This Python sample is available with all Metavision Intelligence Plans. The corresponding C++ sample is available only with our Professional plan.

Model 3D Detection and Tracking using Python

The Python bindings of Metavision SDK CV3D provide algorithms to detect and track a 3D object for which the geometry is known.

The sample shows how to:

  • load a 3D model from a file, the 3D object being described using a simple JSON format

  • use algorithms to detect and track the object from the events stream

  • dynamically adapt the size of the time slices to suit the algorithms’ needs

  • visualize the tracked object in the events stream

Thanks to the simple 3D object representation, the algorithms are also capable of detecting and tracking fiducial markers that are widely used in AR/VR applications.

Expected Output

Setup & requirements

The tracking algorithm relies on the capacity to track the edges of an object. As a result, here are some conditions that are important to fulfill to be able to robustly track that object:

  • the edges defined in the 3D model JSON file should precisely match those of the object

  • the contrast at the edges should be strong enough to trigger events in the event-based sensor

  • there should not be too much texture or other edges near the object’s edges (both in the background and on the object itself)

How to start

To run this sample, you will need to provide:

  • a RAW file containing a sequence in which the 3D object to be tracked appears (offline mode),

  • a JSON file containing the description of the 3D object

  • a JSON file containing an initialization camera pose (pose from which the detection algorithm will try to detect the object),

  • a JSON file containing the calibration of the camera used for the record (or the camera used in live mode)

Optionally, you can also provide a BIAS file when the sample is executed in live mode


The description of the 3D object must contain:

  • a list of all the 3D object’s vertices

  • a list of all the 3D object’s edges, an edge being a pair of two indices pointing to its vertices

  • a list of all the 3D object’s faces that themselves consist in:

    • a list of indices pointing to each edge of the face,

    • the face’s normal in the object’s coordinate system


Take a look at one of the Sample Recordings provided with this sample for more details about these files.

To start the sample based on the live stream from your camera, run:


python3 -i <base_path>/object


python -i <base_path>/object

where <base_path> is a path to a folder containing the following files:

└── <base_path>
        ├── calibration.json
        ├── model_init_pose.json
        └── object.json

If you want to start the sample using a RAW file, run:


python3 -r <base_path>/object.raw


python -r <base_path>/object.raw

where <base_path> is a path to a folder containing the following files:

└── <base_path>
        ├── calibration.json
        ├── model_init_pose.json
        ├── object.json
        └── object.raw

To check for additional options:


python3 -h


python -h

Code Overview

Processing Pipeline

Before detecting and tracking a 3D object, the first step is to load its 3D representation. This is done using the metavision_sdk_cv3d.load_model_3d_from_json() function:

# Load 3D Model
model_3d = mv_cv3d.load_model_3d_from_json(args.model_path)
if not model_3d:
    print("Impossible to load the 3D model from " + args.model_path)

Then the metavision_sdk_cv3d.Model3dDetectionAlgorithm and metavision_sdk_cv3d.Model3dTrackingAlgorithm can be instantiated with:

# Detection and Tracking algorithms
detection_algo = Model3dDetectionAlgorithm(camera_geometry, model_3d, time_surface)
tracking_algo = Model3dTrackingAlgorithm(camera_geometry, model_3d, time_surface)

This sample implements the following basic state machine:


Both algorithms process time slices of events and output the new camera’s pose in case of success. Those time slices can be created according to different strategies (i.e. fixed number of events, fixed duration or a mixed condition). Each of these two algorithms has a more appropriate way of receiving events.

The metavision_sdk_cv3d.Model3dTrackingAlgorithm is meant to track an object while the user is moving very fast. To track that object during fast motions the algorithm relies on the camera’s high temporal resolution which makes the object very close from its previous location from one step to another. This hypothesis enables the implementation of very efficient methods (that still remain valid for slow motions). As a consequence, to work correctly, this algorithm has to be called at a high frequency when the user is moving fast but can be called at a very low frequency when the user is moving slowly. One way to get this behavior is to feed the algorithm with time slices having a fixed number of events. By doing so, the output camera poses frequency automatically adapts to the camera’s speed.

