We tackle the problem of automatically reconstructing a complete 3D model of
a scene from a single RGB image. This challenging task requires inferring the
shape of both visible and occluded surfaces. Our approach utilizes
viewer-centered, multi-layer representation of scene geometry adapted from
recent methods for single object shape completion. To improve the accuracy of
view-centered representations for complex scenes, we introduce a novel
"Epipolar Feature Transformer" that transfers convolutional network features
from an input view to other virtual camera viewpoints, and thus better covers
the 3D scene geometry. Unlike existing approaches that first detect and
localize objects in 3D, and then infer object shape using category-specific
models, our approach is fully convolutional, end-to-end differentiable, and
avoids the resolution and memory limitations of voxel representations. We
demonstrate the advantages of multi-layer depth representations and epipolar
feature transformers on the reconstruction of a large database of indoor
scenes.

此研究论文研究如何从单个 RGB 图像自动重建场景的完整 3D 模型，它采用一种新的 Epipolar Feature Transformer 技术来联合图像的多层深度表示和卷积神经网络特征以提高场景信息的准确性。