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// ----------------------------------------------------------------------------
// - Open3D: www.open3d.org -
// ----------------------------------------------------------------------------
// Copyright (c) 2018-2024 www.open3d.org
// SPDX-License-Identifier: MIT
// ----------------------------------------------------------------------------
#include "open3d/t/pipelines/registration/Registration.h"
#include <benchmark/benchmark.h>
#include "open3d/core/CUDAUtils.h"
#include "open3d/core/EigenConverter.h"
#include "open3d/core/nns/NearestNeighborSearch.h"
#include "open3d/data/Dataset.h"
#include "open3d/t/io/PointCloudIO.h"
#include "open3d/t/pipelines/registration/TransformationEstimation.h"
namespace open3d {
namespace t {
namespace pipelines {
namespace registration {
// Testing parameters:
// ICP ConvergenceCriteria.
static const double relative_fitness = 1e-6;
static const double relative_rmse = 1e-6;
static const int max_iterations = 10;
static const double voxel_downsampling_factor = 0.02;
// NNS parameter.
static const double max_correspondence_distance = 0.05;
// Normal estimation parameters.
static const double normals_search_radius = 10.0;
static const int normals_max_neighbors = 30;
// Initial transformation guess for registration.
static const std::vector<float> initial_transform_flat{
0.862, 0.011, -0.507, 0.5, -0.139, 0.967, -0.215, 0.7,
0.487, 0.255, 0.835, -1.4, 0.0, 0.0, 0.0, 1.0};
static std::tuple<geometry::PointCloud, geometry::PointCloud>
LoadTensorPointCloudFromFile(const std::string& source_pointcloud_filename,
const std::string& target_pointcloud_filename,
const double voxel_downsample_factor,
const core::Dtype& dtype,
const core::Device& device) {
geometry::PointCloud source, target;
io::ReadPointCloud(source_pointcloud_filename, source,
{"auto", false, false, true});
io::ReadPointCloud(target_pointcloud_filename, target,
{"auto", false, false, true});
source = source.To(device);
target = target.To(device);
// Eliminates the case of impractical values (including negative).
if (voxel_downsample_factor > 0.001) {
source = source.VoxelDownSample(voxel_downsample_factor);
target = target.VoxelDownSample(voxel_downsample_factor);
} else {
utility::LogWarning(
"VoxelDownsample: Impractical voxel size [< 0.001], skipping "
"downsampling.");
}
source.SetPointPositions(source.GetPointPositions().To(dtype));
source.SetPointNormals(source.GetPointNormals().To(dtype));
if (source.HasPointColors()) {
source.SetPointColors(source.GetPointColors().To(dtype).Div(255.0));
}
target.SetPointPositions(target.GetPointPositions().To(dtype));
target.SetPointNormals(target.GetPointNormals().To(dtype));
if (target.HasPointColors()) {
target.SetPointColors(target.GetPointColors().To(dtype).Div(255.0));
}
return std::make_tuple(source, target);
}
static void BenchmarkICP(benchmark::State& state,
const core::Device& device,
const core::Dtype& dtype,
const TransformationEstimationType& type) {
utility::SetVerbosityLevel(utility::VerbosityLevel::Error);
data::DemoICPPointClouds demo_icp_pointclouds;
geometry::PointCloud source, target;
std::tie(source, target) = LoadTensorPointCloudFromFile(
demo_icp_pointclouds.GetPaths(0), demo_icp_pointclouds.GetPaths(1),
voxel_downsampling_factor, dtype, device);
std::shared_ptr<TransformationEstimation> estimation;
if (type == TransformationEstimationType::PointToPlane) {
estimation = std::make_shared<TransformationEstimationPointToPlane>();
} else if (type == TransformationEstimationType::PointToPoint) {
estimation = std::make_shared<TransformationEstimationPointToPoint>();
} else if (type == TransformationEstimationType::ColoredICP) {
estimation = std::make_shared<TransformationEstimationForColoredICP>();
}
core::Tensor init_trans =
core::Tensor(initial_transform_flat, {4, 4}, core::Float32, device)
.To(dtype);
RegistrationResult reg_result(init_trans);
// Warm up.
reg_result = ICP(source, target, max_correspondence_distance, init_trans,
*estimation,
ICPConvergenceCriteria(relative_fitness, relative_rmse,
max_iterations));
for (auto _ : state) {
reg_result = ICP(source, target, max_correspondence_distance,
init_trans, *estimation,
ICPConvergenceCriteria(relative_fitness, relative_rmse,
max_iterations));
core::cuda::Synchronize(device);
}
}
core::Tensor ComputeDirectionVectors(const core::Tensor& positions) {
core::Tensor directions = core::Tensor::Empty(
positions.GetShape(), positions.GetDtype(), positions.GetDevice());
for (int64_t i = 0; i < positions.GetLength(); ++i) {
// Compute the norm of the position vector.
core::Tensor norm = (positions[i][0] * positions[i][0] +
positions[i][1] * positions[i][1] +
positions[i][2] * positions[i][2])
.Sqrt();
// If the norm is zero, set the direction vector to zero.
if (norm.Item<float>() == 0.0) {
directions[i].Fill(0.0);
} else {
// Otherwise, compute the direction vector by dividing the position
// vector by its norm.
