1,732 research outputs found
Neural 3D Mesh Renderer
For modeling the 3D world behind 2D images, which 3D representation is most
appropriate? A polygon mesh is a promising candidate for its compactness and
geometric properties. However, it is not straightforward to model a polygon
mesh from 2D images using neural networks because the conversion from a mesh to
an image, or rendering, involves a discrete operation called rasterization,
which prevents back-propagation. Therefore, in this work, we propose an
approximate gradient for rasterization that enables the integration of
rendering into neural networks. Using this renderer, we perform single-image 3D
mesh reconstruction with silhouette image supervision and our system
outperforms the existing voxel-based approach. Additionally, we perform
gradient-based 3D mesh editing operations, such as 2D-to-3D style transfer and
3D DeepDream, with 2D supervision for the first time. These applications
demonstrate the potential of the integration of a mesh renderer into neural
networks and the effectiveness of our proposed renderer
DDSL: Deep Differentiable Simplex Layer for Learning Geometric Signals
We present a Deep Differentiable Simplex Layer (DDSL) for neural networks for
geometric deep learning. The DDSL is a differentiable layer compatible with
deep neural networks for bridging simplex mesh-based geometry representations
(point clouds, line mesh, triangular mesh, tetrahedral mesh) with raster images
(e.g., 2D/3D grids). The DDSL uses Non-Uniform Fourier Transform (NUFT) to
perform differentiable, efficient, anti-aliased rasterization of simplex-based
signals. We present a complete theoretical framework for the process as well as
an efficient backpropagation algorithm. Compared to previous differentiable
renderers and rasterizers, the DDSL generalizes to arbitrary simplex degrees
and dimensions. In particular, we explore its applications to 2D shapes and
illustrate two applications of this method: (1) mesh editing and optimization
guided by neural network outputs, and (2) using DDSL for a differentiable
rasterization loss to facilitate end-to-end training of polygon generators. We
are able to validate the effectiveness of gradient-based shape optimization
with the example of airfoil optimization, and using the differentiable
rasterization loss to facilitate end-to-end training, we surpass state of the
art for polygonal image segmentation given ground-truth bounding boxes
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