@@ -7,43 +7,43 @@ MMDetection3D uses three different coordinate systems. The existence of differen
Despite the variety of datasets and equipment, by summarizing the line of works on 3D object detection we can roughly categorize coordinate systems into three:
- Camera coordinate system -- the coordinate system of most cameras, in which the positive direction of the y-axis points to the ground, the positive direction of the x-axis points to the right, and the positive direction of the z-axis points to the front.
```
up z front
| ^
| /
| /
| /
|/
left ------ 0 ------> x right
|
|
|
|
v
y down
```
```
up z front
| ^
| /
| /
| /
|/
left ------ 0 ------> x right
|
|
|
|
v
y down
```
- LiDAR coordinate system -- the coordinate system of many LiDARs, in which the negative direction of the z-axis points to the ground, the positive direction of the x-axis points to the front, and the positive direction of the y-axis points to the left.
```
z up x front
^ ^
| /
| /
| /
|/
y left <------ 0 ------ right
```
```
z up x front
^ ^
| /
| /
| /
|/
y left <------ 0 ------ right
```
- Depth coordinate system -- the coordinate system used by VoteNet, H3DNet, etc., in which the negative direction of the z-axis points to the ground, the positive direction of the x-axis points to the right, and the positive direction of the y-axis points to the front.
```
z up y front
^ ^
| /
| /
| /
|/
left ------ 0 ------> x right
```
The definition of coordinate systems in this tutorial is actually **more than just defining the three axes**. For a box in the form of ``$$`(x, y, z, dx, dy, dz, r)`$$``, our coordinate systems also define how to interpret the box dimensions ``$$`(dx, dy, dz)`$$`` and the yaw angle ``$$`r`$$``.
```
z up y front
^ ^
| /
| /
| /
|/
left ------ 0 ------> x right
```
The definition of coordinate systems in this tutorial is actually **more than just defining the three axes**. For a box in the form of ``$$`(x, y, z, dx, dy, dz, r)`$$``, our coordinate systems also define how to interpret the box dimensions ``$$`(dx, dy, dz)`$$`` and the yaw angle ``$$`r`$$``.
The illustration of the three coordinate systems is shown below:
...
...
@@ -55,13 +55,13 @@ We will stick to the three coordinate systems defined in this tutorial in the fu
## Definition of the yaw angle
Please refer to [wikipedia](https://en.wikipedia.org/wiki/Euler_angles#Tait%E2%80%93Bryan_angles) for the standard definition of the yaw angle. In object detection, we choose an axis as the gravity axis, and a reference direction on the plane ``$$`\Pi`$$`` perpendicular to the gravity axis, then the reference direction has a yaw angle of 0, and other directions on ``$$`\Pi`$$`` have non-zero yaw angles depending on its angle with the reference direction.
Please refer to [wikipedia](https://en.wikipedia.org/wiki/Euler_angles#Tait%E2%80%93Bryan_angles) for the standard definition of the yaw angle. In object detection, we choose an axis as the gravity axis, and a reference direction on the plane ``$$`\Pi`$$`` perpendicular to the gravity axis, then the reference direction has a yaw angle of 0, and other directions on ``$$`\Pi`$$`` have non-zero yaw angles depending on its angle with the reference direction.
Currently, for all supported datasets, annotations do not include pitch angle and roll angle, which means we need only consider the yaw angle when predicting boxes and calculating overlap between boxes.
In MMDetection3D, all three coordinate systems are right-handed coordinate systems, which means the ascending direction of the yaw angle is counter-clockwise if viewed from the negative direction of the gravity axis (the axis is pointing at one's eyes).
The figure below shows that, in this right-handed coordinate system, if we set the positive direction of the x-axis as a reference direction, then the positive direction of the y-axis has a yaw angle of ``$$`\frac{\pi}{2}`$$``.
The figure below shows that, in this right-handed coordinate system, if we set the positive direction of the x-axis as a reference direction, then the positive direction of the y-axis has a yaw angle of ``$$`\frac{\pi}{2}`$$``.
