Nightmare-n
commented
5 months ago Hello, thank you for your interest. Due to the refactoring of the code, some of the old configs do not exactly match the code. We provide the old configs for reference.
BEVDet:
_base_ = [
'../../../configs/_base_/datasets/nus-3d.py',
'../../../configs/_base_/default_runtime.py'
]
plugin=True
plugin_dir='projects/mmdet3d_plugin/'
# If point cloud range is changed, the models should also change their point
# cloud range accordingly
point_cloud_range = [-51.2, -51.2, -5.0, 51.2, 51.2, 3.0]
voxel_size = [0.2, 0.2, 8]
unified_voxel_size = [0.8, 0.8, 1.6]
frustum_range = [0, 0, 0.0, 1600, 928, 64.0]
frustum_size = [16.0, 16.0, 1.0]
cam_sweep_num = 1
fp16_enabled = True
unified_voxel_shape = [int((point_cloud_range[3] - point_cloud_range[0]) / unified_voxel_size[0]),
int((point_cloud_range[4] - point_cloud_range[1]) / unified_voxel_size[1]),
int((point_cloud_range[5] - point_cloud_range[2]) / unified_voxel_size[2])]
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
# For nuScenes we usually do 10-class detection
class_names = [
'car', 'truck', 'construction_vehicle', 'bus', 'trailer', 'barrier',
'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone'
]
input_modality = dict(
use_lidar=False,
use_camera=True,
use_radar=False,
use_map=False,
use_external=False,
cam_sweep_num=cam_sweep_num)
model = dict(
type='UVTR',
use_grid_mask=True,
img_backbone=dict(
type='MaskConvNeXt',
arch='small',
drop_path_rate=0.2,
out_indices=(2, 3),
norm_out=True,
frozen_stages=1,
init_cfg=dict(type='Pretrained', checkpoint='data/ckpts/processed_convnext_small_1k_224_ema.pth')),
img_neck=dict(
type='CustomFPN',
in_channels=[384, 768],
out_channels=128,
num_outs=1,
start_level=0,
out_ids=[0]),
depth_head=dict(
type='SimpleDepth'),
pts_bbox_head=dict(
type='UVTRHead',
view_cfg=dict(
type='Uni3DVoxelPool',
pc_range=point_cloud_range,
voxel_size=unified_voxel_size,
voxel_shape=unified_voxel_shape,
frustum_range=frustum_range,
frustum_size=frustum_size,
num_convs=3,
kernel_size=(3,3,3),
embed_dim=128,
keep_sweep_dim=True,
fp16_enabled=fp16_enabled,
),
# transformer_cfg
num_query=900,
num_classes=10,
in_channels=128,
sync_cls_avg_factor=True,
with_box_refine=True,
as_two_stage=False,
transformer=dict(
type='Uni3DDETR',
fp16_enabled=fp16_enabled,
decoder=dict(
type='UniTransformerDecoder',
num_layers=6,
return_intermediate=True,
transformerlayers=dict(
type='BaseTransformerLayer',
attn_cfgs=[
dict(
type='MultiheadAttention',
embed_dims=128,
num_heads=8,
dropout=0.1),
dict(
type='UniCrossAtten',
num_points=1,
embed_dims=128,
num_sweeps=cam_sweep_num,
fp16_enabled=fp16_enabled)
],
ffn_cfgs=dict(
type='FFN',
embed_dims=128,
feedforward_channels=512,
num_fcs=2,
ffn_drop=0.1,
act_cfg=dict(type='ReLU', inplace=True),
),
norm_cfg=dict(type='LN'),
operation_order=('self_attn', 'norm', 'cross_attn', 'norm',
'ffn', 'norm'))
)
),
bbox_coder=dict(
type='NMSFreeCoder',
post_center_range=[-61.2, -61.2, -10.0, 61.2, 61.2, 10.0],
pc_range=point_cloud_range,
max_num=300,
voxel_size=voxel_size,
num_classes=10),
positional_encoding=dict(
type='SinePositionalEncoding',
num_feats=64,
normalize=True,
offset=-0.5),
loss_cls=dict(
type='FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=2.0),
loss_bbox=dict(type='L1Loss', loss_weight=0.25),
loss_iou=dict(type='GIoULoss', loss_weight=0.0),
code_weights=[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]),
# model training and testing settings
train_cfg=dict(pts=dict(
assigner=dict(
type='HungarianAssigner3D',
cls_cost=dict(type='FocalLossCost', weight=2.0),
reg_cost=dict(type='BBox3DL1Cost', weight=0.25),
iou_cost=dict(type='IoUCost', weight=0.0), # Fake cost. This is just to make it compatible with DETR head.
