Accurate 6D pose estimation of complex objects in 3D environments is essential for effective robotic manipulation. Yet, existing benchmarks fall short in evaluating6D pose estimation methods under realistic industrial conditions, as most datasets focus on household objects in domestic settings, while the few available industrialdatasets are limited to artificial setups with objects placed on tables. To bridge this gap, we introduce CHIP, the first dataset designed for 6D pose estimation of chairsmanipulated by a robotic arm in a real-world industrial environment. CHIP includes seven distinct chairs captured using three different RGBD sensing technologies andpresents unique challenges, such as distractor objects with fine-grained differences and severe occlusions caused by the robotic arm and human operators. CHIP comprises77,811 RGBD images annotated with ground-truth 6D poses automatically derived from the robot’s kinematics, averaging 11,115 annotations per chair. Webenchmark CHIP using three zero-shot 6D pose estimation methods, assessing performance across different sensor types, localization priors, and occlusion levels.Results show substantial room for improvement, highlighting the unique challenges posed by the dataset. CHIP will be publicly released.