Omkar2118/datasets huggingface dataset:
3DPotatoTwinDemo
Full name: 3DPotatoTwinDemo

Demo dataset with 5 partial and complete 3D shapes of potato tubers.

### 1_rgbd
This folder contains 3 subfolders with the camera intrinsic files, the RGB and depth images, and the partial point clouds. The files are named according to this structure: `[potato-id]_[data-type]_[ycoord].[extension]`. Note that the ycoord is the y-coordinate of the center of the bounding box (bbc) of the annotated potato tuber in reversed order:`[img_height - y_bbc]`. 

```plaintxt
1_rgbd/
|-- 0_camera_intrinsics
|   |-- realsense_d405_camera_intrinsic.json
|   |-- realsense_d405_settings_harvester.json
|-- 1_image
|   |-- 2R2-8
|   |   |-- 2R2-8_depth_098.png
|   |   |-- 2R2-8_rgb_098.png
|   |   |-- ...
|   |-- ...
|-- 2_pcd
|   |-- 2R2-8
|   |   |-- 2R2-8_pcd_098.ply
|   |   |-- ...
|   |-- ...
```

Note that the RGB images have an alpha channel with the mask annotation. To extract the RGB and mask channels individually please use this code:
```python
import cv2
rgba = cv2.imread("./1_rgbd/1_image/2R2-8/2R2-8_rgb_098.png", cv2.IMREAD_UNCHANGED)
rgb = rgba[:,:,:-1]
mask = rgba[:,:,-1]
```
<br/>

### 2_sfm
This folder contains 2 subfolders with the reconstructed 3D meshes from the Structure-from-Motion method and the downsampled point clouds of these meshes (containing 10000, 20000, and 30000 points respectively).

```plaintxt
2_sfm/
|-- 1_mesh
|   |-- 2R2-8
|   |   |-- 2R2-8.jpg
|   |   |-- 2R2-8.mtl
|   |   |-- 2R2-8.obj
|   |-- ...
|-- 2_pcd
|   |-- 2R2-8
|   |   |-- 2R2-8_10000.ply
|   |   |-- 2R2-8_20000.ply
|   |   |-- 2R2-8_30000.ply
|   |-- ...
```
The meshes can be visualized in Open3D:
```python
import open3d as o3d
mesh = o3d.io.read_triangle_mesh("./2_sfm/1_mesh/2R2-8/2R2-8.obj", enable_post_processing=True, print_progress=False)
o3d.visualization.draw_geometries([mesh], window_name="mesh")
```
<br/>

### 3_pair
This folder contains 1 subfolder with the transformation matrices to overlay the partial point cloud with the 3D mesh. Please refer to the **transform.py** file.
Inter-X

Inter-X is a large-scale dataset containing ~11K interaction sequences, more than 8.1M frames and 34K fine-grained human textual descriptions.

Introducing paper: Inter-X: Towards Versatile Human-Human Interaction Analysis
RFUND
Full name: Revised FUNSD and XFUND

RFUND is a relabeled version of FUNSD and XFUND datasets, tackling the following issues in their original annotations:

1. Entity (block) level OCR results. Real-world OCR engines usually produce line-level results, while the annotations in FUNSD and XFUND are at the entity (block) level. Text lines within the same entity are aggregated and serialized in human reading order, simplifying the task scope and failing to reflect real-world challenges.
2. Inconsistent labelling granularity. In FUNSD, while most contents are annotated at the entity level, multi-line entities with first-line indentation are annotated separately, in which the first line is split out and the rest are aggregated. XFUND exhibits variable granularity in annotations, with some contents labelled at the entity level and others at the line level. Such inconsistent labelling standards can hinder model training.
3. Erroneous category annotations. Entities in FUNSD/XFUND are categorized as "header", "question", "answer" and "other". We observed that certain entities in both FUNSD and XFUND have category labels that differ from human understanding. In the following example, the answer entity “Client confirmed agreement ...” was labelled as other, while the other entity “CONFIDENTIAL” was labelled as the question.

Introducing paper: PEneo: Unifying Line Extraction, Line Grouping, and Entity Linking for End-to-end Document Pair Extraction