We present a pose adaptive few-shot learning procedure and a two-stage data
interpolation regularization, termed Pose Adaptive Dual Mixup (PADMix), for
single-image 3D reconstruction. While augmentations via interpolating
feature-label pairs are effective in classification tasks, they fall short in
shape predictions potentially due to inconsistencies between interpolated
products of two images and volumes when rendering viewpoints are unknown.
PADMix targets this issue with two sets of mixup procedures performed
sequentially. We first perform an input mixup which, combined with a pose
adaptive learning procedure, is helpful in learning 2D feature extraction and
pose adaptive latent encoding. The stagewise training allows us to build upon
the pose invariant representations to perform a follow-up latent mixup under
one-to-one correspondences between features and ground-truth volumes. PADMix
significantly outperforms previous literature on few-shot settings over the
ShapeNet dataset and sets new benchmarks on the more challenging real-world
Pix3D dataset.

本文提出了一种姿态自适应的 few-shot 学习方法以及两阶段数据插值规则，称为姿态自适应双混合（PADMix），用于单幅图像的 3D 重建，并在 ShapeNet 数据集上实现了显著优于先前文献的结果，并在更具挑战性的实际数据集 Pix3D 上创新了最好的表现。