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）彻底革新了新视角合成领域，展现出卓越的性能。然而，反射物体的建模与渲染仍具挑战性。近期方法在处理反射场景时虽较基线有显著改进，但牺牲了效率。本研究旨在平衡效率与质量。为此，我们探索了一种基于传统体渲染的隐式-显式混合方法，以提升重建质量并加速训练与渲染过程。我们采用高效的基于密度的网格表示，并在流程中重新参数化反射辐射度。所提出的反射感知方法相较竞争方法实现了具有竞争力的质量-效率权衡。基于实验结果，我们提出并讨论了影响基于密度的反射物体重建方法结果的因素假设。源代码可从以下地址获取：https://github.com/gkouros/ref-dvgo