```
z up y front (yaw=0.5*pi)
...
...
@@ -92,9 +92,9 @@ __|____|____|____|______\ x right
## Definition of the box dimensions
The definition of the box dimensions cannot be disentangled with the definition of the yaw angle. In the previous section, we said that the direction of a box is defined to be parallel with the x-axis if its yaw angle is 0. Then naturally, the dimension of a box which corresponds to the x-axis should be ``$$`dx`$$``. However, this is not always the case in some datasets (we will address that later).
The definition of the box dimensions cannot be disentangled with the definition of the yaw angle. In the previous section, we said that the direction of a box is defined to be parallel with the x-axis if its yaw angle is 0. Then naturally, the dimension of a box which corresponds to the x-axis should be ``$$`dx`$$``. However, this is not always the case in some datasets (we will address that later).
The following figures show the meaning of the correspondence between the x-axis and ``$$`dx`$$``, and between the y-axis and ``$$`dy`$$``.
The following figures show the meaning of the correspondence between the x-axis and ``$$`dx`$$``, and between the y-axis and ``$$`dy`$$``.
```
y front
...
...
@@ -111,7 +111,7 @@ __|____|____|____|______\ x right
| dy
```
Note that the box direction is always parallel with the edge ``$$`dx`$$``.
Note that the box direction is always parallel with the edge ``$$`dx`$$``.
```
y front
...
...
@@ -138,14 +138,12 @@ In SECOND, the LiDAR coordinate system for a box is defined as follows (a bird's
For each box, the dimensions are ``$$`(w, l, h)`$$``, and the reference direction for the yaw angle is the positive direction of the y axis. For more details, refer to the [repo](https://github.com/traveller59/second.pytorch#concepts).
For each box, the dimensions are `` $$`(w, l, h)`$$ ``, and the reference direction for the yaw angle is the positive direction of the y axis. For more details, refer to the [repo](https://github.com/traveller59/second.pytorch#concepts).
Our LiDAR coordinate system has two changes:
- The yaw angle is defined to be right-handed instead of left-handed for consistency;
- The box dimensions are ``$$`(l, w, h)`$$`` instead of ``$$`(w, l, h)`$$``, since ``$$`w`$$`` corresponds to ``$$`dy`$$`` and ``$$`l`$$`` corresponds to ``$$`dx`$$`` in KITTI.
- The box dimensions are ``$$`(l, w, h)`$$`` instead of ``$$`(w, l, h)`$$``, since ``$$`w`$$`` corresponds to ``$$`dy`$$`` and ``$$`l`$$`` corresponds to ``$$`dx`$$`` in KITTI.
### Waymo
...
...
@@ -153,7 +151,7 @@ We use the KITTI-format data of Waymo dataset. Therefore, KITTI and Waymo also s
### NuScenes
NuScenes provides a toolkit for evaluation, in which each box is wrapped into a `Box` instance. The coordinate system of `Box` is different from our LiDAR coordinate system in that the first two elements of the box dimension correspond to ``$$`(dy, dx)`$$``, or ``$$`(w, l)`$$``, respectively, instead of the reverse. For more details, please refer to the NuScenes [tutorial](https://github.com/open-mmlab/mmdetection3d/blob/master/docs/en/datasets/nuscenes_det.md#notes).
NuScenes provides a toolkit for evaluation, in which each box is wrapped into a `Box` instance. The coordinate system of `Box` is different from our LiDAR coordinate system in that the first two elements of the box dimension correspond to ``$$`(dy, dx)`$$``, or ``$$`(w, l)`$$``, respectively, instead of the reverse. For more details, please refer to the NuScenes [tutorial](https://github.com/open-mmlab/mmdetection3d/blob/master/docs/en/datasets/nuscenes_det.md#notes).
