Australian Supermarket Object Set (ASOS): A Benchmark Dataset of Physical Objects and 3D Models for Robotics and Computer Vision
This provides a cost-effective and accessible benchmark for researchers in robotics and computer vision, though it is incremental as it builds on existing dataset efforts.
The paper tackles the lack of accessible real-world datasets for robotics and computer vision by introducing the Australian Supermarket Object Set (ASOS), a dataset of 50 common supermarket items with high-quality 3D meshes, designed for benchmarking tasks like object detection and pose estimation.
This paper introduces the Australian Supermarket Object Set (ASOS), a comprehensive dataset comprising 50 readily available supermarket items with high-quality 3D textured meshes designed for benchmarking in robotics and computer vision applications. Unlike existing datasets that rely on synthetic models or specialized objects with limited accessibility, ASOS provides a cost-effective collection of common household items that can be sourced from a major Australian supermarket chain. The dataset spans 10 distinct categories with diverse shapes, sizes, and weights. 3D meshes are acquired by a structure-from-motion techniques with high-resolution imaging to generate watertight meshes. The dataset's emphasis on accessibility and real-world applicability makes it valuable for benchmarking object detection, pose estimation, and robotics applications.