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Center-based 3D Object Detection and Tracking

Introduction

[ALGORITHM]

We implement CenterPoint and provide the result and checkpoints on nuScenes dataset.

We follow the below style to name config files. Contributors are advised to follow the same style. {xxx} is required field and [yyy] is optional.

{model}: model type like centerpoint.

{model setting}: voxel size and voxel type like 01voxel, 02pillar.

{backbone}: backbone type like second.

{neck}: neck type like secfpn.

[dcn]: Whether to use deformable convolution.

[circle]: Whether to use circular nms.

[batch_per_gpu x gpu]: GPUs and samples per GPU, 4x8 is used by default.

{schedule}: training schedule, options are 1x, 2x, 20e, etc. 1x and 2x means 12 epochs and 24 epochs respectively. 20e is adopted in cascade models, which denotes 20 epochs. For 1x/2x, initial learning rate decays by a factor of 10 at the 8/16th and 11/22th epochs. For 20e, initial learning rate decays by a factor of 10 at the 16th and 19th epochs.

{dataset}: dataset like nus-3d, kitti-3d, lyft-3d, scannet-3d, sunrgbd-3d. We also indicate the number of classes we are using if there exist multiple settings, e.g., kitti-3d-3class and kitti-3d-car means training on KITTI dataset with 3 classes and single class, respectively.

@article{yin2021center,
  title={Center-based 3D Object Detection and Tracking},
  author={Yin, Tianwei and Zhou, Xingyi and Kr{\"a}henb{\"u}hl, Philipp},
  journal={CVPR},
  year={2021},
}

Usage

Test time augmentation

We have supported double-flip and scale augmentation during test time. To use test time augmentation, users need to modify the test_pipeline and test_cfg in the config. For example, we change centerpoint_0075voxel_second_secfpn_circlenms_4x8_cyclic_20e_nus.py to the following.

_base_ = './centerpoint_0075voxel_second_secfpn_circlenms' \
         '_4x8_cyclic_20e_nus.py'

model = dict(
    test_cfg=dict(
        pts=dict(
            use_rotate_nms=True,
            max_num=83)))

point_cloud_range = [-54, -54, -5.0, 54, 54, 3.0]
file_client_args = dict(backend='disk')
class_names = [
    'car', 'truck', 'construction_vehicle', 'bus', 'trailer', 'barrier',
    'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone'
]

test_pipeline = [
    dict(
        type='LoadPointsFromFile',
        load_dim=5,
        use_dim=5,
        file_client_args=file_client_args),
    dict(
        type='LoadPointsFromMultiSweeps',
        sweeps_num=9,
        use_dim=[0, 1, 2, 3, 4],
        file_client_args=file_client_args,
        pad_empty_sweeps=True,
        remove_close=True),
    dict(
        type='MultiScaleFlipAug3D',
        img_scale=(1333, 800),
        pts_scale_ratio=[0.95, 1.0, 1.05],
        flip=True,
        pcd_horizontal_flip=True,
        pcd_vertical_flip=True,
        transforms=[
            dict(
                type='GlobalRotScaleTrans',
                rot_range=[0, 0],
                scale_ratio_range=[1., 1.],
                translation_std=[0, 0, 0]),
            dict(type='RandomFlip3D', sync_2d=False),
            dict(
                type='PointsRangeFilter', point_cloud_range=point_cloud_range),
            dict(
                type='DefaultFormatBundle3D',
                class_names=class_names,
                with_label=False),
            dict(type='Collect3D', keys=['points'])
        ])
]

data = dict(
    val=dict(pipeline=test_pipeline), test=dict(pipeline=test_pipeline))

Results

CenterPoint

Backbone Voxel type (voxel size) Dcn Circular nms Mem (GB) Inf time (fps) mAP NDS Download
SECFPN voxel (0.1) ✗ ✓ 4.9 56.19 64.43 model | log
above w/o circle nms voxel (0.1) ✗ ✗ 56.56 64.46
SECFPN voxel (0.1) ✓ ✓ 5.2 56.34 64.81 model | log
above w/o circle nms voxel (0.1) ✓ ✗ 56.60 64.90
SECFPN voxel (0.075) ✗ ✓ 7.8 57.34 65.23 model | log
above w/o circle nms voxel (0.075) ✗ ✗ 57.63 65.39
SECFPN voxel (0.075) ✓ ✓ 8.5 57.27 65.58 model | log
above w/o circle nms voxel (0.075) ✓ ✗ 57.43 65.63
above w/ double flip voxel (0.075) ✓ ✗ 59.73 67.39
above w/ scale tta voxel (0.075) ✓ ✗ 60.43 67.65
above w/ circle nms w/o scale tta voxel (0.075) ✓ ✗ 59.52 67.24
SECFPN pillar (0.2) ✗ ✓ 4.4 49.07 59.66 model | log
above w/o circle nms pillar (0.2) ✗ ✗ 49.12 59.66
SECFPN pillar (0.2) ✓ ✗ 4.6 48.8 59.67 model | log
above w/ circle nms pillar (0.2) ✓ ✓ 48.79 59.65