[Fix] Update README format

configs/3dssd/README.md

@@ -1,44 +1,28 @@
 # 3DSSD: Point-based 3D Single Stage Object Detector
 
-## Abstract
+> [3DSSD: Point-based 3D Single Stage Object Detector](https://arxiv.org/abs/2002.10187)
 
-<!-- [ABSTRACT] -->
+<!-- [ALGORITHM] -->
 
-Currently, there have been many kinds of voxel-based 3D single stage detectors, while point-based single stage methods are still underexplored. In this paper, we first present a lightweight and effective point-based 3D single stage object detector, named 3DSSD, achieving a good balance between accuracy and efficiency. In this paradigm, all upsampling layers and refinement stage, which are indispensable in all existing point-based methods, are abandoned to reduce the large computation cost. We novelly propose a fusion sampling strategy in downsampling process to make detection on less representative points feasible. A delicate box prediction network including a candidate generation layer, an anchor-free regression head with a 3D center-ness assignment strategy is designed to meet with our demand of accuracy and speed. Our paradigm is an elegant single stage anchor-free framework, showing great superiority to other existing methods. We evaluate 3DSSD on widely used KITTI dataset and more challenging nuScenes dataset. Our method outperforms all state-of-the-art voxel-based single stage methods by a large margin, and has comparable performance to two stage point-based methods as well, with inference speed more than 25 FPS, 2x faster than former state-of-the-art point-based methods.
+## Abstract
 
-<!-- [IMAGE] -->
+Currently, there have been many kinds of voxel-based 3D single stage detectors, while point-based single stage methods are still underexplored. In this paper, we first present a lightweight and effective point-based 3D single stage object detector, named 3DSSD, achieving a good balance between accuracy and efficiency. In this paradigm, all upsampling layers and refinement stage, which are indispensable in all existing point-based methods, are abandoned to reduce the large computation cost. We novelly propose a fusion sampling strategy in downsampling process to make detection on less representative points feasible. A delicate box prediction network including a candidate generation layer, an anchor-free regression head with a 3D center-ness assignment strategy is designed to meet with our demand of accuracy and speed. Our paradigm is an elegant single stage anchor-free framework, showing great superiority to other existing methods. We evaluate 3DSSD on widely used KITTI dataset and more challenging nuScenes dataset. Our method outperforms all state-of-the-art voxel-based single stage methods by a large margin, and has comparable performance to two stage point-based methods as well, with inference speed more than 25 FPS, 2x faster than former state-of-the-art point-based methods.
 
 <div align=center>
 <img src="https://user-images.githubusercontent.com/30491025/143854187-54ed1257-a046-4764-81cd-d2c8404137d3.png" width="800"/>
 </div>
 
-<!-- [PAPER_TITLE: 3DSSD: Point-based 3D Single Stage Object Detector] -->
-<!-- [PAPER_URL: https://arxiv.org/abs/2002.10187] -->
-
 ## Introduction
 
-<!-- [ALGORITHM] -->
-
 We implement 3DSSD and provide the results and checkpoints on KITTI datasets.
 
-```
-@inproceedings{yang20203dssd,
-    author = {Zetong Yang and Yanan Sun and Shu Liu and Jiaya Jia},
-    title = {3DSSD: Point-based 3D Single Stage Object Detector},
-    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
-    year = {2020}
-}
-```
-
-### Experiment details on KITTI datasets
-
 Some settings in our implementation are different from the [official implementation](https://github.com/Jia-Research-Lab/3DSSD), which bring marginal differences to the performance on KITTI datasets in our experiments. To simplify and unify the models of our implementation, we skip them in our models. These differences are listed as below:
 1. We keep the scenes without any object while the official code skips these scenes in training. In the official implementation, only 3229 and 3394 samples are used as training and validation sets, respectively. In our implementation, we keep using 3712 and 3769 samples as training and validation sets, respectively, as those used for all the other models in our implementation on KITTI datasets.
 2. We do not modify the decay of `batch normalization` during training.
 3. While using [`DataBaseSampler`](https://github.com/open-mmlab/mmdetection3d/blob/master/mmdet3d/datasets/pipelines/dbsampler.py#L80) for data augmentation, the official code uses road planes as reference to place the sampled objects while we do not.
 4. We perform detection using LIDAR coordinates while the official code uses camera coordinates.
 
