Advances in deep learning techniques have allowed recent work to reconstruct
the shape of a single object given only one RBG image as input. Building on
common encoder-decoder architectures for this task, we propose three
extensions: (1) ray-traced skip connections that propagate local 2D information
to the output 3D volume in a physically correct manner; (2) a hybrid 3D volume
representation that enables building translation equivariant models, while at
the same time encoding fine object details without an excessive memory
footprint; (3) a reconstruction loss tailored to capture overall object
geometry. Furthermore, we adapt our model to address the harder task of
reconstructing multiple objects from a single image. We reconstruct all objects
jointly in one pass, producing a coherent reconstruction, where all objects
live in a single consistent 3D coordinate frame relative to the camera and they
do not intersect in 3D space. We also handle occlusions and resolve them by
hallucinating the missing object parts in the 3D volume. We validate the impact
of our contributions experimentally both on synthetic data from ShapeNet as
well as real images from Pix3D. Our method improves over the state-of-the-art
single-object methods on both datasets. Finally, we evaluate performance
quantitatively on multiple object reconstruction with synthetic scenes
assembled from ShapeNet objects.

该论文的研究结果表明在深度学习和编码器 - 解码器架构的帮助下，使用光线追踪跳跃连接和混合的 3D 模型表示技术可以从单个图像中重构出 3D 物体模型，并且同样的技术可以从单张图像中重构出多个 3D 物体模型，并处理遮挡问题。