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The explicit neural radiance field (NeRF) has gained considerable interest
for its efficient training and fast inference capabilities, making it a
promising direction such as virtual reality and gaming. In particular,
PlenOctree (POT)[1], an explicit hierarchical multi-scale octree
representation, has emerged as a structural and influential framework. However,
POT's fixed structure for direct optimization is sub-optimal as the scene
complexity evolves continuously with updates to cached color and density,
necessitating refining the sampling distribution to capture signal complexity
accordingly. To address this issue, we propose the dynamic PlenOctree DOT,
which adaptively refines the sample distribution to adjust to changing scene
complexity. Specifically, DOT proposes a concise yet novel hierarchical feature
fusion strategy during the iterative rendering process. Firstly, it identifies
the regions of interest through training signals to ensure adaptive and
efficient refinement. Next, rather than directly filtering out valueless nodes,
DOT introduces the sampling and pruning operations for octrees to aggregate
features, enabling rapid parameter learning. Compared with POT, our DOT
outperforms it by enhancing visual quality, reducing over $55.15$/$68.84\%$
parameters, and providing 1.7/1.9 times FPS for NeRF-synthetic and Tanks $\&$
Temples, respectively. Project homepage:https://vlislab22.github.io/DOT.
[1] Yu, Alex, et al. "Plenoctrees for real-time rendering of neural radiance
fields." Proceedings of the IEEE/CVF International Conference on Computer
Vision. 2021.