-## Results
+## Results and models
 
 ### KITTI
 
@@ -47,3 +31,14 @@ Some settings in our implementation are different from the [official implementat
 |    [PointNet2SAMSG](./3dssd_4x4_kitti-3d-car.py)| Car |72e|4.7||78.69(81.27)<sup>1</sup>|[model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/3dssd/3dssd_kitti-3d-car_20210602_124438-b4276f56.pth) &#124; [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/3dssd/3dssd_kitti-3d-car_20210602_124438.log.json)|
 
 [1]: We report two different 3D object detection performance here. 78.69mAP is evaluated by our evaluation code and 81.27mAP is evaluated by the official development kit （so as that used in the paper and official code of 3DSSD ）. We found that the commonly used Python implementation of [`rotate_iou`](https://github.com/traveller59/second.pytorch/blob/e42e4a0e17262ab7d180ee96a0a36427f2c20a44/second/core/non_max_suppression/nms_gpu.py#L605) which is used in our KITTI dataset evaluation, is different from the official implementation in [KITTI benchmark](http://www.cvlibs.net/datasets/kitti/eval_object.php?obj_benchmark=3d).
+
+## Citation
+
+```latex
+@inproceedings{yang20203dssd,
+    author = {Zetong Yang and Yanan Sun and Shu Liu and Jiaya Jia},
+    title = {3DSSD: Point-based 3D Single Stage Object Detector},
+    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
+    year = {2020}
+}
+```

configs/centerpoint/README.md

@@ -1,24 +1,19 @@
 # Center-based 3D Object Detection and Tracking
 
-## Abstract
+> [Center-based 3D Object Detection and Tracking](https://arxiv.org/abs/2006.11275)
 
-<!-- [ABSTRACT] -->
+<!-- [ALGORITHM] -->
 
-Three-dimensional objects are commonly represented as 3D boxes in a point-cloud. This representation mimics the well-studied image-based 2D bounding-box detection but comes with additional challenges. Objects in a 3D world do not follow any particular orientation, and box-based detectors have difficulties enumerating all orientations or fitting an axis-aligned bounding box to rotated objects. In this paper, we instead propose to represent, detect, and track 3D objects as points. Our framework, CenterPoint, first detects centers of objects using a keypoint detector and regresses to other attributes, including 3D size, 3D orientation, and velocity. In a second stage, it refines these estimates using additional point features on the object. In CenterPoint, 3D object tracking simplifies to greedy closest-point matching. The resulting detection and tracking algorithm is simple, efficient, and effective. CenterPoint achieved state-of-the-art performance on the nuScenes benchmark for both 3D detection and tracking, with 65.5 NDS and 63.8 AMOTA for a single model. On the Waymo Open Dataset, CenterPoint outperforms all previous single model method by a large margin and ranks first among all Lidar-only submissions.
+## Abstract
 
-<!-- [IMAGE] -->
+Three-dimensional objects are commonly represented as 3D boxes in a point-cloud. This representation mimics the well-studied image-based 2D bounding-box detection but comes with additional challenges. Objects in a 3D world do not follow any particular orientation, and box-based detectors have difficulties enumerating all orientations or fitting an axis-aligned bounding box to rotated objects. In this paper, we instead propose to represent, detect, and track 3D objects as points. Our framework, CenterPoint, first detects centers of objects using a keypoint detector and regresses to other attributes, including 3D size, 3D orientation, and velocity. In a second stage, it refines these estimates using additional point features on the object. In CenterPoint, 3D object tracking simplifies to greedy closest-point matching. The resulting detection and tracking algorithm is simple, efficient, and effective. CenterPoint achieved state-of-the-art performance on the nuScenes benchmark for both 3D detection and tracking, with 65.5 NDS and 63.8 AMOTA for a single model. On the Waymo Open Dataset, CenterPoint outperforms all previous single model method by a large margin and ranks first among all Lidar-only submissions.
 
 <div align=center>
 <img src="https://user-images.githubusercontent.com/30491025/143854976-11af75ae-e828-43ad-835d-ac1146f99925.png" width="800"/>
 </div>
 
-<!-- [PAPER_TITLE: Center-based 3D Object Detection and Tracking] -->
-<!-- [PAPER_URL: https://arxiv.org/abs/2006.11275] -->
-
 ## 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.
@@ -42,15 +37,6 @@ We follow the below style to name config files. Contributors are advised to foll
 
