Obtaining accurate 3D object poses is vital for numerous computer vision
applications, such as 3D reconstruction and scene understanding. However,
annotating real-world objects is time-consuming and challenging. While
synthetically generated training data is a viable alternative, the domain shift
between real and synthetic data is a significant challenge. In this work, we
aim to narrow the performance gap between models trained on synthetic data and
few real images and fully supervised models trained on large-scale data. We
achieve this by approaching the problem from two perspectives: 1) We introduce
SyntheticP3D, a new synthetic dataset for object pose estimation generated from
CAD models and enhanced with a novel algorithm. 2) We propose a novel approach
(CC3D) for training neural mesh models that perform pose estimation via inverse
rendering. In particular, we exploit the spatial relationships between features
on the mesh surface and a contrastive learning scheme to guide the domain
adaptation process. Combined, these two approaches enable our models to perform
competitively with state-of-the-art models using only 10% of the respective
real training images, while outperforming the SOTA model by 10.4% with a
threshold of pi/18 using only 50% of the real training data. Our trained model
further demonstrates robust generalization to out-of-distribution scenarios
despite being trained with minimal real data.

本篇论文提出 SyntheticP3D 数据集和 CC3D 方法，结合这两种方法可以实现在只用 10% 的真实数据的情况下，与当前最先进的模型在物体位姿估计方面取得了可与之媲美结果，在采用 50% 的真实数据时还超越了 SOTA 模型 10.4%。