Release the codes of PV-RCNN++, update OpenPCDet to v0.5.2 #737

Release the codes of PV-RCNN++, update OpenPCDet to v0.5.2

README.md

@@ -4,9 +4,11 @@
 
 `OpenPCDet` is a clear, simple, self-contained open source project for LiDAR-based 3D object detection. 
 
-It is also the official code release of [`[PointRCNN]`](https://arxiv.org/abs/1812.04244), [`[Part-A2-Net]`](https://arxiv.org/abs/1907.03670), [`[PV-RCNN]`](https://arxiv.org/abs/1912.13192) and [`[Voxel R-CNN]`](https://arxiv.org/abs/2012.15712). 
+It is also the official code release of [`[PointRCNN]`](https://arxiv.org/abs/1812.04244), [`[Part-A2-Net]`](https://arxiv.org/abs/1907.03670), [`[PV-RCNN]`](https://arxiv.org/abs/1912.13192), [`[Voxel R-CNN]`](https://arxiv.org/abs/2012.15712) and [`[PV-RCNN++]`](https://arxiv.org/abs/2102.00463). 
 
-**NEW**: `OpenPCDet` has been updated to `v0.5.0` (Dec. 2021).
+**Highlights**: 
+* `OpenPCDet` has been updated to `v0.5.2` (Jan. 2022).
+* The codes of PV-RCNN++ has been supported.
 
 ## Overview
 - [Changelog](#changelog)
@@ -19,6 +21,12 @@ It is also the official code release of [`[PointRCNN]`](https://arxiv.org/abs/18
 
 
 ## Changelog
+[2021-01-05] **NEW:** Update `OpenPCDet` to v0.5.2:
+* The code of [PV-RCNN++](https://arxiv.org/abs/2102.00463) has been released to this repo, with higher performance, faster training/inference speed and less memory consumption than PV-RCNN.
+* Add performance of several models trained with full training set of [Waymo Open Dataset](#waymo-open-dataset-baselines).
+* Support Lyft dataset, see the pull request [here](https://github.com/open-mmlab/OpenPCDet/pull/720).
+
+
 [2021-12-09] **NEW:**  Update `OpenPCDet` to v0.5.1:
 * Add PointPillar related baseline configs/results on [Waymo Open Dataset](#waymo-open-dataset-baselines).
 * Support Pandaset dataloader, see the pull request [here](https://github.com/open-mmlab/OpenPCDet/pull/396).
@@ -108,7 +116,7 @@ Contributions are also welcomed.
 
 ### KITTI 3D Object Detection Baselines
 Selected supported methods are shown in the below table. The results are the 3D detection performance of moderate difficulty on the *val* set of KITTI dataset.
-* All models are trained with 8 GTX 1080Ti GPUs and are available for download. 
+* All LiDAR-based models are trained with 8 GTX 1080Ti GPUs and are available for download. 
 * The training time is measured with 8 TITAN XP GPUs and PyTorch 1.5.
 
 |                                             | training time | Car@R11 | Pedestrian@R11 | Cyclist@R11  | download | 
@@ -129,7 +137,7 @@ Selected supported methods are shown in the below table. The results are the 3D
 We provide the setting of [`DATA_CONFIG.SAMPLED_INTERVAL`](tools/cfgs/dataset_configs/waymo_dataset.yaml) on the Waymo Open Dataset (WOD) to subsample partial samples for training and evaluation, 
 so you could also play with WOD by setting a smaller `DATA_CONFIG.SAMPLED_INTERVAL` even if you only have limited GPU resources. 
 
-By default, all models are trained with **20% data (~32k frames)** of all the training samples on 8 GTX 1080Ti GPUs, and the results of each cell here are mAP/mAPH calculated by the official Waymo evaluation metrics on the **whole** validation set (version 1.2).    
+By default, all models are trained with **a single frame** of **20% data (~32k frames)** of all the training samples on 8 GTX 1080Ti GPUs, and the results of each cell here are mAP/mAPH calculated by the official Waymo evaluation metrics on the **whole** validation set (version 1.2).    
 