pc_range=point_cloud_range))))
dataset_type = 'NuScenesSweepDataset'
data_root = 'data/nuscenes/'
file_client_args = dict(
backend='petrel',
path_mapping=dict({
'../data/nuscenes/': 's3://yanghonghui/nuscenes/',
'data/nuscenes/': 's3://yanghonghui/nuscenes/',
}))
# file_client_args = dict(backend='disk')
train_pipeline = [
dict(type='LoadMultiViewMultiSweepImageFromFiles', sweep_num=cam_sweep_num, to_float32=True, file_client_args=file_client_args),
dict(type='PhotoMetricDistortionMultiViewImage'),
dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True, with_attr_label=False),
dict(
type='UnifiedRotScaleTransFlip',
rot_range=[-0.3925, 0.3925],
scale_ratio_range=[0.95, 1.05]),
dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range),
dict(type='ObjectNameFilter', classes=class_names),
dict(type='NormalizeMultiviewImage', **img_norm_cfg),
dict(type='PadMultiViewImage', size_divisor=32),
dict(type='DefaultFormatBundle3D', class_names=class_names),
dict(type='CollectUnified3D', keys=['gt_bboxes_3d', 'gt_labels_3d', 'img'])
]
test_pipeline = [
dict(type='LoadMultiViewMultiSweepImageFromFiles', sweep_num=cam_sweep_num, to_float32=True, file_client_args=file_client_args),
dict(type='NormalizeMultiviewImage', **img_norm_cfg),
dict(type='PadMultiViewImage', size_divisor=32),
dict(type='DefaultFormatBundle3D', class_names=class_names),
dict(type='CollectUnified3D', keys=['img'])
]
data = dict(
samples_per_gpu=1,
workers_per_gpu=4,
train=dict(
type=dataset_type,
data_root=data_root,
ann_file=data_root + 'nuscenes_unified_infos_train.pkl', # please change to your own info file
pipeline=train_pipeline,
classes=class_names,
modality=input_modality,
test_mode=False,
use_valid_flag=True,
box_type_3d='LiDAR',
load_interval=2),
val=dict(type=dataset_type, pipeline=test_pipeline, classes=class_names, modality=input_modality,
ann_file=data_root + "nuscenes_unified_infos_val.pkl"), # please change to your own info file
test=dict(type=dataset_type, pipeline=test_pipeline, classes=class_names, modality=input_modality,
ann_file=data_root + "nuscenes_unified_infos_val.pkl")) # please change to your own info file
optimizer = dict(
type='AdamW',
lr=2e-4,
paramwise_cfg=dict(
custom_keys={
'img_backbone': dict(lr_mult=0.1),
}),
weight_decay=0.01)
optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
# learning policy
lr_config = dict(
policy='CosineAnnealing',
warmup='linear',
warmup_iters=500,
warmup_ratio=1.0 / 3,
min_lr_ratio=1e-3)
total_epochs = 12
evaluation = dict(interval=4, pipeline=test_pipeline)
checkpoint_config = dict(max_keep_ckpts=1, interval=1)
find_unused_parameters = True
runner = dict(type='EpochBasedRunner', max_epochs=total_epochs)
load_from = None
resume_from = None
# fp16 setting
fp16 = dict(loss_scale=32.)BEVformer:
_base_ = [
'../../../configs/_base_/datasets/nus-3d.py',
'../../../configs/_base_/default_runtime.py'
]
plugin=True
plugin_dir='projects/mmdet3d_plugin/'
# If point cloud range is changed, the models should also change their point
# cloud range accordingly
point_cloud_range = [-51.2, -51.2, -5.0, 51.2, 51.2, 3.0]
voxel_size = [0.2, 0.2, 8]
