I am trying to stitch or project three images from three 120 degree FoV cameras that have been aligned to perfectly cover a 360 degree FoV. They have no overlap so the common stitching techniques using opencv’s Stitcher class will not work as relies on overlapping and detecting matching orb points between the images. Basic concatenation does not work either as they need to be warped or projected on to a cylinder in order to get the correct 360 degree image. I am using the CARLA Simulator and Python library.

Camera setup code:

def create_camera_setup(world, blueprint_library, vehicle):
    # Create a camera blueprint
    camera_bp = blueprint_library.find('sensor.camera.rgb')
    camera_bp.set_attribute('fov', '120')
    camera_bp.set_attribute('image_size_x', '1024')
    camera_bp.set_attribute('image_size_y', '728')

    # Set up forward-facing cameras
    camera_left = spawn_camera(world, camera_bp, vehicle, carla.Transform(
        carla.Location(x=0.2, y=-0.3, z=1.3),  
        carla.Rotation(pitch=0, yaw=-60, roll=0)
    ))
    camera_right = spawn_camera(world, camera_bp, vehicle, carla.Transform(
        carla.Location(x=0.2, y=0.3, z=1.3), 
        carla.Rotation(pitch=0, yaw=60, roll=0)
    ))

    # Set up rear-facing camera
    camera_rear = spawn_camera(world, camera_bp, vehicle, carla.Transform(
        carla.Location(x=0.2, y=0, z=1.3),
        carla.Rotation(pitch=0, yaw=180, roll=0)
    ))

    # Set up IMU
    imu_bp = blueprint_library.find('sensor.other.imu')
    imu = world.spawn_actor(imu_bp, carla.Transform(carla.Location(x=1.6, y=0, z=1.7)), attach_to=vehicle)

    # Set up GPS
    gps_bp = blueprint_library.find('sensor.other.gnss')
    gps = world.spawn_actor(gps_bp, carla.Transform(carla.Location(x=1.6, y=0, z=1.7)), attach_to=vehicle)

    return {
        'camera_left': camera_left,
        'camera_right': camera_right,
        'camera_rear': camera_rear,
        'imu': imu,
        'gps': gps
    }

Current simple concatenation of the images:

def create_360_view(camera_left, camera_right, camera_rear):
    left_img = camera_left.get_data()
    right_img = camera_right.get_data()
    rear_img = camera_rear.get_data()

    combined_img = np.concatenate((left_img, right_img, rear_img), axis=1)
    return combined_img

def display_image(display, image):
    surface = pygame.surfarray.make_surface(image.swapaxes(0, 1))
    display.blit(surface, (0, 0))
    pygame.display.flip()

Camera Aspects: 120 degree fov, 1024×728

Resulting image:
https://ibb.co/FnFhBcp

I have tried some transformation work as seen below but the images have gaps between them when projected:

def create_360_view(camera_left, camera_right, camera_rear):
    left_img = camera_left.get_data()
    right_img = camera_right.get_data()
    rear_img = camera_rear.get_data()

    height, width = left_img.shape[:2]

    panorama_width = width * 3
    panorama = np.zeros((height, panorama_width, 3), dtype=np.uint8)

    place_image_cylindrical(panorama, left_img, 0, width)
    place_image_cylindrical(panorama, right_img, width, width)
    place_image_cylindrical(panorama, rear_img, 2*width, width)

    return panorama

def place_image_cylindrical(panorama, img, start_x, width):
    height, img_width = img.shape[:2]
    cylinder_radius = width / (2 * np.pi / 3)  # 120 degrees = 2π/3 radians
    
    theta = np.linspace(-np.pi/3, np.pi/3, width)
    h = np.arange(height) - height // 2
    theta, h = np.meshgrid(theta, h)
    
    x = np.round(cylinder_radius * np.tan(theta) + img_width / 2).astype(int)
    y = np.round(h * np.cos(theta) + height / 2).astype(int)
    
    mask = (x >= 0) & (x < img_width) & (y >= 0) & (y < height)
    
    panorama_x = np.round(theta * width / (2*np.pi/3) + start_x).astype(int)
    panorama_y = h + height // 2
    
    valid_mask = mask & (panorama_x >= 0) & (panorama_x < panorama.shape[1]) & (panorama_y >= 0) & (panorama_y < panorama.shape[0])
    
    panorama[panorama_y[valid_mask], panorama_x[valid_mask]] = img[y[valid_mask], x[valid_mask]]

Resulting image:
https://ibb.co/Mk2FXtV