  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: https://sites.google.com/view/stereobj-1m.