directions[i] = positions[i] / norm;
}
}
return directions;
}
static std::tuple<geometry::PointCloud, geometry::PointCloud>
LoadTensorDopplerPointCloudFromFile(
const std::string& source_pointcloud_filename,
const std::string& target_pointcloud_filename,
const double voxel_downsample_factor,
const core::Dtype& dtype,
const core::Device& device) {
geometry::PointCloud source, target;
io::ReadPointCloud(source_pointcloud_filename, source,
{"auto", false, false, true});
io::ReadPointCloud(target_pointcloud_filename, target,
{"auto", false, false, true});
source.SetPointAttr("directions",
ComputeDirectionVectors(source.GetPointPositions()));
source = source.To(device);
target = target.To(device);
// Eliminates the case of impractical values (including negative).
if (voxel_downsample_factor > 0.001) {
source = source.VoxelDownSample(voxel_downsample_factor);
target = target.VoxelDownSample(voxel_downsample_factor);
} else {
utility::LogWarning(
"VoxelDownsample: Impractical voxel size [< 0.001], skipping "
"downsampling.");
}
source.SetPointPositions(source.GetPointPositions().To(dtype));
source.SetPointAttr("dopplers", source.GetPointAttr("dopplers").To(dtype));
source.SetPointAttr("directions",
source.GetPointAttr("directions").To(dtype));
target.SetPointPositions(target.GetPointPositions().To(dtype));
target.EstimateNormals(normals_search_radius, normals_max_neighbors);
return std::make_tuple(source, target);
}
static void BenchmarkDopplerICP(benchmark::State& state,
const core::Device& device,
const core::Dtype& dtype,
const TransformationEstimationType& type) {
utility::SetVerbosityLevel(utility::VerbosityLevel::Error);
data::DemoDopplerICPSequence demo_sequence;
geometry::PointCloud source, target;
std::tie(source, target) = LoadTensorDopplerPointCloudFromFile(
demo_sequence.GetPath(0), demo_sequence.GetPath(1),
voxel_downsampling_factor, dtype, device);
Eigen::Matrix4d calibration{Eigen::Matrix4d::Identity()};
double period{0.0};
demo_sequence.GetCalibration(calibration, period);
const core::Tensor calibration_t =
core::eigen_converter::EigenMatrixToTensor(calibration)
.To(device, dtype);
TransformationEstimationForDopplerICP estimation_dicp;
estimation_dicp.period_ = period;
estimation_dicp.transform_vehicle_to_sensor_ = calibration_t;
core::Tensor init_trans = core::Tensor::Eye(4, dtype, device);
RegistrationResult reg_result(init_trans);
// Warm up.
reg_result = ICP(source, target, max_correspondence_distance, init_trans,
estimation_dicp,
ICPConvergenceCriteria(relative_fitness, relative_rmse,
max_iterations));
for (auto _ : state) {
reg_result = ICP(source, target, max_correspondence_distance,
init_trans, estimation_dicp,
ICPConvergenceCriteria(relative_fitness, relative_rmse,
max_iterations));
core::cuda::Synchronize(device);
}
}
#define ENUM_ICP_METHOD_DEVICE(BENCHMARK_FUNCTION, METHOD_NAME, \
TRANSFORMATION_TYPE, DEVICE) \
BENCHMARK_CAPTURE(BENCHMARK_FUNCTION, DEVICE METHOD_NAME##_Float32, \
core::Device(DEVICE), core::Float32, \
TRANSFORMATION_TYPE) \
->Unit(benchmark::kMillisecond); \
BENCHMARK_CAPTURE(BENCHMARK_FUNCTION, DEVICE METHOD_NAME##_Float64, \
core::Device(DEVICE), core::Float64, \
TRANSFORMATION_TYPE) \
->Unit(benchmark::kMillisecond);
ENUM_ICP_METHOD_DEVICE(BenchmarkICP,
PointToPoint,
TransformationEstimationType::PointToPoint,
"CPU:0")
ENUM_ICP_METHOD_DEVICE(BenchmarkICP,
PointToPlane,
TransformationEstimationType::PointToPlane,
"CPU:0")
ENUM_ICP_METHOD_DEVICE(BenchmarkICP,
ColoredICP,
TransformationEstimationType::ColoredICP,
"CPU:0")
ENUM_ICP_METHOD_DEVICE(BenchmarkDopplerICP,
DopplerICP,
TransformationEstimationType::DopplerICP,
"CPU:0")
#ifdef BUILD_CUDA_MODULE
ENUM_ICP_METHOD_DEVICE(BenchmarkICP,
PointToPoint,
TransformationEstimationType::PointToPoint,
"CUDA:0")
ENUM_ICP_METHOD_DEVICE(BenchmarkICP,
PointToPlane,
TransformationEstimationType::PointToPlane,
"CUDA:0")
ENUM_ICP_METHOD_DEVICE(BenchmarkICP,
ColoredICP,
TransformationEstimationType::ColoredICP,
"CUDA:0")
ENUM_ICP_METHOD_DEVICE(BenchmarkDopplerICP,
DopplerICP,
TransformationEstimationType::DopplerICP,
"CUDA:0")
#endif
} // namespace registration
} // namespace pipelines
} // namespace t
} // namespace open3d
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