Readers may refer to the [NuScenes development kit](https://github.com/nutonomy/nuscenes-devkit/tree/master/python-sdk/nuscenes/eval/detection) for the definition of a [NuScenes box](https://github.com/nutonomy/nuscenes-devkit/blob/2c6a752319f23910d5f55cc995abc547a9e54142/python-sdk/nuscenes/utils/data_classes.py#L457) and implementation of [NuScenes evaluation](https://github.com/nutonomy/nuscenes-devkit/blob/master/python-sdk/nuscenes/eval/detection/evaluate.py).
...
...
@@ -185,25 +183,25 @@ Take the conversion between our Camera coordinate system and LiDAR coordinate sy
First, for points and box centers, the coordinates before and after the conversion satisfy the following relationship:
-``$$`x_{LiDAR}=z_{camera}`$$``
-``$$`y_{LiDAR}=-x_{camera}`$$``
-``$$`z_{LiDAR}=-y_{camera}`$$``
-``$$`x_{LiDAR}=z_{camera}`$$``
-``$$`y_{LiDAR}=-x_{camera}`$$``
-``$$`z_{LiDAR}=-y_{camera}`$$``
Then, the box dimensions before and after the conversion satisfy the following relationship:
-``$$`dx_{LiDAR}=dx_{camera}`$$``
-``$$`dy_{LiDAR}=dz_{camera}`$$``
-``$$`dz_{LiDAR}=dy_{camera}`$$``
-``$$`dx_{LiDAR}=dx_{camera}`$$``
-``$$`dy_{LiDAR}=dz_{camera}`$$``
-``$$`dz_{LiDAR}=dy_{camera}`$$``
Finally, the yaw angle should also be converted:
-``$$`r_{LiDAR}=-\frac{\pi}{2}-r_{camera}`$$``
-``$$`r_{LiDAR}=-\frac{\pi}{2}-r_{camera}`$$``
See the code [here](https://github.com/open-mmlab/mmdetection3d/blob/master/mmdet3d/core/bbox/structures/box_3d_mode.py) for more details.
### Bird's Eye View
The BEV of a camera coordinate system box is ``$$`(x, z, dx, dz, -r)`$$`` if the 3D box is ``$$`(x, y, z, dx, dy, dz, r)`$$``. The inversion of the sign of the yaw angle is because the positive direction of the gravity axis of the Camera coordinate system points to the ground.
The BEV of a camera coordinate system box is ``$$`(x, z, dx, dz, -r)`$$`` if the 3D box is ``$$`(x, y, z, dx, dy, dz, r)`$$``. The inversion of the sign of the yaw angle is because the positive direction of the gravity axis of the Camera coordinate system points to the ground.
See the code [here](https://github.com/open-mmlab/mmdetection3d/blob/master/mmdet3d/core/bbox/structures/cam_box3d.py) for more details.
...
...
@@ -225,18 +223,18 @@ For each box related op, we have marked the type of boxes to which we can apply
No. For example, in KITTI, we need a calibration matrix when converting from Camera coordinate system to LiDAR coordinate system.
#### Q3: How does a phase difference of ``$$`2\pi`$$`` in the yaw angle of a box affect evaluation?
#### Q3: How does a phase difference of ``$$`2\pi`$$`` in the yaw angle of a box affect evaluation?
For IoU calculation, a phase difference of ``$$`2\pi`$$`` in the yaw angle will result in the same box, thus not affecting evaluation.
For IoU calculation, a phase difference of ``$$`2\pi`$$`` in the yaw angle will result in the same box, thus not affecting evaluation.
For angle prediction evaluation such as the NDS metric in NuScenes and the AOS metric in KITTI, the angle of predicted boxes will be first standardized, so the phase difference of ``$$`2\pi`$$`` will not change the result.
For angle prediction evaluation such as the NDS metric in NuScenes and the AOS metric in KITTI, the angle of predicted boxes will be first standardized, so the phase difference of ``$$`2\pi`$$`` will not change the result.
#### Q4: How does a phase difference of ``$$`\pi`$$`` in the yaw angle of a box affect evaluation?
#### Q4: How does a phase difference of ``$$`\pi`$$`` in the yaw angle of a box affect evaluation?
For IoU calculation, a phase difference of ``$$`\pi`$$`` in the yaw angle will result in the same box, thus not affecting evaluation.
