@@ -41,7 +41,7 @@ Note that we follow the original folder names for clear organization. Please ren
...
@@ -41,7 +41,7 @@ Note that we follow the original folder names for clear organization. Please ren
## Dataset Preparation
## Dataset Preparation
The way to organize Lyft dataset is similar to nuScenes. We also generate the .pkl and .json files which share almost the same structure.
The way to organize Lyft dataset is similar to nuScenes. We also generate the .pkl and .json files which share almost the same structure.
Next, we will mainly focus on the difference between these two datasets. For a more detailed explanation of the info structure, please refer to [nuScenes tutorial](https://github.com/open-mmlab/mmdetection3d/blob/master/docs/datasets/nuscenes_det.md).
Next, we will mainly focus on the difference between these two datasets. For a more detailed explanation of the info structure, please refer to [nuScenes tutorial](https://github.com/open-mmlab/mmdetection3d/blob/master/docs/en/datasets/nuscenes_det.md).
To prepare info files for Lyft, run the following commands:
To prepare info files for Lyft, run the following commands:
The overall process is similar to ScanNet 3D detection task. Please refer to this [section](https://github.com/open-mmlab/mmdetection3d/blob/master/docs/datasets/scannet_det.md#dataset-preparation). Only a few differences and additional information about the 3D semantic segmentation data will be listed below.
The overall process is similar to ScanNet 3D detection task. Please refer to this [section](https://github.com/open-mmlab/mmdetection3d/blob/master/docs/en/datasets/scannet_det.md#dataset-preparation). Only a few differences and additional information about the 3D semantic segmentation data will be listed below.
We provide scripts for multi-modality/single-modality (LiDAR-based/vision-based), indoor/outdoor 3D detection and 3D semantic segmentation demos. The pre-trained models can be downloaded from [model zoo](https://github.com/open-mmlab/mmdetection3d/blob/master/docs/model_zoo.md/). We provide pre-processed sample data from KITTI, SUN RGB-D, nuScenes and ScanNet dataset. You can use any other data following our pre-processing steps.
We provide scripts for multi-modality/single-modality (LiDAR-based/vision-based), indoor/outdoor 3D detection and 3D semantic segmentation demos. The pre-trained models can be downloaded from [model zoo](https://github.com/open-mmlab/mmdetection3d/blob/master/docs/en/model_zoo.md/). We provide pre-processed sample data from KITTI, SUN RGB-D, nuScenes and ScanNet dataset. You can use any other data following our pre-processing steps.
