kvn420/Tenro_V4.1
Any-to-Any
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Updated
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4
Dataset Viewer
id
stringlengths 12
12
| pcd
stringlengths 20
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| layout
stringlengths 23
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scene0000_00
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pcd/scene0000_00.ply
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layout/scene0000_00.txt
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scene0001_00
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pcd/scene0001_00.ply
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layout/scene0001_00.txt
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scene0001_01
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pcd/scene0001_01.ply
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layout/scene0001_01.txt
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scene0002_00
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pcd/scene0002_00.ply
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scene0003_00
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scene0004_01
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scene0006_00
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scene0039_00
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scene0050_00
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scene0055_00
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End of preview. Expand
in Data Studio
SpatialLM Testset
Project page | Paper | Code
We provide a test set of 107 preprocessed point clouds and their corresponding GT layouts, point clouds are reconstructed from RGB videos using MASt3R-SLAM. SpatialLM-Testset is quite challenging compared to prior clean RGBD scan datasets due to the noises and occlusions in the point clouds reconstructed from monocular RGB videos.
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Folder Structure
Outlines of the dataset files:
project-root/
├── pcd/*.ply # Reconstructed point cloud PLY files
├── layout/*.txt # GT FloorPlan Layout
├── benchmark_categories.tsv # Category mappings for evaluation
└── test.csv # Metadata CSV file with columns id, pcd, layout
Usage
Use the SpatialLM code base for reading the point cloud and layout data.
from spatiallm import Layout
from spatiallm.pcd import load_o3d_pcd
# Load Point Cloud
point_cloud = load_o3d_pcd(args.point_cloud)
# Load Layout
with open(args.layout, "r") as f:
layout_content = f.read()
layout = Layout(layout_content)
Visualization
Use rerun to visualize the point cloud and the GT structured 3D layout output:
python visualize.py --point_cloud pcd/scene0000_00.ply --layout layout/scene0000_00.txt --save scene0000_00.rrd
rerun scene0000_00.rrd
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Size of downloaded dataset files:
6.22 kB
Size of the auto-converted Parquet files:
3.64 kB
Number of rows:
107
Models trained or fine-tuned on manycore-research/SpatialLM-Testset