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Neural Radiance Fields (NeRFs) have revolutionized the field of novel viewsynthesis, demonstrating remarkable performance. However, the modeling andrendering of reflective objects remain challenging problems. Recent methodshave shown significant improvements over the baselines in handling reflectivescenes, albeit at the expense of efficiency. In this work, we aim to strike abalance between efficiency and quality. To this end, we investigate animplicit-explicit approach based on conventional volume rendering to enhancethe reconstruction quality and accelerate the training and rendering processes.We adopt an efficient density-based grid representation and reparameterize thereflected radiance in our pipeline. Our proposed reflection-aware approachachieves a competitive quality efficiency trade-off compared to competingmethods. Based on our experimental results, we propose and discuss hypothesesregarding the factors influencing the results of density-based methods forreconstructing reflective objects. The source code is available athttps://github.com/gkouros/ref-dvgo.

Ref-DVGO: Reflection-Aware Direct Voxel Grid Optimization for an Improved Quality-Efficiency Trade-Off in Reflective Scene Reconstruction