diff --git a/cpp/src/DO/Sara/SfM/Odometry/OdometryPipeline.cpp b/cpp/src/DO/Sara/SfM/Odometry/OdometryPipeline.cpp
new file mode 100644
index 000000000..db5616a0a
--- /dev/null
+++ b/cpp/src/DO/Sara/SfM/Odometry/OdometryPipeline.cpp
@@ -0,0 +1,323 @@
+// ========================================================================== //
+// This file is part of Sara, a basic set of libraries in C++ for computer
+// vision.
+//
+// Copyright (C) 2024-present David Ok <david.ok8@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla Public
+// License v. 2.0. If a copy of the MPL was not distributed with this file,
+// you can obtain one at http://mozilla.org/MPL/2.0/.
+// ========================================================================== //
+
+#include <DO/Sara/SfM/Odometry/OdometryPipeline.hpp>
+
+#include <DO/Sara/Logging/Logger.hpp>
+
+#include <DO/Sara/Core/Math/Rotation.hpp>
+#include <DO/Sara/FeatureDetectors/SIFT.hpp>
+#include <DO/Sara/Graphics/ImageDraw.hpp>
+#include <DO/Sara/SfM/Helpers/KeypointMatching.hpp>
+#include <DO/Sara/Visualization/Features/Draw.hpp>
+
+
+using namespace DO::Sara;
+
+
+auto draw_feature_tracks(DO::Sara::ImageView<Rgb8>& display,  //
+                         const CameraPoseGraph& pgraph,       //
+                         const FeatureGraph& fgraph,          //
+                         const CameraPoseGraph::Vertex pose_u,
+                         const std::vector<FeatureTracker::Track>& tracks_alive,
+                         const std::vector<std::size_t>& track_visibility_count,
+                         const float scale) -> void
+{
+  for (auto t = 0u; t < tracks_alive.size(); ++t)
+  {
+    const auto& track = tracks_alive[t];
+
+    // Just to double check.
+    if (track_visibility_count[t] < 3u)
+      continue;
+
+    auto p = std::array<Eigen::Vector2f, 2>{};
+    auto r = std::array<float, 2>{};
+    {
+      const auto& [pose_v0, k] = fgraph[track[0]];
+      const auto& f0 = features(pgraph[pose_v0].keypoints)[k];
+      p[0] = f0.center();
+      r[0] = std::max(f0.radius() * scale, 1.f);
+      const auto color =
+          pose_v0 == pose_u ? Rgb8{0, 255, 255} : Rgb8{0, 127, 127};
+      draw_circle(display, p[0], r[0], color, 3);
+    }
+
+    for (auto v1 = 1u; v1 < track.size(); ++v1)
+    {
+      const auto& [pose_v1, k] = fgraph[track[v1]];
+      const auto& f1 = features(pgraph[pose_v1].keypoints)[k];
+      p[1] = f1.center();
+      r[1] = std::max(f1.radius() * scale, 1.f);
+      const auto color =
+          pose_v1 == pose_u ? Rgb8{255, 0, 0} : Rgb8{0, 127, 127};
+      draw_circle(display, p[1], r[1], color, 3);
+
+      draw_arrow(display, p[0], p[1], color, 3);
+
+      p[0] = p[1];
+      r[0] = r[1];
+    }
+  }
+}
+
+
+auto OdometryPipeline::set_config(
+    const std::filesystem::path& video_path,
+    const v2::BrownConradyDistortionModel<double>& camera) -> void
+{
+  // Build the dependency graph.
+  _video_streamer.open(video_path);
+  // The original camera.
+  _camera = camera;
+  // The virtual camera for the undistorted image.
+  _camera_corrected.focal_lengths() << camera.fx(), camera.fy();
+  _camera_corrected.shear() = camera.shear();
+  _camera_corrected.principal_point() << camera.u0(), camera.v0();
+
+  // Computer vision tasks.
