Lingxiao Li*, Minhyuk Sung*, Anastasia Dubrovina, Li Yi, Leonidas Guibas [arXiv]
CVPR2019 (Oral Presentation)
(* indicates equal contribution)
@misc{1811.08988,
Author = {Lingxiao Li and Minhyuk Sung and Anastasia Dubrovina and Li Yi and Leonidas Guibas},
Title = {Supervised Fitting of Geometric Primitives to 3D Point Clouds},
Year = {2018},
Eprint = {arXiv:1811.08988},
}
Fitting geometric primitives to 3D point cloud data bridges a gap between low-level digitized 3D data and high-level structural information on the underlying 3D shapes. As such, it enables many downstream applications in 3D data processing. For a long time, RANSAC-based methods have been the gold standard for such primitive fitting problems, but they require careful per-input parameter tuning and thus do not scale well for large datasets with diverse shapes. In this work, we introduce Supervised Primitive Fitting Network (SPFN), an end-to-end neural network that can robustly detect a varying number of primitives at different scales without any user control. The network is supervised using ground truth primitive surfaces and primitive membership for the input points. Instead of directly predicting the primitives, our architecture first predicts per-point properties and then uses a differentiable model estimation module to compute the primitive type and parameters. We evaluate our approach on a novel benchmark of ANSI 3D mechanical component models and demonstrate a significant improvement over both the state-of-the-art RANSAC-based methods and the direct neural prediction.
The code has been tested with Tensorflow 1.10 (GPU version) and Python 3.6.6. There are a few dependencies on the following Python libraries : numpy (tested with 1.14.5), scipy (tested with 1.1.0), pandas (tested with 0.23.4), PyYAML (tested with 3.13), and h5py (tested with 2.8.0).
In order to use PointNet++, one needs to run each of the compile scripts under pointnet_plusplus/utils/tf_ops to compile the CUDA code. One needs to change the various path names in pointnet_plusplus/utils/tf_opsto point to the correct CUDA/tensorflow directories.
First, while in the project folder, download processed ANSI 3D dataset of mechanical components (point clouds with 8k points, 9.4GB zip file, 12GB after unzipping):
wget --no-check-certificate https://shapenet.cs.stanford.edu/media/minhyuk/spfn/data/spfn_traceparts.zip
unzip spfn_traceparts.zip
The original CAD data is kindly provided by Traceparts. The provided dataset has been processed to extract primitive surface information and point samples on each surface as well as on the whole shape.
We also provide high-resolution point clouds of the same dataset, which has 131,072 (128k) points for each object (121GB tar file):
wget --no-check-certificate https://shapenet.cs.stanford.edu/media/minhyuk/spfn/data/spfn_traceparts_high_res.tar
Train SPFN with our default configuration by:
mkdir experiments && cd experiments
cp ../default_configs/network_config.yml .
python3 ../spfn/train.py network_config.yml
Note that the script train.py takes a configuration YAML file network_config.yml that contains GPU setting, data source, neural network parameters, training hyperparameters, and I/O parameters. Simply copy the default YAML configuration file and change parameters to your need. During training, three folders will be created/updated. In their default locations, results/model is the directory for storing the Tensorflow model, results/log is the directory for log files (created by tf.summary.FileWriter), and results/val_pred contains predictions for the validation dataset at varying training steps.
At test time, run train.py with --test flag to run the network on test dataset speficied by test_data_file in the YAML configuration:
python3 ../spfn/train.py network_config.yml --test
As a shortcut, and also to test the network with only input points without other supervision, run train.py with --test_pc_in=tmp.xyz and --test_h5_out=tmp.h5 in additional to --test flag, where tmp.xyz is assumed to be a point cloud file with one point x y z on each line:
python3 ../spfn/train.py ../default_configs/network_config.yml --test --test_pc_in=tmp.xyz --test_h5_out=tmp.h5
The predictions are stored in HDF5 format. Each HDF5 prediction file contains per-point normal prediction, per-point membership prediction, per-point type prediction, and estimated parameters for each primitive.
For evaluating the predictions made by SPFN and other approaches (as in ablation studies and RANSAC variants in the paper), we use a unified evaluation pipeline to compute the various metrics proposed in the paper.
While in experiments folder, first copy the default evaluation configuration file:
cp ../default_configs/eval_config.yml .
This file contains pointers to the data source and the prediction directory. One needs to modify prediction_dir to point to a direction containing HDF5 predictions (same format as SPFN predictions). Run the evaluation network by
python3 ../spfn/eval.py eval_config.yml
This will generate a directory (by default results/eval_bundle) containing one bundle file for every shape. The bundle file contains evaluation results following the evaluation metrics propsed in the paper (mean IoU, type accuracy, different kinds of coverage numbers etc.), in addition to input data and the prediction (hence the name "bundle"). These bundle files can then be used for visualization and downstream analysis.
Code in pointnet_plusplus folder is borrowed from PointNet++, with slight modification to support dynamic size and extra parallel fully-connected layers at the end.
This code is released under the MIT License. Refer to LICENSE for details.
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