From 8a629f599fac22ce837ca2ed2e5ff2bf8e8df862 Mon Sep 17 00:00:00 2001
From: sunjiahao1999 <578431509@qq.com>
Date: Mon, 27 Feb 2023 18:52:14 +0800
Subject: [PATCH 1/6] fix polarmix UT

---
 tests/test_datasets/test_transforms/test_transforms_3d.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/test_datasets/test_transforms/test_transforms_3d.py b/tests/test_datasets/test_transforms/test_transforms_3d.py
index b66c3bb3c1..036d8bbfca 100644
--- a/tests/test_datasets/test_transforms/test_transforms_3d.py
+++ b/tests/test_datasets/test_transforms/test_transforms_3d.py
@@ -216,7 +216,7 @@ def test_transform(self):
             transform = PolarMix(instance_classes=[1.0, 2.0])
 
         transform = PolarMix(
-            instance_classes=[1, 2],
+            instance_classes=[15, 16, 17],
             swap_ratio=1.0,
             pre_transform=self.pre_transform)
         results = transform.transform(copy.deepcopy(self.results))

From 1fd1e5c02a881605f4e53bd120cffbfd892a2000 Mon Sep 17 00:00:00 2001
From: sunjiahao1999 <578431509@qq.com>
Date: Tue, 21 Mar 2023 11:23:58 +0800
Subject: [PATCH 2/6] init spvcnn readme

---
 README.md                   |  1 +
 README_zh-CN.md             |  1 +
 configs/spvcnn/README.md    | 44 ++++++++++++++++++++++++++++
 configs/spvcnn/metafile.yml | 57 +++++++++++++++++++++++++++++++++++++
 model-index.yml             |  1 +
 5 files changed, 104 insertions(+)
 create mode 100644 configs/spvcnn/README.md
 create mode 100644 configs/spvcnn/metafile.yml

diff --git a/README.md b/README.md
index f4dd01fd5c..97607656ce 100644
--- a/README.md
+++ b/README.md
@@ -251,6 +251,7 @@ Results and models are available in the [model zoo](docs/en/model_zoo.md).
 |    SA-SSD     |   ✗    |     ✗      |   ✓    |   ✗   |    ✗    |  ✗  |     ✗      |
 |    FCAF3D     |   ✗    |     ✗      |   ✗    |   ✗   |    ✗    |  ✗  |     ✓      |
 |    PV-RCNN    |   ✗    |     ✗      |   ✓    |   ✗   |    ✗    |  ✗  |     ✗      |
+|    SPVCNN     |   ✗    |     ✗      |   ✗    |   ✗   |    ✗    |  ✗  |     ✗      |
 
 **Note:** All the about **300+ models, methods of 40+ papers** in 2D detection supported by [MMDetection](https://github.com/open-mmlab/mmdetection/blob/3.x/docs/en/model_zoo.md) can be trained or used in this codebase.
 
diff --git a/README_zh-CN.md b/README_zh-CN.md
index 9ec26b9ef3..31c13c0b81 100644
--- a/README_zh-CN.md
+++ b/README_zh-CN.md
@@ -232,6 +232,7 @@ MMDetection3D 是一个基于 PyTorch 的目标检测开源工具箱，下一代
 |    SA-SSD     |   ✗    |     ✗      |   ✓    |   ✗   |    ✗    |  ✗  |     ✗      |
 |    FCAF3D     |   ✗    |     ✗      |   ✗    |   ✗   |    ✗    |  ✗  |     ✓      |
 |    PV-RCNN    |   ✗    |     ✗      |   ✓    |   ✗   |    ✗    |  ✗  |     ✗      |
+|    SPVCNN     |   ✗    |     ✗      |   ✗    |   ✗   |    ✗    |  ✗  |     ✗      |
 
 **注意：**[MMDetection](https://github.com/open-mmlab/mmdetection/blob/3.x/docs/zh_cn/model_zoo.md) 支持的基于 2D 检测的 **300+ 个模型，40+ 的论文算法**在 MMDetection3D 中都可以被训练或使用。
 
