Implicit neural representation (INR), in combination with geometric
rendering, has recently been employed in real-time dense RGB-D SLAM. Despite
active research endeavors being made, there lacks a unified protocol for fair
evaluation, impeding the evolution of this area. In this work, we establish, to
our knowledge, the first open-source benchmark framework to evaluate the
performance of a wide spectrum of commonly used INRs and rendering functions
for mapping and localization. The goal of our benchmark is to 1) gain an
intuition of how different INRs and rendering functions impact mapping and
localization and 2) establish a unified evaluation protocol w.r.t. the design
choices that may impact the mapping and localization. With the framework, we
conduct a large suite of experiments, offering various insights in choosing the
INRs and geometric rendering functions: for example, the dense feature grid
outperforms other INRs (e.g. tri-plane and hash grid), even when geometric and
color features are jointly encoded for memory efficiency. To extend the
findings into the practical scenario, a hybrid encoding strategy is proposed to
bring the best of the accuracy and completion from the grid-based and
decomposition-based INRs. We further propose explicit hybrid encoding for
high-fidelity dense grid mapping to comply with the RGB-D SLAM system that puts
the premise on robustness and computation efficiency.

建立了第一个开放共享的基准框架来评估常用的 INRs 和几何渲染函数在建图和定位中的性能，通过大量的实验研究，得出了密集特征网格优于其他 INRs（如三平面和哈希网格）的结论，并提出了一种混合编码策略来兼顾准确性和完整性，以满足 RGB-D SLAM 系统对鲁棒性和计算效率的要求。