  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.
