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zeliu98 / Group-Free-3D

Group-Free 3D Object Detection via Transformers
MIT License
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Inference Queries #24

Open abhigoku10 opened 2 years ago

abhigoku10 commented 2 years ago

@stupidZZ Thanks for opensourcing the code base , i have few queries

  1. how to run on custom point cloud dataset , should we pre process into any one of the format
  2. how to visualize the results shown in the paper , can you please sharre the visualization code base
  3. i am able to run the model and getting the following results hwo to validate the results by metrics and visualization dict_keys(['sa1_inds', 'sa1_xyz', 'sa1_features', 'sa2_inds', 'sa2_xyz', 'sa2_features', 'sa3_xyz', 'sa3_features', 'sa4_xyz', 'sa4_features', 'fp2_features', 'fp2_xyz', 'fp2_inds', 'seed_inds', 'seed_xyz', 'seed_features', 'seeds_obj_cls_logits', 'query_points_xyz', 'query_points_feature', 'query_points_sample_inds', 'proposal_base_xyz', 'proposal_objectness_scores', 'proposal_center', 'proposal_heading_scores', 'proposal_heading_residuals_normalized', 'proposal_heading_residuals', 'proposal_pred_size', 'proposal_sem_cls_scores', '0head_base_xyz', '0head_objectness_scores', '0head_center', '0head_heading_scores', '0head_heading_residuals_normalized', '0head_heading_residuals', '0head_pred_size', '0head_sem_cls_scores', '1head_base_xyz', '1head_objectness_scores', '1head_center', '1head_heading_scores', '1head_heading_residuals_normalized', '1head_heading_residuals', '1head_pred_size', '1head_sem_cls_scores', '2head_base_xyz', '2head_objectness_scores', '2head_center', '2head_heading_scores', '2head_heading_residuals_normalized', '2head_heading_residuals', '2head_pred_size', '2head_sem_cls_scores', '3head_base_xyz', '3head_objectness_scores', '3head_center', '3head_heading_scores', '3head_heading_residuals_normalized', '3head_heading_residuals', '3head_pred_size', '3head_sem_cls_scores', '4head_base_xyz', '4head_objectness_scores', '4head_center', '4head_heading_scores', '4head_heading_residuals_normalized', '4head_heading_residuals', '4head_pred_size', '4head_sem_cls_scores', 'last_base_xyz', 'last_objectness_scores', 'last_center', 'last_heading_scores', 'last_heading_residuals_normalized', 'last_heading_residuals', 'last_pred_size', 'last_sem_cls_scores', 'point_clouds', 'center_label', 'heading_class_label', 'heading_residual_label', 'size_class_label', 'size_residual_label', 'size_gts', 'sem_cls_label', 'box_label_mask', 'point_obj_mask', 'point_instance_label', 'scan_idx', 'max_gt_bboxes', 'points_hard_topk4_pos_ratio', 'points_hard_topk4_neg_ratio', 'points_hard_topk4_upper_recall_ratio', 'query_points_generation_loss', 'proposal_objectness_label', 'proposal_objectness_mask', 'proposal_object_assignment', 'proposal_pos_ratio', 'proposal_neg_ratio', 'proposal_objectness_loss', 'last_objectness_label', 'last_objectness_mask', 'last_object_assignment', 'last_pos_ratio', 'last_neg_ratio', 'last_objectness_loss', '0head_objectness_label', '0head_objectness_mask', '0head_object_assignment', '0head_pos_ratio', '0head_neg_ratio', '0head_objectness_loss', '1head_objectness_label', '1head_objectness_mask', '1head_object_assignment', '1head_pos_ratio', '1head_neg_ratio', '1head_objectness_loss', '2head_objectness_label', '2head_objectness_mask', '2head_object_assignment', '2head_pos_ratio', '2head_neg_ratio', '2head_objectness_loss', '3head_objectness_label', '3head_objectness_mask', '3head_object_assignment', '3head_pos_ratio', '3head_neg_ratio', '3head_objectness_loss', '4head_objectness_label', '4head_objectness_mask', '4head_object_assignment', '4head_pos_ratio', '4head_neg_ratio', '4head_objectness_loss', 'sum_heads_objectness_loss', 'proposal_center_loss', 'proposal_heading_cls_loss', 'proposal_heading_reg_loss', 'proposal_size_reg_loss', 'proposal_box_loss', 'proposal_sem_cls_loss', 'last_center_loss', 'last_heading_cls_loss', 'last_heading_reg_loss', 'last_size_reg_loss', 'last_box_loss', 'last_sem_cls_loss', '0head_center_loss', '0head_heading_cls_loss', '0head_heading_reg_loss', '0head_size_reg_loss', '0head_box_loss', '0head_sem_cls_loss', '1head_center_loss', '1head_heading_cls_loss', '1head_heading_reg_loss', '1head_size_reg_loss', '1head_box_loss', '1head_sem_cls_loss', '2head_center_loss', '2head_heading_cls_loss', '2head_heading_reg_loss', '2head_size_reg_loss', '2head_box_loss', '2head_sem_cls_loss', '3head_center_loss', '3head_heading_cls_loss', '3head_heading_reg_loss', '3head_size_reg_loss', '3head_box_loss', '3head_sem_cls_loss', '4head_center_loss', '4head_heading_cls_loss', '4head_heading_reg_loss', '4head_size_reg_loss', '4head_box_loss', '4head_sem_cls_loss', 'sum_heads_box_loss', 'sum_heads_sem_cls_loss', 'loss', 'batch_gt_map_cls']) Thanks in advance