## 4 Experiments
### Experimental and Performance Analysis of BiResNeRF
#### 4.3.2 Implementation Details  
The architecture of BiResNeRF is shown in Section 3.2, and the specific parameters of the network structure are shown in Table 1. We use the Adam optimizer and adopt different learning rates and learning rate scheduling strategies for pose parameters and neural radiance field parameters. In the first stage for pose parameters, the learning rate decays from 1 \(\times\) 10\({}^{-2}\) to 1 \(\times\) 10\({}^{-5}\) using an exponential decay scheduling strategy with a decay factor of 0.999827. For the neural radiance field parameters in the low-resolution module, the learning rate is set from 1 \(\times\) 10\({}^{-2}\) to 1 \(\times\) 10\({}^{-4}\), using an exponential decay learning rate scheduling strategy with a decay coefficient of 0.999884. The neural radiance field parameters in the high-resolution module maintain a low learning rate of 0.0005(1 \(\times\) 10\({}^{-2}\)/20). In the second stage, the low-resolution module remains unchanged, while the high-resolution module employs a smooth warm-up learning rate scheduling strategy, raising the learning rate to 0.003(0.3 \(\times\) 1 \(\times\) 10\({}^{-2}\)) and then decreasing it to 1 \(\times\) 10\({}^{-4}\). Moreover, for the Lego and Hotdog scenes, some parameters are adjusted. The initial learning rate for the pose is set to 3 \(\times\) 10\({}^{-3}\). The initial learning rate for the low-resolution neural radiance field parameters in the first phase is 5 \(\times\) 10\({}^{-3}\), and the highest learning rate in the second stage for the high resolution is 9 \(\times\) 10\({}^{-4}\)(0.3 \(\times\) 3 \(\times\) 10\({}^{-3}\)). The training was performed for 40,000 iterations for each scene.  
The algorithm compared to ours is BAA-NGP. This algorithm currently offers the best scene reconstruction results using multi-resolution hash encoding in situations with inaccurate poses. Since the original experimental environment is different from ours, which may lead to variations in the results, we decided to evaluate the BAA-NGP algorithm under identical hardware and software environments and with the same perturbation parameters. The experimental details follow the settings in the BAA-NGP [33] paper. Specifically, we use the Adam optimizer and an exponential decay scheduling strategy. The learning rate for pose parameters changes from 1 \(\times\) 10\({}^{-3}\) to 1 \(\times\) 10\({}^{-5}\), and the learning rate for neural radiance field parameters changes from 1 \(\times\) 10\({}^{-2}\) to 1 \(\times\) 10\({}^{-4}\). Additionally, when processing the Materials and Ship data, we adjust the learning rate for pose parameters from 1 \(\times\) 10\({}^{-2}\) to 1 \(\times\) 10\({}^{-5}\) during training.  
Furthermore, for a more comprehensive benchmark test, comparisons were made with the pose optimization parts of BARF [24] and NeRFAcc [32] in MLP-based NeRFs. Parameter settings were referenced from the literature, and the same initial conditions as in  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Parameter** & **Symbol** & **Value** \\ \hline \multirow{2}{*}{Common parameters} & Number of features Dimensions per entry & F & 2 \\  & Hash table size & T & 219 \\ \hline \multirow{3}{*}{Pose estimation module} & Coarsest resolution & \(N_{min}\) & 16 \\  & Finest resolution & \(N_{max}\) & 72 \\  & Number of levels & \(L_{res}\) & 3 \\ \hline \multirow{3}{*}{High-resolution reconstruction module} & Coarsest resolution & \(N_{min}\) & 128 \\  & Finest resolution & \(N_{max}\) & 4096 \\ \cline{1-1}  & Number of levels & \(L_{res}\) & 16 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the network structure.  
this paper were set, specifically choosing a random seed of 3. The training was conducted for 200,000 iterations.