diff --git a/configs/spvcnn/README.md b/configs/spvcnn/README.md
new file mode 100644
index 0000000000..290cd2aac0
--- /dev/null
+++ b/configs/spvcnn/README.md
@@ -0,0 +1,44 @@
+# Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution
+
+> [Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution ](https://arxiv.org/abs/2007.16100)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+Self-driving cars need to understand 3D scenes efficiently and accurately in order to drive safely. Given the limited hardware resources, existing 3D perception models are not able to recognize small instances (e.g., pedestrians, cyclists) very well due to the low-resolution voxelization and aggressive downsampling. To this end, we propose Sparse Point-Voxel Convolution (SPVConv), a lightweight 3D module that equips the vanilla Sparse Convolution with the high-resolution point-based branch. With negligible overhead, this point-based branch is able to preserve the fine details even from large outdoor scenes. To explore the spectrum of efficient 3D models, we first define a flexible architecture design space based on SPVConv, and we then present 3D Neural Architecture Search (3D-NAS) to search the optimal network architecture over this diverse design space efficiently and effectively. Experimental results validate that the resulting SPVNAS model is fast and accurate: it outperforms the state-of-the-art MinkowskiNet by 3.3%, ranking 1st on the competitive SemanticKITTI leaderboard. It also achieves 8x computation reduction and 3x measured speedup over MinkowskiNet with higher accuracy. Finally, we transfer our method to 3D object detection, and it achieves consistent improvements over the one-stage detection baseline on KITTI.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/72679458/226509154-80c27d8e-c138-426a-b92e-72846997b5b3.png" width="800"/>
+</div>
+
+## Introduction
+
+We implement SPVCNN with TorchSparse backend and provide the result and checkpoints on SemanticKITTI datasets.
+
+## Results and models
+
+### SemanticKITTI
+
+|   Method   | Lr schd | Mem (GB) | mIoU |         Download         |
+| :--------: | :-----: | :------: | :--: | :----------------------: |
+| SPVCNN-W16 |   15e   |   3.9    | 61.9 | [model](<>) \| [log](<>) |
+| SPVCNN-W20 |   15e   |   4.2    | 62.7 | [model](<>) \| [log](<>) |
+| SPVCNN-W32 |   15e   |   5.4    | 63.4 | [model](<>) \| [log](<>) |
+
+**Note:** We follow the implementation in SPVNAS original [repo](https://github.com/mit-han-lab/spvnas) and W16\\W20\\W32 indicates different number of channels.
+
+**Note:** Due to TorchSparse backend, the model performance is relatively dependent on random seeds, and if random seeds are not specified the model performance will be different (± 1.5 mIoU).
+
+## Citation
+
+```latex
+@inproceedings{tang2020searching,
+  title={Searching efficient 3d architectures with sparse point-voxel convolution},
+  author={Tang, Haotian and Liu, Zhijian and Zhao, Shengyu and Lin, Yujun and Lin, Ji and Wang, Hanrui and Han, Song},
+  booktitle={Computer Vision--ECCV 2020: 16th European Conference, Glasgow, UK, August 23--28, 2020, Proceedings, Part XXVIII},
+  pages={685--702},
+  year={2020},
+  organization={Springer}
+}
+```
diff --git a/configs/spvcnn/metafile.yml b/configs/spvcnn/metafile.yml
new file mode 100644
index 0000000000..cc4b1444ef
--- /dev/null
+++ b/configs/spvcnn/metafile.yml
@@ -0,0 +1,57 @@
+Collections:
+  - Name: SPVCNN
+    Metadata:
+      Training Techniques:
+        - AdamW
+      Architecture:
+        - SPVCNN
+    Paper:
+      URL: https://arxiv.org/abs/2007.16100
+      Title: 'Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution'
+    README: configs/spvcnn/README.md
+    Code:
+      URL:
+      Version: v1.1.0rc3
+
+Models:
+  - Name: spvcnn_w16_8xb2-15e_semantickitti
+    In Collection: SPVCNN
+    Config: configs/spvcnn/spvcnn_w16_8xb2-15e_semantickitti.py
+    Metadata:
+      Training Data: SemanticKITTI
+      Training Memory (GB): 3.9
+      Training Resources: 8x A100 GPUs
+    Results:
+      - Task: 3D Semantic Segmentation
+        Dataset: SemanticKITTI
+        Metrics:
+          mIOU: 61.7
+    Weights:
+
+  - Name: spvcnn_w20_8xb2-15e_semantickitti
+    In Collection: SPVCNN
+    Config: configs/spvcnn/spvcnn_w20_8xb2-15e_semantickitti.py
+    Metadata:
+      Training Data: SemanticKITTI
+      Training Memory (GB): 4.2
+      Training Resources: 8x A100 GPUs
+    Results:
+      - Task: 3D Semantic Segmentation
+        Dataset: SemanticKITTI
+        Metrics:
+          mIOU: 62.9
+    Weights:
+
+  - Name: spvcnn_w32_8xb2-15e_semantickitti
+    In Collection: SPVCNN
+    Config: configs/spvcnn/spvcnn_w32_8xb2-15e_semantickitti.py
+    Metadata:
+      Training Data: SemanticKITTI
+      Training Memory (GB): 5.4
+      Training Resources: 8x A100 GPUs
+    Results:
+      - Task: 3D Semantic Segmentation
+        Dataset: SemanticKITTI
+        Metrics:
+          mIOU: 63.4
+    Weights:
diff --git a/model-index.yml b/model-index.yml
index abc5ac939c..15653d2049 100644
--- a/model-index.yml
+++ b/model-index.yml
@@ -23,5 +23,6 @@ Import:
   - configs/smoke/metafile.yml
   - configs/ssn/metafile.yml
   - configs/votenet/metafile.yml
+  - configs/spvcnn/metafile.yml
   - configs/pv_rcnn/metafile.yml
   - configs/fcaf3d/metafile.yml

