Comments (3)
Thanks for your interest! For the robustness scenarios, we compose a formal robustness benchmark for lidar-camera fusion models. Please follow this repo and paper at https://arxiv.org/abs/2205.14951 for more details. We will release the full benchmark by the end of June 2022.
from bevfusion.
Thanks for your interest! For the robustness scenarios, we compose a formal robustness benchmark for lidar-camera fusion models. Please follow this repo and paper at https://arxiv.org/abs/2205.14951 for more details. We will release the full benchmark by the end of June 2022.
Thanks for such useful tools and benchmark! May I ask whether the robustness evaluation is based on one model or it is based on different models trained with datasets with different noises? In another word, do we need to keep the consistency between training and evaluation dataset?
from bevfusion.
In this paper, data augmentation is consistent between training and evaluation datasets. You are welcome to train and evaluate models under the combined augmentation strategies and share your findings with us.
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Related Issues (20)
- only camera test mAP==0? HOT 3
- Can not to reproduce BEVFusion (CenterPoint+VoxelNet+Dual-Swin-T,mAP:64.2,NDS:68.0)
- Memory consumption and inference time
- Question about nuscenes camera2lidar extrinsic
- please install mmdet in mmdetection-2.11.0 HOT 2
- ball_query_ext.so undefined symbol
- Nuscenes detection errors on validation set
- Excuse me.There is a problem with the preprocessing mini's nuscence dataset, how should I solve it? HOT 2
- Camera stream augmentations
- se_block在你们的模块中真的work了吗
- 找不到'work_dirs/mask_rcnn_dbswin-t_fpn_3x_nuim_cocopre/epoch_36.pth' HOT 1
- 能否添加一个可视化的命令
- Can anyone share me with the model in camera stream, which name is 'models/cam_tf_e20.pth'?
- AssertionError: top_pool_forward miss in module _ext while executingpython demo/pcd_demo.py ${PCD_FILE} ${CONFIG_FILE} ${CHECKPOINT_FILE} [--device ${GPU_ID}] [--score-thr ${SCORE_THR}] [--out-dir ${OUT_DIR}]
- Installation problem: mmcv package configuration HOT 4
- DockerFile有问题 HOT 2
- Code does not match the paper
- There's no pretrained model for DAL-tiny HOT 1
- About Model Training Results
- Training Problems, Pre-trained Models HOT 2
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