Datasets:

TUL-Poland
/

InLUT3D

x float64 -43.59 32.7	y float64 -50.42 53	z float64 -11.34 22.5	r int64 0 255	g int64 0 255	b int64 0 255
0.041	0.011	1.358	232	233	233
0.041	0.011	1.358	224	225	225
0.041	0.011	1.36	232	233	233
0.041	0.011	1.361	228	229	230
0.041	0.011	1.361	224	225	226
0.041	0.011	1.361	232	233	233
0.041	0.011	1.361	232	233	233
0.041	0.011	1.361	224	225	225
0.041	0.011	1.361	232	233	233
0.041	0.011	1.361	224	225	225
0.041	0.011	1.363	228	229	230
0.041	0.011	1.363	224	225	225
0.041	0.011	1.363	224	225	225
0.041	0.011	1.363	228	229	230
0.041	0.011	1.363	227	228	228
0.041	0.011	1.364	228	229	230
0.041	0.011	1.364	224	225	225
0.052	0.014	1.369	206	205	209
0.052	0.014	1.369	206	205	209
0.052	0.014	1.369	206	205	209
0.052	0.014	1.37	206	205	209
0.052	0.014	1.372	206	205	209
0.052	0.014	1.37	206	205	209
0.052	0.014	1.372	209	206	212
0.052	0.014	1.373	209	206	212
0.052	0.014	1.373	206	205	209
0.052	0.014	1.377	206	205	209
0.067	0.018	1.413	189	181	193
0.067	0.018	1.415	189	181	193
0.067	0.018	1.415	189	181	193
0.067	0.018	1.416	189	181	193
0.067	0.018	1.416	189	181	193
0.078	0.021	1.413	179	174	191
0.078	0.021	1.413	179	174	191
0.078	0.021	1.415	179	174	191
0.078	0.021	1.416	181	173	190
0.078	0.021	1.415	179	174	191
0.078	0.021	1.416	179	174	191
0.085	0.023	1.416	181	180	191
0.085	0.023	1.416	182	179	192
0.085	0.023	1.416	181	180	191
0.085	0.023	1.416	184	179	190
0.096	0.026	1.413	181	179	187
0.096	0.026	1.415	183	178	190
0.096	0.026	1.416	181	180	190
0.096	0.026	1.416	181	179	187
0.096	0.026	1.416	183	178	190
0.096	0.026	1.413	184	177	187
0.096	0.026	1.413	181	179	187
0.096	0.026	1.416	184	177	187
0.096	0.026	1.415	185	175	188
0.096	0.026	1.416	181	179	187
0.096	0.026	1.416	181	179	187
0.107	0.029	1.415	179	175	184
0.108	0.029	1.415	179	175	184
0.107	0.029	1.415	180	175	186
0.108	0.029	1.415	180	173	186
0.107	0.029	1.416	179	175	184
0.108	0.029	1.416	180	173	186
0.107	0.029	1.416	180	175	186
0.108	0.029	1.415	183	173	186
0.108	0.029	1.415	180	173	186
0.111	0.03	1.415	178	173	184
0.111	0.03	1.416	179	174	185
0.119	0.032	1.415	177	174	183
0.119	0.032	1.415	176	172	182
0.119	0.032	1.416	177	170	182
0.119	0.032	1.416	177	170	182
0.119	0.032	1.415	177	170	182
0.119	0.032	1.416	179	171	183
0.119	0.032	1.416	177	170	182
0.125	0.034	1.413	178	170	181
0.125	0.034	1.415	176	173	181
0.125	0.034	1.415	178	170	181
0.125	0.034	1.416	176	173	181
0.125	0.034	1.416	178	170	181
0.13	0.035	1.416	179	171	180
0.13	0.035	1.416	179	171	180
0.13	0.035	1.416	179	171	180
0.136	0.037	1.416	176	172	182
0.137	0.037	1.415	179	170	181
0.137	0.037	1.416	176	172	182
0.137	0.037	1.416	179	170	181
0.14	0.038	1.416	177	176	182
0.14	0.038	1.415	183	173	186
0.14	0.038	1.416	180	173	182
0.148	0.04	1.413	177	174	182
0.148	0.04	1.413	177	174	182
0.148	0.04	1.415	177	174	182
0.148	0.04	1.413	179	172	184
0.149	0.04	1.413	179	172	184
0.148	0.04	1.413	179	172	184
0.149	0.04	1.413	179	172	181
0.148	0.04	1.415	180	174	184
0.149	0.04	1.415	179	172	184
0.151	0.041	1.413	181	172	186
0.151	0.041	1.413	177	173	184
0.151	0.041	1.415	181	172	186
0.151	0.041	1.415	177	173	184
0.151	0.041	1.415	180	171	185

End of preview. Expand in Dataset Viewer.

This resource contains Indoor Lodz University of Technology Point Cloud Dataset (InLUT3D) - a point cloud dataset tailored for real object classification and both semantic and instance segmentation tasks. Comprising of 321 scans, some areas in the dataset are covered by multiple scans. All of them are captured using the Leica BLK360 scanner.

Available categories

The points are divided into 18 distinct categories outlined in the label.yaml file along with their respective codes and colors. Among categories you will find:

ceiling,
floor,
wall,
stairs,
column,
chair,
sofa,
table,
storage,
door,
window,
plant,
dish,
wallmounted,
device,
radiator,
lighting,
other.

Challenges

Several challenges are intrinsic to the presented dataset:

Extremely non-uniform categories distribution across the dataset.
Presence of virtual images, particularly in reflective surfaces, and data exterior to windows and doors.
Occurrence of missing data due to scanning shadows (certain areas were inaccessible to the scanner's laser beam).
High point density throughout the dataset.

Data set structure

The structure of the dataset is the following:

inlut3d.tar.gz/
├─ setup_0/
│  ├─ projection.jpg
│  ├─ segmentation.jpg
│  ├─ setup_0.pts
├─ setup_1/
│  ├─ projection.jpg
│  ├─ segmentation.jpg
│  ├─ setup_1.pts
...

File	Content
projection.jpg	A file containing a spherical projection of a corresponding PTS file.
segmentation.jpg	A file with objects marked with unique colours.
setup_x.pts	A file with point cloud int the textual PTS format.

Point characteristic

Column ID	Description
1	X Cartesian coordinate
2	Y Cartesian coordinate
3	Z Cartesian Coordinate
4	Red colour in RGB space in the range [0, 255]
5	Green colour in RGB space in the range [0, 255]
6	Blue colour in RGB space in the range [0, 255]
7	Category code
8	Instance ID

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Size of downloaded dataset files:

22.9 GB

Size of the auto-converted Parquet files:

22.9 GB

Number of rows:

3,678,158,493