+  _distortion_corrector = std::make_unique<ImageDistortionCorrector>(
+      _video_streamer.frame_rgb8(),     //
+      _video_streamer.frame_gray32f(),  //
+      _camera                           //
+  );
+  _rel_pose_estimator.configure(_camera);
+  _point_cloud_generator = std::make_unique<PointCloudGenerator>(
+      _pose_graph, _feature_tracker._feature_graph, _point_cloud);
+}
+
+auto OdometryPipeline::read() -> bool
+{
+  return _video_streamer.read();
+}
+
+auto OdometryPipeline::process() -> void
+{
+  if (_video_streamer.skip())
+    return;
+
+  auto& logger = Logger::get();
+  SARA_LOGI(logger, "[Video Stream] Processing image frame {}",
+            _video_streamer.frame_number());
+
+  SARA_LOGI(logger, "[Image Distortion] Undistort image frame {}",
+            _video_streamer.frame_number());
+  _distortion_corrector->undistort();
+
+  grow_geometry();
+}
+
+auto OdometryPipeline::make_display_frame() const -> Image<Rgb8>
+{
+  auto frame = Image<Rgb8>{_distortion_corrector->frame_rgb8()};
+  draw_feature_tracks(frame, _pose_graph, _feature_tracker._feature_graph,
+                      _pose_curr, _tracks_alive, _track_visibility_count, 1.f);
+  return frame;
+}
+
+auto OdometryPipeline::detect_keypoints(const ImageView<float>& image) const
+    -> KeypointList<OERegion, float>
+{
+  auto& logger = Logger::get();
+  const auto keys = compute_sift_keypoints(image,  //
+                                           _feature_params.image_pyr_params);
+  SARA_LOGI(logger, "[Keypoint Detection] {} keypoints", features(keys).size());
+  return keys;
+}
+
+auto OdometryPipeline::estimate_relative_pose(
+    const KeypointList<OERegion, float>& keys_src,
+    const KeypointList<OERegion, float>& keys_dst) const
+    -> std::pair<RelativePoseData, TwoViewGeometry>
+{
+  auto& logger = Logger::get();
+
+  if (features(keys_src).empty() || features(keys_dst).empty())
+  {
+    SARA_LOGI(logger, "[Relative Pose] Skipped image matching...");
+    return {};
+  }
+
+  auto matches = match(keys_src, keys_dst, _feature_params.sift_nn_ratio);
+  SARA_LOGI(logger, "[Relative Pose] Matched image keypoints...");
+  if (matches.empty())
+    return {};
+  if (matches.size() > _feature_params.num_matches_max)
+    matches.resize(_feature_params.num_matches_max);
+
+  auto [two_view_geometry, inliers, sample_best] =
+      _rel_pose_estimator.estimate_relative_pose(keys_src, keys_dst, matches);
+  SARA_LOGI(logger, "[Relative Pose] Estimated relative pose...");
+
+  const auto res = std::make_pair(  //
+      RelativePoseData{.matches = std::move(matches),
+                       .inliers = std::move(inliers),
+                       .motion =
+                           {
+                               .R = two_view_geometry.C2.R,  //
+                               .t = two_view_geometry.C2.t   //
+                           }
+
+      },
+      std::move(two_view_geometry)  //
+  );
+
+  return res;
+}
+
+auto OdometryPipeline::grow_geometry() -> bool
+{
+  auto& logger = Logger::get();
+
+  // Detect and describe the local features.
+  _pose_prev = _pose_curr;
+
+  const auto frame_gray32f = _distortion_corrector->frame_gray32f();
+  const auto frame_number = _video_streamer.frame_number();
+  auto keys_curr = detect_keypoints(frame_gray32f);
+
+  // Boundary case: the graphs are empty.
+  if (_pose_graph.num_vertices() == 0)
+  {
+    // Initialize the new camera pose from the latest image frame.
+    auto abs_pose_curr = QuaternionBasedPose<double>::identity();
+    auto abs_pose_data = AbsolutePoseData{
+        frame_number,             //
+        std::move(keys_curr),     //
+        std::move(abs_pose_curr)  //
+    };
+    _pose_curr = _pose_graph.add_absolute_pose(std::move(abs_pose_data));
+
+    return true;
+  }
+
+  const auto& keys_prev = _pose_graph[_pose_prev].keypoints;
+  auto [rel_pose_data, two_view_geometry] =
+      estimate_relative_pose(keys_prev, keys_curr);
+  const auto num_inliers = rel_pose_data.inliers.flat_array().count();
+  SARA_LOGI(logger, "[SfM] Relative pose inliers: {} 3D points", num_inliers);
+  if (num_inliers < _feature_params.num_inliers_min)
+  {
+    SARA_LOGI(logger, "[SfM] Relative pose failed!");
+    return false;
+  }
+  SARA_LOGI(logger, "[SfM] Relative pose succeeded!");
+
+  // 1. Add the absolute pose vertex.
+  auto abs_pose_curr = QuaternionBasedPose<double>::nan();
+  if (_pose_graph.num_vertices() == 1)
+    abs_pose_curr = {
+        .q = Eigen::Quaterniond{rel_pose_data.motion.R},  //
+        .t = rel_pose_data.motion.t                       //
+    };
+  auto abs_pose_data = AbsolutePoseData{
+      frame_number,             //
+      std::move(keys_curr),     //
+      std::move(abs_pose_curr)  //
+  };
+  _pose_curr = _pose_graph.add_absolute_pose(std::move(abs_pose_data));
+
+  // 2. Update the feature tracks by adding the feature matches that are
+  //    verified by the relative pose estimation.