For IoU calculation, a phase difference of ``$$`\pi`$$`` in the yaw angle will result in the same box, thus not affecting evaluation.
However, for angle prediction evaluation, this will result in the exact opposite direction.
Just think about a car. The yaw angle is the angle between the direction of the car front and the positive direction of the x-axis. If we add ``$$`\pi`$$`` to this angle, the car front will become the car rear.
Just think about a car. The yaw angle is the angle between the direction of the car front and the positive direction of the x-axis. If we add ``$$`\pi`$$`` to this angle, the car front will become the car rear.
For categories such as barrier, the front and the rear have no difference, therefore a phase difference of ``$$`\pi`$$`` will not affect the angle prediction score.
For categories such as barrier, the front and the rear have no difference, therefore a phase difference of ``$$`\pi`$$`` will not affect the angle prediction score.
@@ -222,62 +222,62 @@ There are three ways to concatenate the dataset.
1. If the datasets you want to concatenate are in the same type with different annotation files, you can concatenate the dataset configs like the following.
```python
dataset_A_train = dict(
type='Dataset_A',
ann_file = ['anno_file_1', 'anno_file_2'],
pipeline=train_pipeline
)
```
If the concatenated dataset is used for test or evaluation, this manner supports to evaluate each dataset separately. To test the concatenated datasets as a whole, you can set `separate_eval=False` as below.
```python
dataset_A_train = dict(
type='Dataset_A',
ann_file = ['anno_file_1', 'anno_file_2'],
separate_eval=False,
pipeline=train_pipeline
)
```
```python
dataset_A_train=dict(
type='Dataset_A',
ann_file=['anno_file_1','anno_file_2'],
pipeline=train_pipeline
)
```
If the concatenated dataset is used for test or evaluation, this manner supports to evaluate each dataset separately. To test the concatenated datasets as a whole, you can set `separate_eval=False` as below.
```python
dataset_A_train=dict(
type='Dataset_A',
ann_file=['anno_file_1','anno_file_2'],
separate_eval=False,
pipeline=train_pipeline
)
```
2. In case the dataset you want to concatenate is different, you can concatenate the dataset configs like the following.
```python
dataset_A_train = dict()
dataset_B_train = dict()
data = dict(
imgs_per_gpu=2,
workers_per_gpu=2,
train = [
dataset_A_train,
dataset_B_train
],
val = dataset_A_val,
test = dataset_A_test
)
```
```python
dataset_A_train=dict()
dataset_B_train=dict()
data=dict(
imgs_per_gpu=2,
workers_per_gpu=2,
train=[
dataset_A_train,
dataset_B_train
],
val=dataset_A_val,
test=dataset_A_test
)
```
If the concatenated dataset is used for test or evaluation, this manner also supports to evaluate each dataset separately.
If the concatenated dataset is used for test or evaluation, this manner also supports to evaluate each dataset separately.
3. We also support to define `ConcatDataset` explicitly as the following.
```python
dataset_A_val = dict()
dataset_B_val = dict()
data = dict(
imgs_per_gpu=2,
workers_per_gpu=2,
train=dataset_A_train,
val=dict(
type='ConcatDataset',
datasets=[dataset_A_val, dataset_B_val],
separate_eval=False))
```
This manner allows users to evaluate all the datasets as a single one by setting `separate_eval=False`.
```python
dataset_A_val=dict()
dataset_B_val=dict()
data=dict(
imgs_per_gpu=2,
workers_per_gpu=2,
train=dataset_A_train,
val=dict(
type='ConcatDataset',
datasets=[dataset_A_val,dataset_B_val],
separate_eval=False))
```
This manner allows users to evaluate all the datasets as a single one by setting `separate_eval=False`.
If your config inherits the base config which already sets the `optimizer_config`, you might need `_delete_=True` to override the unnecessary settings in the base config. See the [config documentation](https://mmdetection.readthedocs.io/en/latest/tutorials/config.html) for more details.
