SUNRGB-D 3D Object Detection Challenge


3D object detection is a fundamental task for scene understanding. In this task, we focus on predicting a 3D bounding box in real world dimension to include an object at its full extent. The test data consist of 2860 newly acquired RGB-D images that ground-truth bounding boxes are not publically available. We use the existing SUNRGB-D dataset as training data. This challenge is hosted with LSUN challenge in CVPR.


Evaluation and submission:

We evaluate 3D object detection by extending the standard evaluation criteria for 2D object detection to 3D. Assuming the box aligns with the gravity direction, we use the 3D intersection over union of the predicted and ground truth boxes for 3D evaluation with 0.25 as the IoU threshold. We use mean average precision as final evaluation metric. Please submit a downloadable link to Shuran Song the result file.

Submission format:

Here you can find an example result: [exampleresult_bathtub.mat] and [Evaluation code]
More details about the data and 3D object detection evaluation please reference to [SUNRGB-D home page]