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Add select_by_index method to Feature class (#7039)
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* Add select_by_index method to Feature class

* better comments

---------

Co-authored-by: Benjamin Ummenhofer <benjamin.ummenhofer@intel.com>
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nicolaloi and benjaminum authored Nov 28, 2024
1 parent 4c3fc34 commit c8856fc
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1 change: 1 addition & 0 deletions CHANGELOG.md
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Expand Up @@ -52,6 +52,7 @@
- Fix alpha shape reconstruction if alpha too small for point scale (PR #6998)
- Fix render to depth image on Apple Retina displays (PR #7001)
- Fix infinite loop in segment_plane if num_points < ransac_n (PR #7032)
- Add select_by_index method to Feature class (PR #7039)

## 0.13

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25 changes: 25 additions & 0 deletions cpp/open3d/pipelines/registration/Feature.cpp
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Expand Up @@ -18,6 +18,31 @@ namespace open3d {
namespace pipelines {
namespace registration {

std::shared_ptr<Feature> Feature::SelectByIndex(
const std::vector<size_t> &indices, bool invert /* = false */) const {
auto output = std::make_shared<Feature>();
output->Resize(data_.rows(), indices.size());

std::vector<bool> mask = std::vector<bool>(data_.cols(), invert);
for (size_t i : indices) {
mask[i] = !invert;
}

size_t current_col_feature = 0;
for (size_t i = 0; i < static_cast<size_t>(data_.cols()); i++) {
if (mask[i]) {
output->data_.col(current_col_feature) = data_.col(i);
current_col_feature++;
}
}

utility::LogDebug(
"Feature group down sampled from {:d} features to {:d} features.",
(int)data_.cols(), (int)output->data_.cols());

return output;
}

static Eigen::Vector4d ComputePairFeatures(const Eigen::Vector3d &p1,
const Eigen::Vector3d &n1,
const Eigen::Vector3d &p2,
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8 changes: 8 additions & 0 deletions cpp/open3d/pipelines/registration/Feature.h
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Expand Up @@ -42,6 +42,14 @@ class Feature {
/// Returns number of points.
size_t Num() const { return data_.cols(); }

/// \brief Selects features from \p input Feature group, with indices in \p
/// indices, and returns a new Feature group with selected features.
///
/// \param indices Indices of features to be selected.
/// \param invert Set to `True` to invert the selection of indices.
std::shared_ptr<Feature> SelectByIndex(const std::vector<size_t> &indices,
bool invert = false) const;

public:
/// Data buffer storing features.
Eigen::MatrixXd data_;
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9 changes: 9 additions & 0 deletions cpp/pybind/pipelines/registration/feature.cpp
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Expand Up @@ -31,6 +31,10 @@ void pybind_feature_definitions(py::module &m_registration) {
.def("dimension", &Feature::Dimension,
"Returns feature dimensions per point.")
.def("num", &Feature::Num, "Returns number of points.")
.def("select_by_index", &Feature::SelectByIndex,
"Function to select features from input Feature group into "
"output Feature group.",
"indices"_a, "invert"_a = false)
.def_readwrite("data", &Feature::data_,
"``dim x n`` float64 numpy array: Data buffer "
"storing features.")
Expand All @@ -48,6 +52,11 @@ void pybind_feature_definitions(py::module &m_registration) {
docstring::ClassMethodDocInject(m_registration, "Feature", "resize",
{{"dim", "Feature dimension per point."},
{"n", "Number of points."}});
docstring::ClassMethodDocInject(
m_registration, "Feature", "select_by_index",
{{"indices", "Indices of features to be selected."},
{"invert",
"Set to ``True`` to invert the selection of indices."}});
m_registration.def("compute_fpfh_feature", &ComputeFPFHFeature,
"Function to compute FPFH feature for a point cloud",
"input"_a, "search_param"_a);
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32 changes: 32 additions & 0 deletions cpp/tests/t/pipelines/registration/Feature.cpp
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Expand Up @@ -25,6 +25,38 @@ INSTANTIATE_TEST_SUITE_P(Feature,
FeaturePermuteDevices,
testing::ValuesIn(PermuteDevices::TestCases()));

TEST_P(FeaturePermuteDevices, SelectByIndex) {
core::Device device = GetParam();

open3d::geometry::PointCloud pcd_legacy;
data::BunnyMesh bunny;
open3d::io::ReadPointCloud(bunny.GetPath(), pcd_legacy);

pcd_legacy.EstimateNormals();
// Convert to float64 to avoid precision loss.
const auto pcd = t::geometry::PointCloud::FromLegacy(pcd_legacy,
core::Float64, device);

const auto fpfh = pipelines::registration::ComputeFPFHFeature(
pcd_legacy, geometry::KDTreeSearchParamHybrid(0.01, 100));
const auto fpfh_t =
t::pipelines::registration::ComputeFPFHFeature(pcd, 100, 0.01);

const auto selected_fpfh =
fpfh->SelectByIndex({53, 194, 839, 2543, 6391, 29483}, false);
const auto selected_indeces_t =
core::TensorKey::IndexTensor(core::Tensor::Init<int64_t>(
{53, 194, 839, 2543, 6391, 29483}, device));
const auto selected_fpfh_t = fpfh_t.GetItem(selected_indeces_t);

EXPECT_TRUE(selected_fpfh_t.AllClose(
core::eigen_converter::EigenMatrixToTensor(selected_fpfh->data_)
.T()
.To(selected_fpfh_t.GetDevice(),
selected_fpfh_t.GetDtype()),
1e-4, 1e-4));
}

TEST_P(FeaturePermuteDevices, ComputeFPFHFeature) {
core::Device device = GetParam();

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