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// ----------------------------------------------------------------------------
// - Open3D: www.open3d.org -
// ----------------------------------------------------------------------------
// Copyright (c) 2018-2024 www.open3d.org
// SPDX-License-Identifier: MIT
// ----------------------------------------------------------------------------
#include <string>
#include <vector>
#include "core/CoreTest.h"
#include "open3d/camera/PinholeCameraTrajectory.h"
#include "open3d/core/EigenConverter.h"
#include "open3d/core/MemoryManager.h"
#include "open3d/io/PinholeCameraTrajectoryIO.h"
#include "open3d/io/PoseGraphIO.h"
#include "open3d/pipelines/registration/PoseGraph.h"
#include "open3d/t/geometry/VoxelBlockGrid.h"
#include "open3d/t/io/ImageIO.h"
#include "open3d/t/io/PointCloudIO.h"
#include "open3d/t/pipelines/registration/Registration.h"
#include "open3d/t/pipelines/slac/SLACOptimizer.h"
#include "open3d/utility/FileSystem.h"
#include "open3d/utility/Timer.h"
#include "tests/Tests.h"
namespace open3d {
namespace tests {
class SLACPermuteDevices : public PermuteDevices {};
INSTANTIATE_TEST_SUITE_P(SLAC,
SLACPermuteDevices,
testing::ValuesIn(PermuteDevices::TestCases()));
// PointCloud is similar if fitness is higher and rmse is lower than tolerance
// threshold.
static bool IsPointCloudSimilar(t::geometry::PointCloud source,
t::geometry::PointCloud target,
double voxel_size = 0.05,
float inlier_fitness_threshold = 0.99,
float inlier_rmse_threshold = 0.0001) {
auto result = t::pipelines::registration::EvaluateRegistration(
source, target, /*search_distance*/ voxel_size,
core::Tensor::Eye(4, core::Float64, core::Device("CPU:0")));
if (result.fitness_ >= inlier_fitness_threshold &&
result.inlier_rmse_ <= inlier_rmse_threshold) {
return true;
}
return false;
}
TEST_P(SLACPermuteDevices, DISABLED_RunSLACOptimizerForFragments) {
core::Device device = GetParam();
std::string dataset_folder =
"/reconstruction_system/livingroom1_clean_micro";
std::string fragment_folder = dataset_folder + "/test_fragments";
std::string scene_folder = dataset_folder + "/test_scene";
std::string slac_folder = dataset_folder + "/output_slac";
std::string test_slac_folder = dataset_folder + "/test_slac";
std::vector<std::string> fragment_fnames;
utility::filesystem::ListFilesInDirectoryWithExtension(
fragment_folder, "ply", fragment_fnames);
if (fragment_fnames.size() == 0) {
utility::LogError(
"No fragment found in {}, please make sure the test dataset "
"has been downloaded.",
fragment_folder);
}
std::sort(fragment_fnames.begin(), fragment_fnames.end());
std::string pose_graph_fname =
scene_folder + "/refined_registration_optimized.json";
auto pose_graph = open3d::io::CreatePoseGraphFromFile(pose_graph_fname);
// Optimizer Parameters. [Hard coded for unit-tests].
auto params = t::pipelines::slac::SLACOptimizerParams();
params.slac_folder_ = slac_folder;
params.voxel_size_ = 0.05;
params.regularizer_weight_ = 1;
params.distance_threshold_ = 0.07;
params.fitness_threshold_ = 0.3;
params.max_iterations_ = 5;
params.device_ = device;
// Debug options.
auto debug_option = t::pipelines::slac::SLACDebugOption();
debug_option.debug_ = false;
t::pipelines::slac::ControlGrid control_grid;
open3d::pipelines::registration::PoseGraph pose_graph_updated;
// Testing SLAC.
std::tie(pose_graph_updated, control_grid) =
t::pipelines::slac::RunSLACOptimizerForFragments(
fragment_fnames, *pose_graph, params, debug_option);
// TODO.
// AssertSavedCorrespondences();
// Write control grids.
auto hashmap = control_grid.GetHashMap();
core::Tensor active_buf_indices;
hashmap->GetActiveIndices(active_buf_indices);
active_buf_indices = active_buf_indices.To(core::Int64);
hashmap->GetKeyTensor()
.IndexGet({active_buf_indices})
.Save(params.GetSubfolderName() + "/ctr_grid_keys.npy");
// Check if same.
core::Tensor output_control_grid_keys = core::Tensor::Load(
params.GetSubfolderName() + "/ctr_grid_keys.npy");
core::Tensor test_control_grid_keys = core::Tensor::Load(
test_slac_folder + "/0.050" + "/ctr_grid_keys.npy");
hashmap->GetValueTensor()
.IndexGet({active_buf_indices})
.Save(params.GetSubfolderName() + "/ctr_grid_values.npy");
// Check if same.
core::Tensor output_control_grid_values = core::Tensor::Load(
params.GetSubfolderName() + "/ctr_grid_values.npy");
core::Tensor test_control_grid_values = core::Tensor::Load(
test_slac_folder + "/0.050" + "/ctr_grid_values.npy");
// Write pose graph.
io::WritePoseGraph(params.GetSubfolderName() + "/optimized_posegraph_" +
"slac" + ".json",
pose_graph_updated);
// Skipping comparing PoseGraph. TODO.
