This repository contains a Torch implementation of a PointNet Auto Encoder, inspired by [1] and [2].
[1] Charles Ruizhongtai Qi, Hao Su, Kaichun Mo, Leonidas J. Guibas:
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation. CoRR abs/1612.00593 (2016)
[2] Haoqiang Fan, Hao Su, Leonidas J. Guibas:
A Point Set Generation Network for 3D Object Reconstruction from a Single Image. CoRR abs/1612.00603 (2016)
If you use this code, please also cite the following master thesis:
@misc{Stutz2017,
author = {David Stutz},
title = {Learning Shape Completion from Bounding Boxes with CAD Shape Priors},
month = {September},
year = {2017},
institution = {RWTH Aachen University},
address = {Aachen, Germany},
howpublished = {http://davidstutz.de/},
}
First of all, make sure to have Torch installed, for example through
torch/distro which includes the required
(cu)nn(x) packages. Then, the C++ code can be compiled using
# CPU code
cd lib/cpp/cpu
mkdir build
cd build
cmake ..
make
# GPU code
cd ..
cd gpu/
mkdir build
cd build
cmake ..
make
Both the CPU and GPU code can be tested by running the following tests:
# within the build directory
./tests/test_chamfer_distance
./tests/test_max_distance
For the GPU code, you need to have CUDA installed, recommended is CUDA 8.
However, it also runs with lower CUDA version when adapting the used architecture.
For CUDA 8, using a Tesla K40, the compute architecture is sm_35
as shown in lib/gpu/CMakeLists.txt:
list(APPEND CUDA_NVCC_FLAGS "-arch=sm_35;-O2;-DVERBOSE")
If you use a different CUDA version and/or graphics card, make sure to adapt the architecture accordingly. Then rerun the tests to see if it works. When you still get errors such as
CUDA error at /BS/dstutz/work/shape-completion/code/release/pointnet_auto_encoder/lib/cpp/gpu/chamfer_distance.cu:80 code=30(cudaErrorUnknown) "cudaMalloc(&d_loss, sizeof(float))"
CUDA error at /BS/dstutz/work/shape-completion/code/release/pointnet_auto_encoder/lib/cpp/gpu/chamfer_distance.cu:81 code=30(cudaErrorUnknown) "cudaMemcpy(d_loss, &loss, sizeof(float), cudaMemcpyHostToDevice)"
CUDA error at /BS/dstutz/work/shape-completion/code/release/pointnet_auto_encoder/lib/cpp/gpu/chamfer_distance.cu:90 code=30(cudaErrorUnknown) "cudaMemcpy(&loss, d_loss, sizeof(float), cudaMemcpyDeviceToHost)"
CUDA error at /BS/dstutz/work/shape-completion/code/release/pointnet_auto_encoder/lib/cpp/gpu/chamfer_distance.cu:92 code=30(cudaErrorUnknown) "cudaFree(d_loss)"
it is very likely that the set architecture does not meet your installed CUDA version!
For training the auto encoder, the following Torch packages are required in addition to torch/distro:
Follow the instructions from the respective packages.
A usage example is provided in auto_encoder_train.lua which includes
three different models and a simple training and evaluation loop. Also see
the corresponding blog article on davidstutz.de.
License for source code corresponding to:
D. Stutz. Learning Shape Completion from Bounding Boxes with CAD Shape Priors. Master Thesis, RWTH Aachen University, 2017.
Copyright (c) 2018 David Stutz, Max-Planck-Gesellschaft
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