论文标题
Transpr：透明射线蓄积神经3D场景点渲染器
TRANSPR: Transparency Ray-Accumulating Neural 3D Scene Point Renderer
论文作者
论文摘要
我们建议并评估一种基于神经点的图形方法，该方法可以对半透明的场景部分进行建模。与其前身管道类似，我们的速度使用点云来对代理几何形状进行建模，并使用神经描述符增加每个点。此外，在我们的每个点的方法中都引入了可学习的透明度值。 我们的神经渲染程序包括两个步骤。首先，使用射线分组为多通道图像进行栅格化。接下来是使用可学习的卷积网络将栅格化图像“转换”到RGB输出中的神经渲染步骤。可以使用基于梯度的神经描述符和渲染网络的优化对新场景进行建模。 我们表明，在训练我们的方法后，可以产生半透明的点云场景的新颖观点。我们的实验证明了将半透明度引入基于神经点的建模的好处，以适用于具有半透明部分的一系列场景。
We propose and evaluate a neural point-based graphics method that can model semi-transparent scene parts. Similarly to its predecessor pipeline, ours uses point clouds to model proxy geometry, and augments each point with a neural descriptor. Additionally, a learnable transparency value is introduced in our approach for each point. Our neural rendering procedure consists of two steps. Firstly, the point cloud is rasterized using ray grouping into a multi-channel image. This is followed by the neural rendering step that "translates" the rasterized image into an RGB output using a learnable convolutional network. New scenes can be modeled using gradient-based optimization of neural descriptors and of the rendering network. We show that novel views of semi-transparent point cloud scenes can be generated after training with our approach. Our experiments demonstrate the benefit of introducing semi-transparency into the neural point-based modeling for a range of scenes with semi-transparent parts.