If your config inherits the base config which already sets the `optimizer_config`, you might need `_delete_=True` to override the unnecessary settings in the base config. See the [config documentation](https://mmdetection.readthedocs.io/en/latest/tutorials/config.html) for more details.
- __Use momentum schedule to accelerate model convergence__:
We support momentum scheduler to modify model's momentum according to learning rate, which could make the model converge in a faster way.
Momentum scheduler is usually used with LR scheduler, for example, the following config is used in 3D detection to accelerate convergence.
For more details, please refer to the implementation of [CyclicLrUpdater](https://github.com/open-mmlab/mmcv/blob/v1.3.7/mmcv/runner/hooks/lr_updater.py#L358) and [CyclicMomentumUpdater](https://github.com/open-mmlab/mmcv/blob/v1.3.7/mmcv/runner/hooks/momentum_updater.py#L225).
```python
lr_config = dict(
policy='cyclic',
target_ratio=(10, 1e-4),
cyclic_times=1,
step_ratio_up=0.4,
)
momentum_config = dict(
policy='cyclic',
target_ratio=(0.85 / 0.95, 1),
cyclic_times=1,
step_ratio_up=0.4,
)
```
We support momentum scheduler to modify model's momentum according to learning rate, which could make the model converge in a faster way.
Momentum scheduler is usually used with LR scheduler, for example, the following config is used in 3D detection to accelerate convergence.
For more details, please refer to the implementation of [CyclicLrUpdater](https://github.com/open-mmlab/mmcv/blob/v1.3.7/mmcv/runner/hooks/lr_updater.py#L358) and [CyclicMomentumUpdater](https://github.com/open-mmlab/mmcv/blob/v1.3.7/mmcv/runner/hooks/momentum_updater.py#L225).
```python
lr_config=dict(
policy='cyclic',
target_ratio=(10,1e-4),
cyclic_times=1,
step_ratio_up=0.4,
)
momentum_config=dict(
policy='cyclic',
target_ratio=(0.85/0.95,1),
cyclic_times=1,
step_ratio_up=0.4,
)
```
## Customize training schedules
...
...
@@ -153,20 +153,20 @@ We support many other learning rate schedule [here](https://github.com/open-mmla
- add: img_meta (the keys of img_meta is specified by `meta_keys`)
- remove: all other keys except for those specified by `keys`
### Test time augmentation
`MultiScaleFlipAug`
- update: scale, pcd_scale_factor, flip, flip_direction, pcd_horizontal_flip, pcd_vertical_flip with list of augmented data with these specific parameters
## Extend and use custom pipelines
1. Write a new pipeline in any file, e.g., `my_pipeline.py`. It takes a dict as input and return a dict.
*`deploy_cfg` : The path of deploy config file in MMDeploy codebase.
*`model_cfg` : The path of model config file in OpenMMLab codebase.
*`checkpoint` : The path of model checkpoint file.
*`img` : The path of point cloud file or image file that used to convert model.
*`--test-img` : The path of image file that used to test model. If not specified, it will be set to `None`.
*`--work-dir` : The path of work directory that used to save logs and models.
*`--calib-dataset-cfg` : Only valid in int8 mode. Config used for calibration. If not specified, it will be set to `None` and use "val" dataset in model config for calibration.
*`--device` : The device used for conversion. If not specified, it will be set to `cpu`.
*`--log-level` : To set log level which in `'CRITICAL', 'FATAL', 'ERROR', 'WARN', 'WARNING', 'INFO', 'DEBUG', 'NOTSET'`. If not specified, it will be set to `INFO`.
*`--show` : Whether to show detection outputs.
*`--dump-info` : Whether to output information for SDK.
-`deploy_cfg` : The path of deploy config file in MMDeploy codebase.
-`model_cfg` : The path of model config file in OpenMMLab codebase.
-`checkpoint` : The path of model checkpoint file.
-`img` : The path of point cloud file or image file that used to convert model.
-`--test-img` : The path of image file that used to test model. If not specified, it will be set to `None`.
