale driving scenes with long trajectories, particularly when the rendering quality and efficiency are in high demand. Existing methods for such scenes usually involve with spatial warping, geometric supervision from zero-shot normal or depth estimation, or scene division strategies, where the synthesized views are often blurry or fail to meet the requirement of efficient rendering. To address the above challenges, this paper presents a novel framework that learns a density space from the scenes to guide the construction of a point-based renderer, dubbed as DGNR (Density-Guided Neural Rendering). In DGNR, geometric priors are no longer needed, which can be intrinsically learned from the density space through volumetric rendering. Specifically, we make use of a differentiable renderer to synthesize images from the neural density features obtained from the learned density space. A density-based fusion module and geometric regularization are proposed to optimize the density space. By conducting experiments on a widely used autonomous driving dataset, we have validated the effectiveness of DGNR in synthesizing photorealistic driving scenes and achieving real-time capable rendering.

翻译：尽管神经辐射场（NeRF）近期取得了成功，但在大规模驾驶场景的长轨迹渲染中仍面临挑战，尤其是当对渲染质量与效率有较高要求时。现有处理此类场景的方法通常涉及空间扭曲、基于零样本法线或深度估计的几何监督，或场景分割策略，但其合成视图往往模糊不清，或无法满足高效渲染的需求。为解决上述挑战，本文提出了一种新颖框架，该框架从场景中学习密度空间以引导基于点的渲染器构建，称为DGNR（密度引导神经渲染）。在DGNR中，无需引入几何先验，其可通过对密度空间进行体渲染来内生学习。具体而言，我们利用可微渲染器从密度空间中习得的神经密度特征合成图像，并设计了基于密度的融合模块与几何正则化以优化密度空间。通过在广泛使用的自动驾驶数据集上进行实验，我们验证了DGNR在合成逼真驾驶场景及实现实时渲染方面的有效性。