Point Cloud Registration Network

Related tags

Deep Learning pcrnet
Overview

PCRNet: Point Cloud Registration Network using PointNet Encoding

Source Code Author: Vinit Sarode and Xueqian Li

Paper | Website | Video | Pytorch Implementation

Requirements:

  1. Cuda 10
  2. tensorflow==1.14
  3. transforms3d==0.3.1
  4. h5py==2.9.0

Dataset:

Path for dataset: Link

  1. Download 'train_data' folder from above link for iterative PCRNet.
  2. Download 'car_data' folder from above link for PCRNet.

Pretrained Model:

Download pretrained models from Link

How to use code:

Compile loss functions:

  1. cd utils/pc_distance
  2. make -f makefile_10.0 clean
  3. make -f makefile_10.0

Train Iterative-PCRNet:

  1. chmod +x train_itrPCRNet.sh
  2. ./train_itrPCRNet.sh

Train PCRNet:

  1. chmod +x train_PCRNet.sh
  2. ./train_PCRNet.sh

Citation

@InProceedings{vsarode2019pcrnet,
       author = {Sarode, Vinit and Li, Xueqian and Goforth, Hunter and Aoki, Yasuhiro and Arun Srivatsan, Rangaprasad and Lucey, Simon and Choset, Howie},
       title = {PCRNet: Point Cloud Registration Network using PointNet Encoding},
       month = {Aug},
       year = {2019}
}
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Comments
  • Bump numpy from 1.16.3 to 1.22.0

    Bump numpy from 1.16.3 to 1.22.0

    Bumps numpy from 1.16.3 to 1.22.0.

    Release notes

    Sourced from numpy's releases.

    v1.22.0

    NumPy 1.22.0 Release Notes

    NumPy 1.22.0 is a big release featuring the work of 153 contributors spread over 609 pull requests. There have been many improvements, highlights are:

    • Annotations of the main namespace are essentially complete. Upstream is a moving target, so there will likely be further improvements, but the major work is done. This is probably the most user visible enhancement in this release.
    • A preliminary version of the proposed Array-API is provided. This is a step in creating a standard collection of functions that can be used across application such as CuPy and JAX.
    • NumPy now has a DLPack backend. DLPack provides a common interchange format for array (tensor) data.
    • New methods for quantile, percentile, and related functions. The new methods provide a complete set of the methods commonly found in the literature.
    • A new configurable allocator for use by downstream projects.

    These are in addition to the ongoing work to provide SIMD support for commonly used functions, improvements to F2PY, and better documentation.

    The Python versions supported in this release are 3.8-3.10, Python 3.7 has been dropped. Note that 32 bit wheels are only provided for Python 3.8 and 3.9 on Windows, all other wheels are 64 bits on account of Ubuntu, Fedora, and other Linux distributions dropping 32 bit support. All 64 bit wheels are also linked with 64 bit integer OpenBLAS, which should fix the occasional problems encountered by folks using truly huge arrays.

    Expired deprecations

    Deprecated numeric style dtype strings have been removed

    Using the strings "Bytes0", "Datetime64", "Str0", "Uint32", and "Uint64" as a dtype will now raise a TypeError.

    (gh-19539)

    Expired deprecations for loads, ndfromtxt, and mafromtxt in npyio

    numpy.loads was deprecated in v1.15, with the recommendation that users use pickle.loads instead. ndfromtxt and mafromtxt were both deprecated in v1.17 - users should use numpy.genfromtxt instead with the appropriate value for the usemask parameter.

    (gh-19615)

    ... (truncated)

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    opened by dependabot[bot] 0
  • The problems for test

    The problems for test

    Thank you for your good work. However, I have some questions about the test.
    1)It's hard to understand the code of the test part. Can we implement the test just by changing the train to test in the mode? 2) How can we get the dataset for testing? I didn't find it in the code. The dataset for training and evaling is easy to be obtained and it is easy to be understand.

    opened by renmaqilong 0
  • The result of tf_nndistance.nn_distance is zero

    The result of tf_nndistance.nn_distance is zero

    When I "make -f makefile_10.0 clean" and "make -f makefile_10.0", I can use the tf_nndistance.nn_distance function ,but no matter what two sets of point clouds I enter, the returned distance and idx are zero. How can I use the function correctly?Thanks

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Owner
ViNiT SaRoDe
"Vinit is a computer scientist and roboticist. His research focuses on making machines intelligent."
ViNiT SaRoDe
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