how to visualize the experiments results

[Wj-costumer]

Hello, I have successfully run the code and I have two questions. First, I'd like to know how to visualize the experiments results and how can I render the nuscenes dataset scene like the pictures shown in your paper, and I would be appreciate it if you could provide the corresponding code or a tutorial! Second, I'm trying to project the track 3d-box on the image but there's something wrong.

As you can see, the coordinates of the boxes on the y-axis are offset downwards as a whole.
boxes_3d = predictor_utils.to_numpy(results[0]['boxes_3d'].tensor) # lidar coordinate bboxes_3d = img_metas[0]['box_type_3d'][0](boxes_3d, 9) # convert to <class LiDARInstance3DBoxes> corners = bboxes_3d.corners # n * 8 * 3 for cam_idx in range(6): lidar_to_image = lidar2img[0][cam_idx] image_idx = images[cam_idx] for i in range(len(bboxes_3d)): corners_lidar = corners[i].squeeze(0).cpu().numpy() # (8, 3) corners_lidar = np.concatenate([corners_lidar, np.ones((corners_lidar.shape[0], 1))], axis = 1) # 8 x 4 corners_image = corners_lidar @ lidar_to_image.T # torch.matmul(lidar_to_image, corners_lidar) # corners_image[:, :2] /= corners_image[:, [2]] # x, y / z if np.all((corners_image[:, 2] > 0) == True) == True: track_id = track_ids[i] track_cls = class_names[track_labels[i]] # ['car', 'truck', ...] color = colors[track_labels[i]] # [(0, 255, 255), ...] # 画3d_track_instance image_idx = draw_cube_on_image(image_idx, corners_image[:, :2], color) corners_image = corners_image[:, :2].astype(int)
As the results 'boxes_3d' are in lidar coordinate, I jusr convert them to the and return their corners. Then I project the corners using array 'lidar2img' on the raw images. Could you help me find the error? Thanks!

[GentleSmile]

We have not released the visualization code because the toolkit is quite complicated to setup. The box center have two formats, i.e., nuscenes format and KITTI format. They are converted here.