In contrast to the metavision_sdk_cv3d.Model3dTrackingAlgorithm, the metavision_sdk_cv3d.Model3dDetectionAlgorithm algorithm is meant to detect an object that is not necessarily very close from its initialization pose while the user is moving relatively slowly. The former point implies that we can no longer rely on the close locality hypothesis and thus have to rely on more costly methods to detect that object. However, the latter point implies that the algorithm can be called at a relative low frequency which compensates its extra cost. As a result, big fixed duration time slices (e.g. 10ms) are well suited for this algorithm.

To address this, the algorithms could have been implemented in an asynchronous way, each producing its result (i.e. calling the asynchronous output callback) according to its configuration (i.e. N events, N us or mixed), but this would have made the synchronization between them complicated during state changes.

Instead, the problem is solved upstreams by using a metavision_core.event_io.RawReader instance. Indeed, this class allows us to change the configuration of the time slices at runtime when a state change is detected (i.e. switching from detection to tracking and vice versa). Here is the pipeline implemented in the sample:


And here is what the full processing loop looks like:

# Process events
while (not reader.is_done()) and (args.process_to is None or reader.current_time < args.process_to):
    # Dispatch system events to the window

    prev_is_tracking = is_tracking

    if is_tracking:
        evs = reader.load_n_events(args.n_events)
        is_tracking = tracking_algo.process_events(evs, T_c_w)
        evs = reader.load_delta_t(args.detection_period_us)
        success, visible_edges, detected_edges = detection_algo.process_events(evs, T_c_w)
        if success:
            # We wait for several detections before considering the model as detected
            # to avoid false positive detections
            n_detection += 1
            is_tracking = (n_detection > args.n_detections)

    # The frame generation algorithm processing can trigger a call to show which can trigger
    # a reset of the tracking if the space bar has been pressed.

    if prev_is_tracking != is_tracking:
        if is_tracking:


We can note from the code that we do not switch to the tracking state as soon as the object is detected. Instead, we wait for N successive detections to reduce false positive ones.


In the sample, the tracking can also be reset by pressing the space bar key


For the display we use the metavision_sdk_core.PeriodicFrameGenerationAlgorithm asynchronous algorithm which generates frames from events at a fixed frame rate. When a new frame is available we draw the model’s edges on top of it using the last known camera’s pose. When the pipeline is in the detection state, tracked and non tracked edges are drawn using different colors:

# Periodic Event Frame Generator
def periodic_frame_gen_cb(ts, cv_frame):
    nonlocal is_tracking
    nonlocal visible_edges
    nonlocal detected_edges

    cv2.putText(cv_frame, str(ts), (0, 10), cv2.FONT_HERSHEY_DUPLEX, 0.5,
                (0, 255, 0) if is_tracking else (0, 0, 255))
    # Be careful, here the events and the 3D model are not rendered in a tightly synchronized way,
    # meaning that some shifts might occur. However, most of the time they should not be noticeable
    if is_tracking:
        visible_edges = mv_cv3d.select_visible_edges(T_c_w, model_3d)
        mv_cv3d.draw_edges(camera_geometry, T_c_w, model_3d, visible_edges, cv_frame, (0, 255, 0))
        cv2.putText(cv_frame, "tracking", (0, 30), cv2.FONT_HERSHEY_DUPLEX, 0.5, (0, 255, 0))
        mv_cv3d.draw_edges(camera_geometry, T_c_w, model_3d, visible_edges, cv_frame, (0, 0, 255))
        mv_cv3d.draw_edges(camera_geometry, T_c_w, model_3d, detected_edges, cv_frame, (0, 255, 0))
        cv2.putText(cv_frame, "detecting", (0, 30), cv2.FONT_HERSHEY_DUPLEX, 0.5, (0, 0, 255))
    if args.out_video:


As pointed out in the code, the background frame and the 3D model’s edges may not be perfectly aligned. However this should be imperceptible in practice.