 |    Performance@(train with 20\% Data)            | Vec_L1 | Vec_L2 | Ped_L1 | Ped_L2 | Cyc_L1 | Cyc_L2 |  
 |---------------------------------------------|----------:|:-------:|:-------:|:-------:|:-------:|:-------:|
@@ -141,6 +149,22 @@ By default, all models are trained with **20% data (~32k frames)** of all the tr
 | [Part-A2-Anchor](tools/cfgs/waymo_models/PartA2.yaml) | 74.66/74.12	|65.82/65.32	|71.71/62.24	|62.46/54.06	|66.53/65.18	|64.05/62.75 |
 | [PV-RCNN (AnchorHead)](tools/cfgs/waymo_models/pv_rcnn.yaml) | 75.41/74.74	|67.44/66.80	|71.98/61.24	|63.70/53.95	|65.88/64.25	|63.39/61.82 | 
 | [PV-RCNN (CenterHead)](tools/cfgs/waymo_models/pv_rcnn_with_centerhead_rpn.yaml) | 75.95/75.43	|68.02/67.54	|75.94/69.40	|67.66/61.62	|70.18/68.98	|67.73/66.57|
+| [PV-RCNN++](tools/cfgs/waymo_models/pv_rcnn_plusplus.yaml) | 77.82/77.32|	69.07/68.62|	77.99/71.36|	69.92/63.74|	71.80/70.71|	69.31/68.26|
+| [PV-RCNN++ (ResNet)](tools/cfgs/waymo_models/pv_rcnn_plusplus_resnet.yaml) |77.61/77.14|	69.18/68.75|	79.42/73.31|	70.88/65.21|	72.50/71.39|	69.84/68.77|
+
+
+Here we also provide the performance of several models trained on the full training set (refer to the paper of [PV-RCNN++](https://arxiv.org/abs/2102.00463)):
+
+|    Performance@(train with 100\% Data)            | Vec_L1 | Vec_L2 | Ped_L1 | Ped_L2 | Cyc_L1 | Cyc_L2 |  
+|---------------------------------------------|----------:|:-------:|:-------:|:-------:|:-------:|:-------:|
+| [SECOND](tools/cfgs/waymo_models/second.yaml) | 72.27/71.69 | 63.85/63.33 | 68.70/58.18 | 60.72/51.31 | 60.62/59.28 | 58.34/57.05| 
+| [Part-A2-Anchor](tools/cfgs/waymo_models/PartA2.yaml) | 77.05/76.51 | 68.47/67.97 | 75.24/66.87 | 66.18/58.62 | 68.60/67.36 | 66.13/64.93 |
+| [PV-RCNN (CenterHead)](tools/cfgs/waymo_models/pv_rcnn_with_centerhead_rpn.yaml) | 78.00/77.50 | 69.43/68.98 | 79.21/73.03 | 70.42/64.72 | 71.46/70.27 | 68.95/67.79|
+| [PV-RCNN++](tools/cfgs/waymo_models/pv_rcnn_plusplus.yaml) | 79.10/78.63 | 70.34/69.91 | 80.62/74.62 | 71.86/66.30 | 73.49/72.38 | 70.70/69.62 |
+| [PV-RCNN++ (ResNet)](tools/cfgs/waymo_models/pv_rcnn_plusplus_resnet.yaml) |79.25/78.78 | 70.61/70.18 | 81.83/76.28 | 73.17/68.00 | 73.72/72.66 | 71.21/70.19|
+
+
+
 
 We could not provide the above pretrained models due to [Waymo Dataset License Agreement](https://waymo.com/open/terms/), 
 but you could easily achieve similar performance by training with the default configs.

docs/GETTING_STARTED.md

@@ -86,7 +86,7 @@ OpenPCDet
 ```shell script
 pip3 install --upgrade pip
 # tf 2.0.0
-pip3 install waymo-open-dataset-tf-2-0-0==1.2.0 --user
+pip3 install waymo-open-dataset-tf-2-5-0 --user
 ```
 
 * Extract point cloud data from tfrecord and generate data infos by running the following command (it takes several hours, 

pcdet/datasets/processor/point_feature_encoder.py

@@ -30,7 +30,7 @@ def forward(self, data_dict):
             data_dict['points']
         )
         data_dict['use_lead_xyz'] = use_lead_xyz
-        
+
         if self.point_encoding_config.get('filter_sweeps', False) and 'timestamp' in self.src_feature_list:
             max_sweeps = self.point_encoding_config.max_sweeps
             idx = self.src_feature_list.index('timestamp')