Most self-supervised 6D object pose estimation methods can only work with
additional depth information or rely on the accurate annotation of 2D
segmentation masks, limiting their application range. In this paper, we propose
a 6D object pose estimation method that can be trained with pure RGB images
without any auxiliary information. We first obtain a rough pose initialization
from networks trained on synthetic images rendered from the target's 3D mesh.
Then, we introduce a refinement strategy leveraging the geometry constraint in
synthetic-to-real image pairs from multiple different views. We formulate this
geometry constraint as pixel-level flow consistency between the training images
with dynamically generated pseudo labels. We evaluate our method on three
challenging datasets and demonstrate that it outperforms state-of-the-art
self-supervised methods significantly, with neither 2D annotations nor
additional depth images.

无需辅助信息，我们提出了一种使用纯 RGB 图像进行训练的 6D 物体姿态估计方法，通过利用来自多个不同视角的合成至实图像对中的几何约束（以像素级流一致性的形式表示）进行精化策略，从合成图像中获得初始姿态估计，并使用动态生成的伪标签。在三个具有挑战性的数据集上进行评估，结果表明我们的方法明显优于最先进的自监督方法，无需 2D 注释或额外的深度图像。

自监督 6D 物体姿态估计中的伪流一致性

Pseudo Flow Consistency for Self-Supervised 6D Object Pose Estimation

Depth estimation and semantic segmentation play essential roles in scene
understanding. The state-of-the-art methods employ multi-task learning to
simultaneously learn models for these two tasks at the pixel-wise level. They
usually focus on sharing the common features or stitching feature maps from the
corresponding branches. However, these methods lack in-depth consideration on
the correlation of the geometric cues and the scene parsing. In this paper, we
first introduce the concept of semantic objectness to exploit the geometric
relationship of these two tasks through an analysis of the imaging process,
then propose a Semantic Object Segmentation and Depth Estimation Network
(SOSD-Net) based on the objectness assumption. To the best of our knowledge,
SOSD-Net is the first network that exploits the geometry constraint for
simultaneous monocular depth estimation and semantic segmentation. In addition,
considering the mutual implicit relationship between these two tasks, we
exploit the iterative idea from the expectation-maximization algorithm to train
the proposed network more effectively. Extensive experimental results on the
Cityscapes and NYU v2 dataset are presented to demonstrate the superior
performance of the proposed approach.

通过引入语义物体性概念分析成像过程，本文提出了一种基于物体性假设的语义物体分割和深度估计网络（SOSD-Net），并采用期望最大化算法的迭代思想更有效地训练该网络，实验结果表明该方法具有卓越的性能。