A fast python implementation of DTU MVS 2014 evaluation

Last update: Dec 27, 2022

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Overview

DTUeval-python

A python implementation of DTU MVS 2014 evaluation. It only takes 1min for each mesh evaluation. And the gap between the two implementations is negligible.

Setup and Usage

This script requires the following dependencies.

numpy open3d scikit-learn tqdm scipy multiprocessing argparse

Download the STL point clouds and Sample Set and prepare the ground truth folder as follows.

<dataset_dir>
- Points
    - stl
        - stlxxx_total.ply
- ObsMask
    - ObsMaskxxx_10.mat
    - Planexxx.mat

Run the evaluation script (e.g. scan24, mesh mode)

python eval.py --data <input> --scan 24 --mode mesh --dataset_dir <dataset_dir> --vis_out_dir <out_dir_for_visualization>

Discussion on randomness

There is randomness in point cloud downsampling in both versions. It iterates through the points and delete the points with distance < 0.2. So the order of points matters. We randomly shuffle the points before downsampling.

Comparison with the official script

We evaluate a set of meshes from Colmap and compare the results. We run our script 10 times and take the average.

	diff/official	official	py_avg	py_std/official
24	0.0184%	0.986317	0.986135	0.0108%
37	0.0001%	2.354124	2.354122	0.0091%
40	0.0038%	0.730464	0.730492	0.0104%
55	0.0436%	0.530899	0.531131	0.0104%
63	0.0127%	1.555828	1.556025	0.0118%
65	0.0409%	1.007686	1.008098	0.0080%
69	0.0082%	0.888434	0.888361	0.0125%
83	0.0207%	1.136882	1.137117	0.0096%
97	0.0314%	0.907528	0.907813	0.0089%
105	0.0129%	1.463337	1.463526	0.0118%
106	0.1424%	0.785527	0.786646	0.0151%
110	0.0592%	1.076125	1.075488	0.0132%
114	0.0049%	0.436169	0.436190	0.0074%
118	0.1123%	0.679574	0.680337	0.0099%
122	0.0347%	0.726771	0.726519	0.0178%
avg	0.0153%	1.017711	1.017867

Error visualization

vis_xxx_d2s.ply and vis_xxx_s2d.ply are error visualizations.

Blue: Out of bounding box or ObsMask
Green: Errors larger than threshold (20)
White to Red: Errors counted in the reported statistics

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Comments

memory error

Hi, thanks for the code. just tried to run the code and set it to pcd instead of mesh.

But i encountered a memory error. Any suggestions?

Many thansks

error File "sklearn\neighbors_binary_tree.pxi", line 1591, in sklearn.neighbors._kd_tree.BinaryTree.query_radius File "sklearn\neighbors_binary_tree.pxi", line 1547, in sklearn.neighbors._kd_tree.BinaryTree.query_radius MemoryError downsample pcd: 25%|█████████████████████████████▊ | 2/8 [07:27<22:21, 223.50s/it]

also, i found the scale of point clouds produced by colmap is totally different to the dtu dataset. wondering if you preprocessed the colmap dense reconstruction or scale it up before using the code?

opened by TWang1017 2
Error in .ply

TriangleMesh with 51069949 points and 0 triangles. sample pcd from mesh: 11%|█████ | 1/9 [00:07<01:02, 7.76s/it]Traceback (most recent call last): File "pc_eval.py", line 70, in new_pts = np.concatenate(new_pts, axis=0) File "<array_function internals>", line 180, in concatenate ValueError: need at least one array to concatenate

Hi, I use IterMVS (CVPR 2022) to generate .ply, however during the evaluation, I encountered an error that there is no triangles, how can I solve it？

opened by gangweiX 2
How to obtain accuracy / completeness / overall?

Nice code ! 1.Does the output mean_d2s, mean_s2d, overall refer to accuracy(mm), completeness(mm), overall(mm) in most paper? 2.Besides, could you explain the metric

diff/official | official | py_avg | py_std/official -- | -- | -- | --

more specifically?

opened by AddASecond 4

Owner

GitHub

Team collaborative evaluation tracker.

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