# The Cube Surface Light Field for Interactive Free-Viewpoint Rendering
## 5 Results and Discussion
### Time Performance  
To evaluate the time performance of the proposed method, we use a cube surface light field of the Cornell Box with a resolution of \(64\times 64\times 256\times 256\) for all of our experiments. The under-sampled light rays are all processed by the CBSI, and the images with the resolutions of \(512\times 512\), \(1024\times 1024\), \(2048\times 2048\), \(4096\times 4096\) are rendered for this evaluation. We count the frame rate of generating 100 novel views using our interactive rendering as well as the path tracing rendering with the corresponding resolutions. The comparisons of these two kinds of frame rates are shown in Figure 7. It is worth noting that the frame rate of the path tracing rendering refers to the time consumption when each pixel represents by only one light ray. Although the configuration in the path tracing rendering will lead to aliasing artifacts, this evaluation only considers the time performance of different methods. It can be seen that different rendering resolutions have little influence on the frame rate. With the improvement of rendering resolution, the frame rate does not decrease or increase significantly. This is because 3D texture mapping is implemented in GPU with parallel acceleration. Under the same rendering resolution, our method can get a higher frame rate than the path tracing rendering, and the highest frame rate reaches more than 250 FPS. In contrast, the average frame rate of the path tracing rendering is about 30 FPS, indicating that our method gets better time performance. This is because our rendering  
Figure 6: The distributions of light ray in two different coordinate systems. (**a**) a single ray in CCS, (**b**) a single ray in PCS, (**c**) 2 PP light field in CCS, (**d**) 2 PP light field in PCS, (**e**) the cube surface light field in CCS, (**f**) the cube surface light field in PCS. only requires querying light rays from the pre-sampled cube surface light field, and the complexity for the light ray query is O(1) by using compressed 3D texture mapping. At the same time, we also count the frame rate when using different interpolation methods (NNI, LI, BLI, BCI, and CBSI) to render novel views with a fixed image resolution of \(2048\times 2048\) pixels. As can be seen from Figure 8, different interpolation methods have an obvious influence on the rendering frame rate, and more complex interpolation methods will appropriately reduce the frame rate of interactive novel view rendering. This is because the complex interpolation algorithm requires blending more light rays, which increases the computation consumption. When the CBSI is used for processing the under-sampled light rays, the average frame rate of the proposed method is about 75 FPS (the lowest average frame rate), i.e., the worst-case can still meet the real-time requirement for human eyes. This is due to the simplicity of our single-layer ray casting algorithm and the GPU parallel acceleration of the light ray query, making the rendering in real-time. In this evaluation, the memory usage of our method is about 384 MB for the cube surface light field images, while the memory usage of the path tracing rendering is almost negligible because of the geometric simplicity of the Cornell Box scene. Our method uses the cube surface light field to encode the whole scene's geometry and appearance. In contrast to traditional mesh-based methods, our method does not depend on the geometric complexity of the scene, i.e., the geometric complexity of the scene can hardly influence the rendering efficiency. However, the traditional mesh-based methods must consider the rendering overhead caused by various scenes' geometric structures. Moreover, mesh-based large scene rendering is still a problem to be further studied. Compared with the learning-based methods, our method can achieve interactive free-viewpoint rendering.