unified_voxel_size = [0.8, 0.8, 1.6]
cam_sweep_num = 1
fp16_enabled = True
unified_voxel_shape = [int((point_cloud_range[3] - point_cloud_range[0]) / unified_voxel_size[0]),
int((point_cloud_range[4] - point_cloud_range[1]) / unified_voxel_size[1]),
int((point_cloud_range[5] - point_cloud_range[2]) / unified_voxel_size[2])]
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
# For nuScenes we usually do 10-class detection
class_names = [
'car', 'truck', 'construction_vehicle', 'bus', 'trailer', 'barrier',
'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone'
]
input_modality = dict(
use_lidar=False,
use_camera=True,
use_radar=False,
use_map=False,
use_external=False,
cam_sweep_num=cam_sweep_num)
model = dict(
type='UVTR',
use_grid_mask=True,
img_backbone=dict(
type='MaskConvNeXt',
arch='small',
drop_path_rate=0.2,
out_indices=(0, 1, 2, 3),
norm_out=True,
frozen_stages=1,
init_cfg=dict(type='Pretrained', checkpoint='data/ckpts/processed_convnext_small_1k_224_ema.pth')),
img_neck=dict(
type='FPN',
in_channels=[96, 192, 384, 768],
out_channels=128,
start_level=1,
add_extra_convs='on_output',
num_outs=4,
relu_before_extra_convs=True),
depth_head=None,
pts_bbox_head=dict(
type='UVTRHead',
view_cfg=dict(
type='Uni3DCrossAttn',
pc_range=point_cloud_range,
voxel_size=unified_voxel_size,
voxel_shape=unified_voxel_shape,
num_convs=3,
kernel_size=(3,3,3),
embed_dim=128,
num_levels=4,
num_points=2,
normalize=True,
keep_sweep_dim=True,
fp16_enabled=fp16_enabled,
),
# transformer_cfg
num_query=900,
num_classes=10,
in_channels=128,
sync_cls_avg_factor=True,
with_box_refine=True,
as_two_stage=False,
transformer=dict(
type='Uni3DDETR',
fp16_enabled=fp16_enabled,
decoder=dict(
type='UniTransformerDecoder',
num_layers=6,
return_intermediate=True,
transformerlayers=dict(
type='BaseTransformerLayer',
attn_cfgs=[
dict(
type='MultiheadAttention',
embed_dims=128,
num_heads=8,
dropout=0.1),
dict(
type='UniCrossAtten',
num_points=1,
embed_dims=128,
num_sweeps=cam_sweep_num,
fp16_enabled=fp16_enabled)
],
ffn_cfgs=dict(
type='FFN',
embed_dims=128,
feedforward_channels=512,
num_fcs=2,
ffn_drop=0.1,
act_cfg=dict(type='ReLU', inplace=True),
),
norm_cfg=dict(type='LN'),
operation_order=('self_attn', 'norm', 'cross_attn', 'norm',
'ffn', 'norm'))
)
),
bbox_coder=dict(
type='NMSFreeCoder',
post_center_range=[-61.2, -61.2, -10.0, 61.2, 61.2, 10.0],
pc_range=point_cloud_range,
max_num=300,
voxel_size=voxel_size,
num_classes=10),
positional_encoding=dict(
type='SinePositionalEncoding',
num_feats=64,
normalize=True,
offset=-0.5),
loss_cls=dict(
type='FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=2.0),
loss_bbox=dict(type='L1Loss', loss_weight=0.25),
loss_iou=dict(type='GIoULoss', loss_weight=0.0),
code_weights=[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]),
# model training and testing settings
train_cfg=dict(pts=dict(
assigner=dict(
type='HungarianAssigner3D',
cls_cost=dict(type='FocalLossCost', weight=2.0),
reg_cost=dict(type='BBox3DL1Cost', weight=0.25),
iou_cost=dict(type='IoUCost', weight=0.0), # Fake cost. This is just to make it compatible with DETR head.
