The early released dataset can be accesible to the link : https://datasets.liris.cnrs.fr/fruitbin-version1
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@@ -165,23 +165,6 @@ The compute step takes the rearranged data as input and processes it for future
The table below provides information about the various generated features. Furthermore, this processed data takes into account the filtering parameters specified in the main.py script, such as the desired level of occlusion. Regarding FruitBin, the scene scenarios are divided into 6000 scenes for training, 2000 scenes for evaluation, and 10000 scenes for testing. In addition, 9 cameras are allocated for training, while 3 cameras are assigned for evaluation and testing, resulting in a total of 15 cameras.
Fruit_i The fruit category being considered
Meta_Gen Metadata describing fruit-specific information such as Scene ID, Camera ID, a list of instance IDs related to the fruit, and associated occlusion rates
BBox 2D bounding boxes
Bbox_3d_Gen 3D bounding boxes
Depth_Gen Depth map data with a resolution of 1280x720
Depth_resized Resized depth map data with a resolution of 640x480 for training
FPS Farthest Point Sampling (FPS) key points for the 1280x720 image used in Pvnet
FPS_resized Resized FPS data with a resolution of 640x480 for training in Pvnet
Instance_Mask Instance mask data with a resolution of 1280x720
Instance_Mask_resized Resized instance mask data with a resolution of 640x480 for training
Labels Instance mask in the Yolov8 format (generated using the 'compute label' script explained below)
Models Meshes of the 8 fruits in a common PLY format
Pose_transformed 6D pose annotations in the PVNet format
RGB_Gen RGB image data with a resolution of 1280x720
RGB_resized Resized RGB image data with a resolution of 640x480
Splitting This folder is only available when the dataset is downloaded online. It contains a list of .txt splitting files for different scenarios, describing the train/eval/test split.