// auto output_pose_graph = io::CreatePoseGraphFromFile(
// params.GetSubfolderName() + "/optimized_posegraph_" + "slac" +
// ".json");
// auto test_pose_graph = io::CreatePoseGraphFromFile(
// test_slac_folder + "/0.050" + "/optimized_posegraph_" + "slac" +
// ".json");
// Write trajectory for SLACIntegrate
open3d::camera::PinholeCameraTrajectory trajectory;
for (size_t i = 0; i < pose_graph_updated.nodes_.size(); ++i) {
auto fragment_pose_graph = io::CreatePoseGraphFromFile(fmt::format(
"{}/fragment_optimized_{:03d}.json", fragment_folder, i));
for (auto node : fragment_pose_graph->nodes_) {
auto pose = pose_graph_updated.nodes_[i].pose_ * node.pose_;
camera::PinholeCameraParameters param;
param.extrinsic_ = pose.inverse().eval();
trajectory.parameters_.push_back(param);
}
}
// Skipping comparing CameraTrajectory. TODO.
open3d::io::WritePinholeCameraTrajectory(params.GetSubfolderName() +
"/optimized_trajectory_" +
"slac" + ".log",
trajectory);
}
TEST_P(SLACPermuteDevices, DISABLED_SLACIntegrate) {
core::Device device = GetParam();
std::string dataset_folder =
"/reconstruction_system/livingroom1_clean_micro";
std::string fragment_folder = dataset_folder + "/test_fragments";
std::string color_folder = dataset_folder + "/image";
std::string depth_folder = dataset_folder + "/depth";
std::string scene_folder = dataset_folder + "/test_scene";
std::string test_slac_subfolder = dataset_folder + "/test_slac/0.050";
std::string test_pointcloud_filename =
dataset_folder + "/test_output_pcd.ply";
std::string output_slac_folder = dataset_folder + "/output_slac";
// Optimized fragment pose graph.
std::string posegraph_path =
std::string(test_slac_subfolder + "/optimized_posegraph_slac.json");
auto posegraph = io::CreatePoseGraphFromFile(posegraph_path);
// Intrinsics. [None provided - default Primesense intrinsics will be used].
std::string intrinsic_path = "";
camera::PinholeCameraIntrinsic intrinsic = camera::PinholeCameraIntrinsic(
camera::PinholeCameraIntrinsicParameters::PrimeSenseDefault);
auto focal_length = intrinsic.GetFocalLength();
auto principal_point = intrinsic.GetPrincipalPoint();
core::Tensor intrinsic_t = core::Tensor::Init<double>(
{{focal_length.first, 0, principal_point.first},
{0, focal_length.second, principal_point.second},
{0, 0, 1}});
// Voxelgrid options.
int block_count = 40000;
float voxel_size = 0.05;
float depth_scale = 1000.f;
float depth_max = 3.f;
t::geometry::VoxelBlockGrid voxel_grid(
{"tsdf", "weight", "color"},
{core::Dtype::Float32, core::Dtype::Float32, core::Dtype::Float32},
{{1}, {1}, {3}}, voxel_size, 16, block_count, device);
// Load control grid
core::Tensor ctr_grid_keys = core::Tensor::Load(test_slac_subfolder +
"/ctr_grid_keys."
"npy");
core::Tensor ctr_grid_values = core::Tensor::Load(test_slac_subfolder +
"/ctr_grid_values."
"npy");
t::pipelines::slac::ControlGrid ctr_grid(3.0 / 8, ctr_grid_keys.To(device),
ctr_grid_values.To(device),
device);
std::vector<std::string> color_filenames;
utility::filesystem::ListFilesInDirectory(color_folder, color_filenames);
std::sort(color_filenames.begin(), color_filenames.end());
std::vector<std::string> depth_filenames;
utility::filesystem::ListFilesInDirectory(depth_folder, depth_filenames);
std::sort(depth_filenames.begin(), depth_filenames.end());
int k = 0;
for (size_t i = 0; i < posegraph->nodes_.size(); ++i) {
utility::LogDebug("Fragment: {}", i);
auto fragment_pose_graph = *io::CreatePoseGraphFromFile(fmt::format(
"{}/fragment_optimized_{:03d}.json", fragment_folder, i));
for (auto node : fragment_pose_graph.nodes_) {
Eigen::Matrix4d pose_local = node.pose_;
core::Tensor extrinsic_local_t =
core::eigen_converter::EigenMatrixToTensor(
pose_local.inverse().eval());
Eigen::Matrix4d pose = posegraph->nodes_[i].pose_ * node.pose_;
core::Tensor extrinsic_t =
core::eigen_converter::EigenMatrixToTensor(
pose.inverse().eval());
auto depth =
t::io::CreateImageFromFile(depth_filenames[k])->To(device);
auto color =
t::io::CreateImageFromFile(color_filenames[k])->To(device);
t::geometry::RGBDImage rgbd(color, depth);
utility::Timer timer;
timer.Start();
t::geometry::RGBDImage rgbd_projected =
ctr_grid.Deform(rgbd, intrinsic_t, extrinsic_local_t,
depth_scale, depth_max);
core::Tensor frustum_block_coords =
voxel_grid.GetUniqueBlockCoordinates(
rgbd.depth_, intrinsic_t, extrinsic_t, depth_scale,
depth_max);
voxel_grid.Integrate(frustum_block_coords, rgbd_projected.depth_,
rgbd_projected.color_, intrinsic_t,
extrinsic_t, depth_scale, depth_max);
timer.Stop();
++k;
utility::LogDebug("{}: Deformation + Integration takes {}", k,
timer.GetDurationInMillisecond());
}
}
auto pcd = voxel_grid.ExtractPointCloud();
t::geometry::PointCloud test_pointcloud(device);
t::io::ReadPointCloud(test_pointcloud_filename, test_pointcloud,
{"auto", false, false, true});
test_pointcloud = test_pointcloud.To(device);
IsPointCloudSimilar(pcd, test_pointcloud, voxel_size, 0.98, 0.00004);
}
} // namespace tests
} // namespace open3d
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