pc_range=point_cloud_range))))
dataset_type = 'NuScenesSweepDataset'
data_root = 'data/nuscenes/'
file_client_args = dict(
backend='petrel',
path_mapping=dict({
'../data/nuscenes/': 's3://yanghonghui/nuscenes/',
'data/nuscenes/': 's3://yanghonghui/nuscenes/',
}))
# file_client_args = dict(backend='disk')
train_pipeline = [
dict(type='LoadMultiViewMultiSweepImageFromFiles', sweep_num=cam_sweep_num, to_float32=True, file_client_args=file_client_args),
dict(type='PhotoMetricDistortionMultiViewImage'),
dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True, with_attr_label=False),
dict(
type='UnifiedRotScaleTransFlip',
rot_range=[-0.3925, 0.3925],
scale_ratio_range=[0.95, 1.05]),
dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range),
dict(type='ObjectNameFilter', classes=class_names),
dict(type='NormalizeMultiviewImage', **img_norm_cfg),
dict(type='PadMultiViewImage', size_divisor=32),
dict(type='DefaultFormatBundle3D', class_names=class_names),
dict(type='CollectUnified3D', keys=['gt_bboxes_3d', 'gt_labels_3d', 'img'])
]
test_pipeline = [
dict(type='LoadMultiViewMultiSweepImageFromFiles', sweep_num=cam_sweep_num, to_float32=True, file_client_args=file_client_args),
dict(type='NormalizeMultiviewImage', **img_norm_cfg),
dict(type='PadMultiViewImage', size_divisor=32),
dict(type='DefaultFormatBundle3D', class_names=class_names),
dict(type='CollectUnified3D', keys=['img'])
]
data = dict(
samples_per_gpu=1,
workers_per_gpu=4,
train=dict(
type=dataset_type,
data_root=data_root,
ann_file=data_root + 'nuscenes_unified_infos_train.pkl', # please change to your own info file
pipeline=train_pipeline,
classes=class_names,
modality=input_modality,
test_mode=False,
use_valid_flag=True,
box_type_3d='LiDAR',
load_interval=2),
val=dict(type=dataset_type, pipeline=test_pipeline, classes=class_names, modality=input_modality,
ann_file=data_root + "nuscenes_unified_infos_val.pkl"), # please change to your own info file
test=dict(type=dataset_type, pipeline=test_pipeline, classes=class_names, modality=input_modality,
ann_file=data_root + "nuscenes_unified_infos_val.pkl")) # please change to your own info file
optimizer = dict(
type='AdamW',
lr=2e-4,
paramwise_cfg=dict(
custom_keys={
'img_backbone': dict(lr_mult=0.1),
}),
weight_decay=0.01)
optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
# learning policy
lr_config = dict(
policy='CosineAnnealing',
warmup='linear',
warmup_iters=500,
warmup_ratio=1.0 / 3,
min_lr_ratio=1e-3)
total_epochs = 12
evaluation = dict(interval=4, pipeline=test_pipeline)
checkpoint_config = dict(max_keep_ckpts=1, interval=1)
find_unused_parameters = True
runner = dict(type='EpochBasedRunner', max_epochs=total_epochs)
load_from = None
resume_from = None
# fp16 setting
fp16 = dict(loss_scale=32.)BEVDepth:
_base_ = [
'../../../configs/_base_/datasets/nus-3d.py',
'../../../configs/_base_/default_runtime.py'
]
plugin=True
plugin_dir='projects/mmdet3d_plugin/'
# If point cloud range is changed, the models should also change their point
# cloud range accordingly
point_cloud_range = [-51.2, -51.2, -5.0, 51.2, 51.2, 3.0]
voxel_size = [0.2, 0.2, 8]
unified_voxel_size = [0.8, 0.8, 1.6]
frustum_range = [0, 0, 1.0, 1600, 928, 60.0]
