Scalable-Neural-Indoor-Scene-Rendering是一种基于深度学习的图像渲染技术,旨在为室内场景提供高质量的渲染效果。这种技术通过使用神经网络模型来分析输入图像中的物体、纹理和颜色等信息,然后生成与原始图像相似的高质量渲染图。 与传统的图像渲染方法相比,Scalable-Neural-Indoor-Scene-Rendering具有更高的精度和更广泛的...
These three contributions together lead to a novel system that produces highly realistic rendering results with various reflections. The rendering quality outperforms state-of-the-art IBR or neural rendering algorithms considerably. 展开 关键词: Image-based rendering Two-layer mesh Reflection Super-...
Over the last few years, implicit 3D representation has attracted more and more research endeavors, typified by the so-called Neural Radiance Fields (NeRF). The original NeRF and some relevant variants mostly address on small-scale scene (such ...
Dist: Rendering deep implicit signed distance function with differentiable sphere tracing. In CVPR, 2020. 2 [23] Ricardo Martin-Brualla, Noha Radwan, Mehdi SM Sajjadi, Jonathan T Barron, Alexey Dosovitskiy, and Daniel Duck- worth. Nerf in the wild: Neural radiance fields for unco...
Next, they divide this image into an 8x8 grid and calculate the probability distribution from the rendering losses. This means that if the resolution of an image is 1200x680 (Replica), only around 3 pixels are sampled to calculate the distribution for a 150x85 grid patch. This is not too...
1. Introduction Recent advances in neural rendering techniques have lead to significant progress towards photo-realistic novel view synthesis, a prerequisite towards many VR and AR applications. In particular, Neural Radiance Fields (NeRFs) [24] have attracted s...
Firstly, fast camera motion causes significant rotation, rendering the optimization of camera pose highly nonlinear. When seeking to optimize the pose using gradient descent, it is easy for the optimization to become trapped in a local optimum. Secondly, fast camera motion can lead to serious ...
Firstly, fast camera motion causes significant rotation, rendering the optimization of camera pose Drones 2023, 7, 358 4 of 20 highly nonlinear. When seeking to optimize the pose using gradient descent, it is easy for the optimization to become trapped in a local optimum. Secondly, fast camera...