+  //    Notice move semantics which will the relative pose data after this call.
+  //
+  // Note that in the case of only two views, feature tracks are "compressed"
+  // feature matches.
+  const auto pose_edge = _pose_graph.add_relative_pose(  //
+      _pose_prev, _pose_curr,                            //
+      std::move(rel_pose_data));
+  _feature_tracker.update_feature_tracks(_pose_graph, pose_edge);
+
+  // 3. Recalculate the feature tracks that are still alive.
+  std::tie(_tracks_alive, _track_visibility_count) =
+      _feature_tracker.calculate_alive_feature_tracks(_pose_curr);
+
+  // Extra steps for when the pose graph contains 2 poses.
+  if (_pose_graph.num_vertices() == 2)
+  {
+    _tracks_alive_without_scene_point = _tracks_alive;
+  }
+  // Extra steps for when the pose graph contains 3 poses or more.
+  else
+  {
+    // 4. Propagate the scene point to the feature tracks that grew longer.
+    //    The feature tracks that grew longer can only be those among the
+    //    tracks that are still alive.
+    SARA_LOGI(
+        logger,
+        "Propagating the scene points to feature tracks that grew longer...");
+    _point_cloud_generator->propagate_scene_point_indices(_tracks_alive);
+
+    // 5. Reassign a unique scene point cloud to each feature tracks by
+    //    compressing the point cloud.
+    SARA_LOGI(logger, "Compressing the point cloud...");
+    _point_cloud_generator->compress_point_cloud(
+        _feature_tracker._feature_tracks);
+
+    // 6. Determine the current absolute pose from the alive tracks using a PnP
+    //    approach.
+    std::tie(_tracks_alive_with_known_scene_point,
+             _tracks_alive_without_scene_point) =
+        _point_cloud_generator->split_by_scene_point_knowledge(_tracks_alive);
+    const auto [abs_pose_mat, abs_pose_est_successful] =
+        _abs_pose_estimator.estimate_pose(_tracks_alive_with_known_scene_point,
+                                          _pose_curr, _camera_corrected,
+                                          *_point_cloud_generator);
+    if (!abs_pose_est_successful)
+    {
+      SARA_LOGI(logger, "Failed to estimate the absolute pose!");
+      return false;
+    }
+
+    // 7. Update the current absolute pose, which was initialized dummily.
+    abs_pose_curr = QuaternionBasedPose<double>{
+        Eigen::Quaterniond{abs_pose_mat.leftCols<3>()},  //
+        abs_pose_mat.col(3)                              //
+    };
+    _pose_graph[_pose_curr].pose = abs_pose_curr;
+  }
+
+  // 8. Grow point cloud by triangulation.
+  //
+  // TODO: don't add 3D scene points that are too far, like points in the sky
+  const auto frame_corrected = _distortion_corrector->frame_rgb8();
+  _point_cloud_generator->grow_point_cloud(_tracks_alive_without_scene_point,
+                                           frame_corrected, pose_edge,
+                                           _camera_corrected);
+
+  // The rotation is expressed in the camera coordinates.
+  // But the calculation is done in the automotive/aeronautics coordinate
+  // system.
+  //
+  // The z-coordinate of the camera coordinates is the x-axis of the
+  // automotive coordinates
+  //
+  // clang-format off
+  static const auto P = (Eigen::Matrix3d{} <<
+     0,  0, 1,
+    -1,  0, 0,
+     0, -1, 0
+  ).finished();
+  // clang-format on
+
+  const auto& R = _pose_graph[pose_edge].motion.R;
+  const Eigen::Matrix3d R_delta_abs = P * R.transpose() * P.transpose();
+  _current_global_rotation = R_delta_abs * _current_global_rotation;
+
+  const auto q_global = Eigen::Quaterniond{_current_global_rotation};
+  auto angles = calculate_yaw_pitch_roll(q_global);
+  static constexpr auto degrees = 180. / M_PI;
+  SARA_LOGI(logger, "Global yaw   = {:0.3f} deg", angles(0) * degrees);
+  SARA_LOGI(logger, "Global pitch = {:0.3f} deg", angles(1) * degrees);
+  SARA_LOGI(logger, "Global roll  = {:0.3f} deg", angles(2) * degrees);
+
+  return true;
+}