 `{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
@@ -118,7 +104,7 @@ data = dict(
 
 ```
 
-## Results
+## Results and models
 
 ### CenterPoint
 
@@ -139,3 +125,14 @@ data = dict(
 |above w/o circle nms|pillar (0.2)|✗|✗| | |49.12|59.66||
 |[SECFPN](./centerpoint_02pillar_second_secfpn_dcn_4x8_cyclic_20e_nus.py)|pillar (0.2)|✓|✗| 4.6| |48.8 |59.67 |[model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/centerpoint/centerpoint_02pillar_second_secfpn_dcn_4x8_cyclic_20e_nus/centerpoint_02pillar_second_secfpn_dcn_4x8_cyclic_20e_nus_20200930_103722-3bb135f2.pth) &#124; [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/centerpoint/centerpoint_02pillar_second_secfpn_dcn_4x8_cyclic_20e_nus/centerpoint_02pillar_second_secfpn_dcn_4x8_cyclic_20e_nus_20200930_103722.log.json)|
 |above w/ circle nms|pillar (0.2)|✓|✓| | |48.79|59.65||
+
+## Citation
+
+```latex
+@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},
+}
+```

configs/dgcnn/README.md

@@ -1,38 +1,24 @@
 # Dynamic Graph CNN for Learning on Point Clouds
 
-## Abstract
+> [Dynamic Graph CNN for Learning on Point Clouds](https://arxiv.org/abs/1801.07829)
 
-<!-- [ABSTRACT] -->
+<!-- [ALGORITHM] -->
 
-Point clouds provide a flexible geometric representation suitable for countless applications in computer graphics; they also comprise the raw output of most 3D data acquisition devices. While hand-designed features on point clouds have long been proposed in graphics and vision, however, the recent overwhelming success of convolutional neural networks (CNNs) for image analysis suggests the value of adapting insight from CNN to the point cloud world. Point clouds inherently lack topological information so designing a model to recover topology can enrich the representation power of point clouds. To this end, we propose a new neural network module dubbed EdgeConv suitable for CNN-based high-level tasks on point clouds including classification and segmentation. EdgeConv acts on graphs dynamically computed in each layer of the network. It is differentiable and can be plugged into existing architectures. Compared to existing modules operating in extrinsic space or treating each point independently, EdgeConv has several appealing properties: It incorporates local neighborhood information; it can be stacked applied to learn global shape properties; and in multi-layer systems affinity in feature space captures semantic characteristics over potentially long distances in the original embedding. We show the performance of our model on standard benchmarks including ModelNet40, ShapeNetPart, and S3DIS.
+## Abstract
 
-<!-- [IMAGE] -->
+Point clouds provide a flexible geometric representation suitable for countless applications in computer graphics; they also comprise the raw output of most 3D data acquisition devices. While hand-designed features on point clouds have long been proposed in graphics and vision, however, the recent overwhelming success of convolutional neural networks (CNNs) for image analysis suggests the value of adapting insight from CNN to the point cloud world. Point clouds inherently lack topological information so designing a model to recover topology can enrich the representation power of point clouds. To this end, we propose a new neural network module dubbed EdgeConv suitable for CNN-based high-level tasks on point clouds including classification and segmentation. EdgeConv acts on graphs dynamically computed in each layer of the network. It is differentiable and can be plugged into existing architectures. Compared to existing modules operating in extrinsic space or treating each point independently, EdgeConv has several appealing properties: It incorporates local neighborhood information; it can be stacked applied to learn global shape properties; and in multi-layer systems affinity in feature space captures semantic characteristics over potentially long distances in the original embedding. We show the performance of our model on standard benchmarks including ModelNet40, ShapeNetPart, and S3DIS.
 
 <div align=center>
 <img src="https://user-images.githubusercontent.com/30491025/143855852-3d7888ed-2cfc-416c-9ec8-57621edeaa34.png" width="800"/>
 </div>
 
-<!-- [PAPER_TITLE: Dynamic Graph CNN for Learning on Point Clouds] -->
-<!-- [PAPER_URL: https://arxiv.org/abs/1801.07829] -->
-
 ## Introduction
 
-<!-- [ALGORITHM] -->
-
 We implement DGCNN and provide the results and checkpoints on S3DIS dataset.
 
-```
-@article{dgcnn,
-  title={Dynamic Graph CNN for Learning on Point Clouds},
-  author={Wang, Yue and Sun, Yongbin and Liu, Ziwei and Sarma, Sanjay E. and Bronstein, Michael M. and Solomon, Justin M.},
-  journal={ACM Transactions on Graphics (TOG)},
-  year={2019}
-}
-```
-
 **Notice**: We follow the implementations in the original DGCNN paper and a PyTorch implementation of DGCNN [code](https://github.com/AnTao97/dgcnn.pytorch).
 
-## Results
+## Results and models
 
 ### S3DIS
 
@@ -56,3 +42,14 @@ We implement DGCNN and provide the results and checkpoints on S3DIS dataset.
 ## Indeterminism
 
 Since DGCNN testing adopts sliding patch inference which involves random point sampling, and the test script uses fixed random seeds while the random seeds of validation in training are not fixed, the test results may be slightly different from the results reported above.
+
+## Citation
+
+```latex
+@article{dgcnn,
+  title={Dynamic Graph CNN for Learning on Point Clouds},
+  author={Wang, Yue and Sun, Yongbin and Liu, Ziwei and Sarma, Sanjay E. and Bronstein, Michael M. and Solomon, Justin M.},
+  journal={ACM Transactions on Graphics (TOG)},
+  year={2019}
+}
+```