Deferred neural rendering (DNR) is an emerging computer graphics pipeline designed for high-fidelity rendering and robotic perception. However, DNR heavily relies on datasets composed of numerous ray-traced images and demands substantial computational resources. It remains under-explored how to reduce the reliance on high-quality ray-traced images while maintaining the rendering fidelity. In this paper, we propose DNRSelect, which integrates a reinforcement learning-based view selector and a 3D texture aggregator for deferred neural rendering. We first propose a novel view selector for deferred neural rendering based on reinforcement learning, which is trained on easily obtained rasterized images to identify the optimal views. By acquiring only a few ray-traced images for these selected views, the selector enables DNR to achieve high-quality rendering. To further enhance spatial awareness and geometric consistency in DNR, we introduce a 3D texture aggregator that fuses pyramid features from depth maps and normal maps with UV maps. Given that acquiring ray-traced images is more time-consuming than generating rasterized images, DNRSelect minimizes the need for ray-traced data by using only a few selected views while still achieving high-fidelity rendering results. We conduct detailed experiments and ablation studies on the NeRF-Synthetic dataset to demonstrate the effectiveness of DNRSelect. The code will be released.

翻译：延迟神经渲染（DNR）是一种新兴的计算机图形学管线，旨在实现高保真渲染与机器人感知。然而，DNR严重依赖由大量光线追踪图像构成的数据集，且需要大量计算资源。如何在保持渲染质量的同时减少对高质量光线追踪图像的依赖，目前仍未得到充分探索。本文提出DNRSelect，该方法集成了基于强化学习的视角选择器与三维纹理聚合器用于延迟神经渲染。我们首先提出一种基于强化学习的新型延迟神经渲染视角选择器，该选择器在易于获取的光栅化图像上进行训练以识别最优视角。通过仅为这些选定视角采集少量光线追踪图像，该选择器能使DNR实现高质量渲染。为进一步增强DNR的空间感知与几何一致性，我们引入一种三维纹理聚合器，将深度图与法线图的金字塔特征与UV贴图进行融合。鉴于获取光线追踪图像比生成光栅化图像更为耗时，DNRSelect通过仅使用少量选定视角的光线追踪数据，在实现高保真渲染结果的同时最小化了对光线追踪数据的需求。我们在NeRF-Synthetic数据集上进行了详尽的实验与消融研究，以证明DNRSelect的有效性。代码将公开释放。