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The explicit neural radiance field (NeRF) has gained considerable interestfor its efficient training and fast inference capabilities, making it apromising direction such as virtual reality and gaming. In particular,PlenOctree (POT)[1], an explicit hierarchical multi-scale octreerepresentation, has emerged as a structural and influential framework. However,POT's fixed structure for direct optimization is sub-optimal as the scenecomplexity evolves continuously with updates to cached color and density,necessitating refining the sampling distribution to capture signal complexityaccordingly. To address this issue, we propose the dynamic PlenOctree DOT,which adaptively refines the sample distribution to adjust to changing scenecomplexity. Specifically, DOT proposes a concise yet novel hierarchical featurefusion strategy during the iterative rendering process. Firstly, it identifiesthe regions of interest through training signals to ensure adaptive andefficient refinement. Next, rather than directly filtering out valueless nodes,DOT introduces the sampling and pruning operations for octrees to aggregatefeatures, enabling rapid parameter learning. Compared with POT, our DOToutperforms 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 radiancefields." Proceedings of the IEEE/CVF International Conference on ComputerVision. 2021.

Dynamic PlenOctree for Adaptive Sampling Refinement in Explicit NeRF