## 1 Introduction  
In computer vision, the advent of NeRF (neural radiance fields) technology has enabled photo-realistic rendering, greatly advancing the development of more lifelike virtual environments. This technology shows great potential in various fields, including urban digitization [1, 2], autonomous driving [3, 4] and the creation of virtual characters [5, 6, 7]. Particularly in virtual reality (VR), the application of NeRF technology has enhanced the realism of scenes, providing users with a deeper immersive experience. Research combining NeRF and VR technologies continues to progress. For instance, FoV-NeRF [8] significantly reduces latency by incorporating eye-tracking technology, improving perceptual quality in environments with high fields of view and resolution. The Re-ReND [9] method optimizesthe rendering of neural radiance fields in VR devices, achieving high efficiency and minimal quality loss. Additionally, VR technology has extended into fields such as medicine, education and psychology. For example, the application of VR and AR in ophthalmic diagnosis and screening has shown their potential in disease diagnostics [10]. These technologies also play a crucial role in professional skills training, for example, in the training of nursing students [11], otolaryngology surgeons [12] and adults with autism spectrum disorders [13], where VR significantly enhances participants' confidence levels. In conclusion, with the ongoing development and application of NeRF technology, work related to more immersive scenes is steadily improving, revealing its vast potential and value in engineering science. In the NeRF-based method, the photo-realistic rendering heavily relies on precise 3D scene reconstruction. However, the information usually recorded in the photographing process is the scene's photo-metric information, not the camera's pose, which means that it does not include the specific location and orientation of the camera when each photo is taken. Therefore, further estimation of the camera pose is required. Currently, there are two main methods for estimating camera pose in NeRF: the traditional method based on feature point matching and the joint pose optimization method based on photo-metric information between images. Nevertheless, these two methods have the following problems:  
1. The traditional method of camera pose estimation relies on detecting and matching feature points. However, this method encounters significant difficulties in scenes lacking obvious features [14], such as low textures or reflections. The lack of sufficient feature points in these scenes makes it difficult for traditional algorithms to accurately calculate the camera pose, thereby affecting the quality of the entire 3D scene reconstruction. 2. Although the joint pose optimization method can have better effects on areas with low texture, this method is currently mainly based on the Multilayer Perceptron (MLP) model, which is slow to converge and requires more processing time and resources. This poses a significant limitation for the research and application of such algorithms. To address the aforementioned issues, this paper introduces multi-resolution hash encoding within the framework of the joint pose optimization method. This improved approach not only has more significant advantages in scenarios with low textures and reflections but also allows for more efficient pose adjustment and reduced reconstruction time. Specifically, this paper proposes a scene reconstruction method named BiResNeRF, which employs a neural network architecture with a fusion module of hash encoding at two different resolutions. Furthermore, a two-stage training strategy is adopted based on this architecture. In the strategy, low-resolution and high-resolution hash encoding modules are, respectively, responsible for pose estimation and high-precision scene reconstruction during the training. This method not only ensures the quality of the scene reconstruction but also achieves a more efficient reconstruction process.