Neural Radiance Fields (NeRFs) have revolutionized the field of novel view synthesis, demonstrating remarkable performance. However, the modeling and rendering of reflective objects remain challenging problems. Recent methods have shown significant improvements over the baselines in handling reflective scenes, albeit at the expense of efficiency. In this work, we aim to strike a balance between efficiency and quality. To this end, we investigate an implicit-explicit approach based on conventional volume rendering to enhance the reconstruction quality and accelerate the training and rendering processes. We adopt an efficient density-based grid representation and reparameterize the reflected radiance in our pipeline. Our proposed reflection-aware approach achieves a competitive quality efficiency trade-off compared to competing methods. Based on our experimental results, we propose and discuss hypotheses regarding the factors influencing the results of density-based methods for reconstructing reflective objects. The source code is available at https://github.com/gkouros/ref-dvgo.

翻译：神经辐射场（NeRF）在新视角合成领域引起了革命性突破，展现出卓越性能。然而，反射物体的建模与渲染仍是一项具有挑战性的难题。近期方法虽在基准之上显著提升了反射场景的处理能力，却以牺牲效率为代价。本研究致力于实现效率与质量之间的平衡，为此，我们基于传统体渲染技术探索了一种隐式-显式混合方法，旨在提升重建质量并加速训练与渲染流程。我们采用高效的密度网格表示，并在处理管线中对反射辐射度进行重参数化。所提出的反射感知方法能够在质量-效率权衡中与竞争方法形成竞争优势。基于实验结果，我们提出并讨论了影响基于密度方法重建反射物体效果的关键因素假说。源代码已开源在https://github.com/gkouros/ref-dvgo。