google-research-datasets/Objectron
Objectron is a dataset of short, object-centric video clips. In addition, the videos also contain AR session metadata including camera poses, sparse point-clouds and planes. In each video, the camera moves around and above the object and captures it from different views. Each object is annotated with a 3D bounding box. The 3D bounding box describes the object’s position, orientation, and dimensions. The dataset contains about 15K annotated video clips and 4M annotated images in the following categories: bikes, books, bottles, cameras, cereal boxes, chairs, cups, laptops, and shoes
Data is provided in multiple formats—raw videos with protobuf annotations, pre-processed tf.records for TensorFlow/PyTorch, and tf.SequenceExample video sequences—enabling direct integration into ML pipelines without custom preprocessing. The dataset includes pre-trained 3D object detection models released through MediaPipe for shoes, chairs, mugs, and cameras, with evaluation tools built around 3D Intersection-over-Union (IoU) metrics for oriented bounding boxes. Supporting scripts enable Apache Beam processing on Google Cloud and include tutorials for NeRF model training, making it suitable for 6-DoF pose estimation and neural rendering applications.
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