-`--work-dir` : The path of work directory that used to save logs and models.
-`--calib-dataset-cfg` : Only valid in int8 mode. Config used for calibration. If not specified, it will be set to `None` and use "val" dataset in model config for calibration.
-`--device` : The device used for conversion. If not specified, it will be set to `cpu`.
-`--log-level` : To set log level which in `'CRITICAL', 'FATAL', 'ERROR', 'WARN', 'WARNING', 'INFO', 'DEBUG', 'NOTSET'`. If not specified, it will be set to `INFO`.
-`--show` : Whether to show detection outputs.
-`--dump-info` : Whether to output information for SDK.
### Example
...
...
@@ -110,12 +110,12 @@ python tools/test.py \
## Supported models
| Model | TorchScript | OnnxRuntime | TensorRT | NCNN | PPLNN | OpenVINO | Model config |
@@ -261,62 +261,62 @@ There are three ways to concatenate the dataset.
1. If the datasets you want to concatenate are in the same type with different annotation files, you can concatenate the dataset configs like the following.
```python
dataset_A_train = dict(
type='Dataset_A',
ann_file = ['anno_file_1', 'anno_file_2'],
pipeline=train_pipeline
)
```
If the concatenated dataset is used for test or evaluation, this manner supports to evaluate each dataset separately. To test the concatenated datasets as a whole, you can set `separate_eval=False` as below.
```python
dataset_A_train = dict(
type='Dataset_A',
ann_file = ['anno_file_1', 'anno_file_2'],
separate_eval=False,
pipeline=train_pipeline
)
```
```python
dataset_A_train=dict(
type='Dataset_A',
ann_file=['anno_file_1','anno_file_2'],
pipeline=train_pipeline
)
```
If the concatenated dataset is used for test or evaluation, this manner supports to evaluate each dataset separately. To test the concatenated datasets as a whole, you can set `separate_eval=False` as below.
```python
dataset_A_train=dict(
type='Dataset_A',
ann_file=['anno_file_1','anno_file_2'],
separate_eval=False,
pipeline=train_pipeline
)
```
2. In case the dataset you want to concatenate is different, you can concatenate the dataset configs like the following.
```python
dataset_A_train = dict()
dataset_B_train = dict()
data = dict(
imgs_per_gpu=2,
workers_per_gpu=2,
train = [
dataset_A_train,
dataset_B_train
],
val = dataset_A_val,
test = dataset_A_test
)
```
```python
dataset_A_train=dict()
dataset_B_train=dict()
If the concatenated dataset is used for test or evaluation, this manner also supports to evaluate each dataset separately.
data=dict(
imgs_per_gpu=2,
workers_per_gpu=2,
train=[
dataset_A_train,
dataset_B_train
],
val=dataset_A_val,
test=dataset_A_test
)
```
If the concatenated dataset is used for test or evaluation, this manner also supports to evaluate each dataset separately.
3. We also support to define `ConcatDataset` explicitly as the following.
```python
dataset_A_val = dict()
dataset_B_val = dict()
```python
dataset_A_val=dict()
dataset_B_val=dict()
data = dict(
imgs_per_gpu=2,
workers_per_gpu=2,
train=dataset_A_train,
val=dict(
type='ConcatDataset',
datasets=[dataset_A_val, dataset_B_val],
separate_eval=False))
```
data=dict(
imgs_per_gpu=2,
workers_per_gpu=2,
train=dataset_A_train,
val=dict(
type='ConcatDataset',
datasets=[dataset_A_val,dataset_B_val],
separate_eval=False))
```
This manner allows users to evaluate all the datasets as a single one by setting `separate_eval=False`.
This manner allows users to evaluate all the datasets as a single one by setting `separate_eval=False`.
**Note:**
...
...