frustum_size = [16.0, 16.0, 0.5]
cam_sweep_num = 1
fp16_enabled = True
unified_voxel_shape = [int((point_cloud_range[3] - point_cloud_range[0]) / unified_voxel_size[0]),
int((point_cloud_range[4] - point_cloud_range[1]) / unified_voxel_size[1]),
int((point_cloud_range[5] - point_cloud_range[2]) / unified_voxel_size[2])]
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
# For nuScenes we usually do 10-class detection
class_names = [
'car', 'truck', 'construction_vehicle', 'bus', 'trailer', 'barrier',
'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone'
]
input_modality = dict(
use_lidar=False,
use_camera=True,
use_radar=False,
use_map=False,
use_external=False,
cam_sweep_num=cam_sweep_num)
model = dict(
type='UVTR',
use_grid_mask=True,
img_backbone=dict(
type='MaskConvNeXt',
arch='small',
drop_path_rate=0.2,
out_indices=(2, 3),
norm_out=True,
frozen_stages=1,
init_cfg=dict(type='Pretrained', checkpoint='data/ckpts/processed_convnext_small_1k_224_ema.pth')),
img_neck=dict(
type='CustomFPN',
in_channels=[384, 768],
out_channels=128,
num_outs=1,
start_level=0,
out_ids=[0]),
depth_head=dict(
type='ComplexDepth',
use_dcn=False,
aspp_mid_channels=96),
pts_bbox_head=dict(
type='UVTRHead',
view_cfg=dict(
type='Uni3DVoxelPoolDepth',
pc_range=point_cloud_range,
voxel_size=unified_voxel_size,
voxel_shape=unified_voxel_shape,
frustum_range=frustum_range,
frustum_size=frustum_size,
num_convs=3,
kernel_size=(3,3,3),
embed_dim=128,
keep_sweep_dim=True,
fp16_enabled=fp16_enabled,
loss_cfg=dict(
close_radius=3.0,
depth_loss_weights=[1.0]
),
),
# transformer_cfg
num_query=900,
num_classes=10,
in_channels=128,
sync_cls_avg_factor=True,
with_box_refine=True,
as_two_stage=False,
transformer=dict(
type='Uni3DDETR',
fp16_enabled=fp16_enabled,
decoder=dict(
type='UniTransformerDecoder',
num_layers=6,
return_intermediate=True,
transformerlayers=dict(
type='BaseTransformerLayer',
attn_cfgs=[
dict(
type='MultiheadAttention',
embed_dims=128,
num_heads=8,
dropout=0.1),
dict(
type='UniCrossAtten',
num_points=1,
embed_dims=128,
num_sweeps=cam_sweep_num,
fp16_enabled=fp16_enabled)
],
ffn_cfgs=dict(
type='FFN',
embed_dims=128,
feedforward_channels=512,
num_fcs=2,
ffn_drop=0.1,
act_cfg=dict(type='ReLU', inplace=True),
),
norm_cfg=dict(type='LN'),
operation_order=('self_attn', 'norm', 'cross_attn', 'norm',
'ffn', 'norm'))
)
),
bbox_coder=dict(
type='NMSFreeCoder',
post_center_range=[-61.2, -61.2, -10.0, 61.2, 61.2, 10.0],
pc_range=point_cloud_range,
max_num=300,
voxel_size=voxel_size,
num_classes=10),
positional_encoding=dict(
type='SinePositionalEncoding',
num_feats=64,
normalize=True,
offset=-0.5),
loss_cls=dict(
type='FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=2.0),
loss_bbox=dict(type='L1Loss', loss_weight=0.25),
loss_iou=dict(type='GIoULoss', loss_weight=0.0),
code_weights=[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]),
# model training and testing settings
train_cfg=dict(pts=dict(
assigner=dict(
type='HungarianAssigner3D',
cls_cost=dict(type='FocalLossCost', weight=2.0),
reg_cost=dict(type='BBox3DL1Cost', weight=0.25),
iou_cost=dict(type='IoUCost', weight=0.0), # Fake cost. This is just to make it compatible with DETR head.
