We present a large-scale stereo RGB image object pose estimation dataset
named the $\textbf{StereOBJ-1M}$ dataset. The dataset is designed to address
challenging cases such as object transparency, translucency, and specular
reflection, in addition to the common challenges of occlusion, symmetry, and
variations in illumination and environments. In order to collect data of
sufficient scale for modern deep learning models, we propose a novel method for
efficiently annotating pose data in a multi-view fashion that allows data
capturing in complex and flexible environments. Fully annotated with 6D object
poses, our dataset contains over 393K frames and over 1.5M annotations of 18
objects recorded in 182 scenes constructed in 11 different environments. The 18
objects include 8 symmetric objects, 7 transparent objects, and 8 reflective
objects. We benchmark two state-of-the-art pose estimation frameworks on
StereOBJ-1M as baselines for future work. We also propose a novel object-level
pose optimization method for computing 6D pose from keypoint predictions in
multiple images. Project website: this https URL

我们介绍了一个大规模立体 RGB 图像物体姿态估计数据集 StereOBJ-1M，为了解决透明、半透明、反射等复杂场景下的姿态估计问题，并通过一种新颖的多视图注释方法有效地收集了足够规模的数据。该数据集包含超过 393K 帧、超过 1.5M 注释的 18 个物体的全标注 6D 姿态，并在 11 个不同环境中记录的 182 个场景中展示。我们将两种最先进的姿态估计框架作为基线，提出了一种新颖的基于关键点预测的物体级姿态优化方法。

StereOBJ-1M：6D 物体位姿估计的大规模立体图像数据集

StereOBJ-1M: Large-scale Stereo Image Dataset for 6D Object Pose  Estimation

Human pose estimation is the task of localizing body keypoints from still
images. The state-of-the-art methods suffer from insufficient examples of
challenging cases such as symmetric appearance, heavy occlusion and nearby
person. To enlarge the amounts of challenging cases, previous methods augmented
images by cropping and pasting image patches with weak semantics, which leads
to unrealistic appearance and limited diversity. We instead propose Semantic
Data Augmentation (SDA), a method that augments images by pasting segmented
body parts with various semantic granularity. Furthermore, we propose
Adversarial Semantic Data Augmentation (ASDA), which exploits a generative
network to dynamiclly predict tailored pasting configuration. Given
off-the-shelf pose estimation network as discriminator, the generator seeks the
most confusing transformation to increase the loss of the discriminator while
the discriminator takes the generated sample as input and learns from it. The
whole pipeline is optimized in an adversarial manner. State-of-the-art results
are achieved on challenging benchmarks.

提出了基于语义数据增强和对抗学习的人体姿势估计方法，在应对外观对称性、严重遮挡和近距离干扰等多种挑战性情况下，取得了最先进的结果。