@@ -435,7 +435,6 @@ Here you can refer to the setting of the existing datasets. theoretically, `voxe
if the `point_cloud_range` and `voxel_size` are set to be `[0, -40, -3, 70.4, 40, 1]` and `[0.05, 0.05, 0.1]` respectively, then the shape of intermediate feature map should be `[(1-(-3))/0.1+1, (40-(-40))/0.05, (70.4-0)/0.05]=[41, 1600, 1408]`. More details refers to this [issue](https://github.com/open-mmlab/mmdetection3d/issues/382).
### Adjust Anchor Range and Size in Config
```python
...
...
@@ -450,6 +449,7 @@ anchor_generator=dict(
rotations=[0,1.57],
reshape_out=False),
```
Regarding the setting of `anchor_range`, it is generally adjusted according to dataset. Note that `z` value needs to be adjusted accordingly to the position of the point cloud, please refer to this [issue](https://github.com/open-mmlab/mmdetection3d/issues/986).
Regarding the setting of `anchor_size`, it is usually necessary to count the average length, width and height of the entire training dataset as `anchor_size` to obtain the best results.
python tools/deployment/test_torchserver.py demo/data/kitti/kitti_000008.bin configs/second/hv_second_secfpn_6x8_80e_kitti-3d-car.py checkpoints/hv_second_secfpn_6x8_80e_kitti-3d-car_20200620_230238-393f000c.pth second
```
 
 
# Model Complexity
...
...
@@ -213,7 +213,7 @@ comparisons, but double check it before you adopt it in technical reports or pap
2. Some operators are not counted into FLOPs like GN and custom operators. Refer to [`mmcv.cnn.get_model_complexity_info()`](https://github.com/open-mmlab/mmcv/blob/master/mmcv/cnn/utils/flops_counter.py) for details.
3. We currently only support FLOPs calculation of single-stage models with single-modality input (point cloud or image). We will support two-stage and multi-modality models in the future.
- `--data-root`: the root of the dataset, defaults to `./data/nuimages`.
- `--version`: the version of the dataset, defaults to `v1.0-mini`. To get the full dataset, please use `--version v1.0-train v1.0-val v1.0-mini`
- `--out-dir`: the output directory of annotations and semantic masks, defaults to `./data/nuimages/annotations/`.
- `--nproc`: number of workers for data preparation, defaults to `4`. Larger number could reduce the preparation time as images are processed in parallel.
- `--extra-tag`: extra tag of the annotations, defaults to `nuimages`. This can be used to separate different annotations processed in different time for study.
- `--data-root`: the root of the dataset, defaults to `./data/nuimages`.
- `--version`: the version of the dataset, defaults to `v1.0-mini`. To get the full dataset, please use `--version v1.0-train v1.0-val v1.0-mini`
- `--out-dir`: the output directory of annotations and semantic masks, defaults to `./data/nuimages/annotations/`.
- `--nproc`: number of workers for data preparation, defaults to `4`. Larger number could reduce the preparation time as images are processed in parallel.
- `--extra-tag`: extra tag of the annotations, defaults to `nuimages`. This can be used to separate different annotations processed in different time for study.
More details could be referred to the [doc](https://mmdetection3d.readthedocs.io/en/latest/data_preparation.html) for dataset preparation and [README](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/nuimages/README.md/) for nuImages dataset.
-`points/xxxxx.bin`：下采样后，未与坐标轴平行（即没有对齐）的点云。因为 ScanNet 3D 检测任务将与坐标轴平行的点云作为输入，而 ScanNet 3D 语义分割任务将对齐前的点云作为输入，我们选择存储对齐前的点云和它们的对齐矩阵。请注意：在 3D 检测的预处理流程 [`GlobalAlignment`](https://github.com/open-mmlab/mmdetection3d/blob/9f0b01caf6aefed861ef4c3eb197c09362d26b32/mmdet3d/datasets/pipelines/transforms_3d.py#L423) 后，点云就都是与坐标轴平行的了。
在生成 bin 文件后，您可以简单地构建二进制文件 `create_submission`，并按照[指示](https://github.com/waymo-research/waymo-open-dataset/blob/master/docs/quick_start.md/) 创建一个提交文件。下面是一些示例：
