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%。

基于合成数据的鲁棒性类别层次 3D 姿态估计

Robust Category-Level 3D Pose Estimation from Synthetic Data

Compared to 2D object bounding-box labeling, it is very difficult for humans
to annotate 3D object poses, especially when depth images of scenes are
unavailable. This paper investigates whether we can estimate the object poses
effectively when only RGB images and 2D object annotations are given. To this
end, we present a two-step pose estimation framework to attain 6DoF object
poses from 2D object bounding-boxes. In the first step, the framework learns to
segment objects from real and synthetic data in a weakly-supervised fashion,
and the segmentation masks will act as a prior for pose estimation. In the
second step, we design a dual-scale pose estimation network, namely
DSC-PoseNet, to predict object poses by employing a differential renderer. To
be specific, our DSC-PoseNet firstly predicts object poses in the original
image scale by comparing the segmentation masks and the rendered visible object
masks. Then, we resize object regions to a fixed scale to estimate poses once
again. In this fashion, we eliminate large scale variations and focus on
rotation estimation, thus facilitating pose estimation. Moreover, we exploit
the initial pose estimation to generate pseudo ground-truth to train our
DSC-PoseNet in a self-supervised manner. The estimation results in these two
scales are ensembled as our final pose estimation. Extensive experiments on
widely-used benchmarks demonstrate that our method outperforms state-of-the-art
models trained on synthetic data by a large margin and even is on par with
several fully-supervised methods.

本文基于 2D 物体注释和 RGB 图像提出了一个双步骤的姿态估计框架，该框架使用 DSC-PoseNet 网络实现了基于差分渲染器的姿态估计，同时还利用自我监督学习生成伪标签进行训练，实验表明该方法在真实数据集上性能优于基于合成数据集的现有模型。