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（NeRFs）已彻底改变了新视角合成领域，展现出卓越的性能。然而，反射物体的建模与渲染仍然是具有挑战性的问题。近期方法在处理反射场景方面相比基线取得了显著改进，但代价是牺牲了效率。在本工作中，我们旨在平衡效率与质量。为此，我们基于传统体渲染研究了一种隐式-显式结合的方法，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，并在流程中对反射亮度进行重新参数化。所提出的反射感知方法在质量-效率权衡方面达到了与竞争方法相媲美的水平。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结果的因素相关假设。源代码见：https://github.com/gkouros/ref-dvgo