bevfusion kitti

### BEVFusion Implementation on Kitti Dataset For implementing BEVFusion with the Kitti dataset, adjustments are necessary to adapt from its original setup primarily focused on the NuScenes dataset[^1]. The primary steps involve configuring data creation scripts and modifying configuration files specific to the Kitti dataset. #### Data Preparation Script Adjustment The script used for preparing datasets differs between various datasets due to their unique structures. For adapting `create_data.py` specifically for the Kitti dataset instead of using it as intended originally for NuScenes: ```python # Assuming modifications need to be made within create_data.py or similar file. if 'kitti' in args.dataset.lower(): kitti_preparation() else: # Original logic remains unchanged for other datasets like nuScenes pass ``` This pseudo-code snippet illustrates where conditional checks can direct execution paths based on whether the target is the Kitti dataset. However, actual implementation details would depend heavily upon how closely one wishes to align preprocessing methods across different datasets while ensuring compatibility with existing codebases such as those found at the provided GitHub repository link[^2]. #### Configuration File Adaptation Configuration settings must also reflect changes required by switching datasets. This includes but may not limit to altering parameters related to input/output directories, sensor modalities supported (e.g., LiDAR point clouds vs camera images), annotation formats expected during training/testing phases, etc. Given that no explicit mention exists regarding support directly out-of-the-box for Kitti inside mentioned repositories without customization efforts being applied first; interested parties should look into community contributions or forks which might have already addressed these concerns effectively elsewhere online beyond just official sources alone.