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manycore-research/SpatialLM-Testset

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. Folder Structure Outlines of the dataset files:… See the full description on the dataset page: https://huggingface.co/datasets/manycore-research/SpatialLM-Testset.

sourceHugging Facecc-by-nc-4.0updated 1y agoView on Hugging Face
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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.

<table style="table-layout: fixed;"> <tr> <td style="text-align: center; vertical-align: middle; width: 25%"> <img src="./figures/a.jpg" alt="exmaple a" width="100%" style="display: block;"></td> <td style="text-align: center; vertical-align: middle; width: 25%"> <img src="./figures/b.jpg" alt="exmaple b" width="100%" style="display: block;"></td> <td style="text-align: center; vertical-align: middle; width: 25%"> <img src="./figures/c.jpg" alt="exmaple c" width="100%" style="display: block;"></td> <td style="text-align: center; vertical-align: middle; width: 25%"> <img src="./figures/d.jpg" alt="exmaple d" width="100%" style="display: block;"></td> </tr> </tr> </table>

Folder Structure

Outlines of the dataset files:

bash
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.

python
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:

bash
python visualize.py --point_cloud pcd/scene0000_00.ply --layout layout/scene0000_00.txt --save scene0000_00.rrd
rerun scene0000_00.rrd