pc_range=point_cloud_range))))
dataset_type = 'NuScenesSweepDataset'
data_root = 'data/nuscenes/'
file_client_args = dict(
backend='petrel',
path_mapping=dict({
'../data/nuscenes/': 's3://yanghonghui/nuscenes/',
'data/nuscenes/': 's3://yanghonghui/nuscenes/',
}))
# file_client_args = dict(backend='disk')
train_pipeline = [
dict(type='LoadPointsFromFile', coord_type='LIDAR', load_dim=5, use_dim=5, file_client_args=file_client_args),
dict(type='LoadMultiViewMultiSweepImageFromFiles', sweep_num=cam_sweep_num, to_float32=True, file_client_args=file_client_args),
dict(type='PhotoMetricDistortionMultiViewImage'),
dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True, with_attr_label=False),
dict(
type='UnifiedRotScaleTransFlip',
rot_range=[-0.3925, 0.3925],
scale_ratio_range=[0.95, 1.05]),
dict(type='PointsRangeFilter', point_cloud_range=point_cloud_range),
dict(type='PointShuffle'),
dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range),
dict(type='ObjectNameFilter', classes=class_names),
dict(type='NormalizeMultiviewImage', **img_norm_cfg),
dict(type='PadMultiViewImage', size_divisor=32),
dict(type='DefaultFormatBundle3D', class_names=class_names),
dict(type='CollectUnified3D', keys=['gt_bboxes_3d', 'gt_labels_3d', 'points', 'img'])
]
test_pipeline = [
dict(type='LoadMultiViewMultiSweepImageFromFiles', sweep_num=cam_sweep_num, to_float32=True, file_client_args=file_client_args),
dict(type='NormalizeMultiviewImage', **img_norm_cfg),
dict(type='PadMultiViewImage', size_divisor=32),
dict(type='DefaultFormatBundle3D', class_names=class_names),
dict(type='CollectUnified3D', keys=['img'])
]
data = dict(
samples_per_gpu=1,
workers_per_gpu=4,
train=dict(
type=dataset_type,
data_root=data_root,
ann_file=data_root + 'nuscenes_unified_infos_train.pkl', # please change to your own info file
pipeline=train_pipeline,
classes=class_names,
modality=input_modality,
test_mode=False,
use_valid_flag=True,
box_type_3d='LiDAR',
load_interval=2),
val=dict(type=dataset_type, pipeline=test_pipeline, classes=class_names, modality=input_modality,
ann_file=data_root + "nuscenes_unified_infos_val.pkl"), # please change to your own info file
test=dict(type=dataset_type, pipeline=test_pipeline, classes=class_names, modality=input_modality,
ann_file=data_root + "nuscenes_unified_infos_val.pkl")) # please change to your own info file
optimizer = dict(
type='AdamW',
lr=2e-4,
paramwise_cfg=dict(
custom_keys={
'img_backbone': dict(lr_mult=0.1),
}),
weight_decay=0.01)
optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
# learning policy
lr_config = dict(
policy='CosineAnnealing',
warmup='linear',
warmup_iters=500,
warmup_ratio=1.0 / 3,
min_lr_ratio=1e-3)
total_epochs = 12
evaluation = dict(interval=4, pipeline=test_pipeline)
checkpoint_config = dict(max_keep_ckpts=1, interval=1)
find_unused_parameters = True
runner = dict(type='EpochBasedRunner', max_epochs=total_epochs)
load_from = None
resume_from = None
# fp16 setting
fp16 = dict(loss_scale=32.)
hottruong98
Hi. Thank you for your interesting work. I have a question about the ablation study of your paper in Table 5. Did you mean you only take the view transformation from BEVDet, BEVDepth and BEVFormer while keeping other components like image backbone, detection head, ... same as in UVTR-cam? Could you please provide the config files for these studies? Thank you in advance.