Category-level pipe pose and size estimation via geometry-aware adaptive curvature convolution

Pipe pose estimation provides crucial positional information for robots, enhancing assembly efficiency and precision, while its accuracy critically impacts the final product's reliability and quality. To handle unseen pipes, we propose a category-level pipe pose and size estimation network via Normalized Object Coordinate Space (NOCS) representation. Given an RGB image and its corresponding depth map, our network predicts class labels, bounding boxes and instance masks for detection, as well as NOCS maps for pose estimation. Then these predictions are aligned with the depth map to estimate pipe's pose and size. To better extract complex and variable pipe morphology, geometry-aware adaptive curvature convolution is introduced to dynamically adapt to the slender structure and improve segmentation performance. Facing the lack of pipe pose datasets with enough instances, pose, clutter, occlusion, and illumination variation, we propose a novel domain randomization mixed reality approach to efficiently generate synthetic data, which addresses the limitations of training datasets, making data generation more time- and effort-efficient. Experimental results demonstrate that our Geometry-Aware Adaptive Convolutional Network (GACNet) outperforms other methods and robustly estimates the pose and size of unseen pipes in real-world environments.