In inspection and display scenarios, reconstructing and rendering the entire surface of a building is a critical step in presenting the overall condition of the building. In building reconstruction, most works are based on point clouds because of their enhanced availability. In recent years, neural radiance fields (NeRF) have become a common function for implementing novel view synthesis. Compared to other traditional 3D graphic methods, NeRF-based models have a solid ability to produce photorealistic images with rich details that point clouds based methods cannot offer. As a result, we decided to investigate the performance of this technique in architectural scenes and look for ways to improve it for more significant scenes. This thesis explores the ability to reconstruct large-field scenes with NeRF-based models. NeRF introduced a fully-connected network to predict the volume density and view-dependent emitted radiance at the special location, which will be projected into an image through classic volume rendering techniques. Due to the limitation of near-field ambiguity and parameterization of unbounded scenes, the original NeRF does not perform well on 360° input view, especially when the inputs are sparse. An inverted sphere parameterization that facilitates free view synthesis is introduced to address this limitation so that the foreground and background views can be trained separately. Besides that, we also compare the performance of tracing different light geometries, ray and cone, respectively. Meanwhile, to generate the reconstructed scene precisely, raw RGB images should be pre-processed to estimate the corresponding camera parameters. Finally, customized camera paths should be prepared to generate the final rendered video. According to our experiments, training foreground and background separately is a promising method to solve practical large-scale scene reconstruction problems. A complete wrap-around view of the target building can be obtained using adjusted camera path parameters. Furthermore, introducing conical frustum casting into the original model also provides an alternative method to implement reconstruction. We named this method mip-NeRF++, which can contribute to the final results to some extent.

The stunning growth of Internet users through Cloud File Sharing (CFS) is raising great concerns about unprecedented cloud security and privacy breach. Also, the recent breakthrough in quantum computing further reinforces this kind of concerns, thus we exploit an efficient solution to guarantee personal privacy and resist quantum attacks in the CFS service. In our solution, we integrate the Attribute-based Access Control/eXtensible Access Control Markup Language (ABAC/XACML) model and the Ciphertext-Policy Attribute-Based Encryption (CP-ABE) into the CFS. To improve the performance of CP-ABE, we make use of an optimization method to convert the ABAC/XACML policy into a Small Policy Matrix (SPM). We further prove that this matrix has small coefficients and generates an all-one reconstruction vector, such that it reduces the cumulative error in lattice cryptosystem to the minimum. By using the SPM, we design a new CP-ABE scheme from Lattice (CP-ABE-L) to prevent the enlargement of error bounds. We also give the optimal estimation of system parameters, which satisfy three lattice-generation conditions to implement a valid Error Proportion Allocation (EPA). Our scheme is proved secure against chosen-plaintext attack with a selective attribute set under the Decision Learning with Errors (DLWE) assumption in the standard model. The performance evaluation and analyses illustrate that our scheme not only has short parameters, but also maintains efficient computation and reasonable storage overloads.@en

The stunning growth of Internet users through Cloud File Sharing (CFS) is raising great concerns about unprecedented cloud security and privacy breach. Also, the recent breakthrough in quantum computing further reinforces this kind of concerns, thus we exploit an efficient solution to guarantee personal privacy and resist quantum attacks in the CFS service. In our solution, we integrate the Attribute-based Access Control/eXtensible Access Control Markup Language (ABAC/XACML) model and the Ciphertext-Policy Attribute-Based Encryption (CP-ABE) into the CFS. To improve the performance of CP-ABE, we make use of an optimization method to convert the ABAC/XACML policy into a Small Policy Matrix (SPM). We further prove that this matrix has small coefficients and generates an all-one reconstruction vector, such that it reduces the cumulative error in lattice cryptosystem to the minimum. By using the SPM, we design a new CP-ABE scheme from Lattice (CP-ABE-L) to prevent the enlargement of error bounds. We also give the optimal estimation of system parameters, which satisfy three lattice-generation conditions to implement a valid Error Proportion Allocation (EPA). Our scheme is proved secure against chosen-plaintext attack with a selective attribute set under the Decision Learning with Errors (DLWE) assumption in the standard model. The performance evaluation and analyses illustrate that our scheme not only has short parameters, but also maintains efficient computation and reasonable storage overloads.@en