From 7154bf2707ea480da7e2e86c08cd6d4595ad60a5 Mon Sep 17 00:00:00 2001
From: sunjiahao1999 <578431509@qq.com>
Date: Tue, 21 Mar 2023 15:26:05 +0800
Subject: [PATCH 3/6] fix imou

---
 configs/spvcnn/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/configs/spvcnn/README.md b/configs/spvcnn/README.md
index 290cd2aac0..b285e8dd75 100644
--- a/configs/spvcnn/README.md
+++ b/configs/spvcnn/README.md
@@ -24,7 +24,7 @@ We implement SPVCNN with TorchSparse backend and provide the result and checkpoi
 | :--------: | :-----: | :------: | :--: | :----------------------: |
 | SPVCNN-W16 |   15e   |   3.9    | 61.9 | [model](<>) \| [log](<>) |
 | SPVCNN-W20 |   15e   |   4.2    | 62.7 | [model](<>) \| [log](<>) |
-| SPVCNN-W32 |   15e   |   5.4    | 63.4 | [model](<>) \| [log](<>) |
+| SPVCNN-W32 |   15e   |   5.4    | 64.3 | [model](<>) \| [log](<>) |
 
 **Note:** We follow the implementation in SPVNAS original [repo](https://github.com/mit-han-lab/spvnas) and W16\\W20\\W32 indicates different number of channels.
 

From b2eb3a5979a02531e38de5537be514797fbcfd71 Mon Sep 17 00:00:00 2001
From: sunjiahao1999 <578431509@qq.com>
Date: Wed, 22 Mar 2023 15:13:41 +0800
Subject: [PATCH 4/6] add link

---
 README.md                   | 59 +++++++++++++++++++++----------------
 README_zh-CN.md             | 59 +++++++++++++++++++++----------------
 configs/spvcnn/README.md    | 10 +++----
 configs/spvcnn/metafile.yml | 12 ++++----
 model-index.yml             |  2 +-
 5 files changed, 78 insertions(+), 64 deletions(-)

diff --git a/README.md b/README.md
index 97607656ce..937f761bd1 100644
--- a/README.md
+++ b/README.md
@@ -128,6 +128,7 @@ Results and models are available in the [model zoo](docs/en/model_zoo.md).
         <li><a href="configs/dgcnn">DGCNN (TOG'2019)</a></li>
         <li>DLA (CVPR'2018)</li>
         <li>MinkResNet (CVPR'2019)</li>
+        <li><a href="configs/minkunet">MinkUNet (CVPR'2019)</a></li>
       </ul>
       </td>
       <td>
@@ -212,6 +213,11 @@ Results and models are available in the [model zoo](docs/en/model_zoo.md).
         </ul>
       </td>
       <td>
+        <li><b>Outdoor</b></li>
+        <ul>
+          <li><a href="configs/minkunet">MinkUNet (CVPR'2019)</a></li>
+          <li><a href="configs/spvcnn">SPVCNN (ECCV'2020)</a></li>
+        </ul>
         <li><b>Indoor</b></li>
         <ul>
           <li><a href="configs/pointnet2">PointNet++ (NeurIPS'2017)</a></li>
@@ -226,32 +232,33 @@ Results and models are available in the [model zoo](docs/en/model_zoo.md).
   </tbody>
 </table>
 
-|               | ResNet | PointNet++ | SECOND | DGCNN | RegNetX | DLA | MinkResNet |
-| :-----------: | :----: | :--------: | :----: | :---: | :-----: | :-: | :--------: |
-|    SECOND     |   ✗    |     ✗      |   ✓    |   ✗   |    ✗    |  ✗  |     ✗      |
-| PointPillars  |   ✗    |     ✗      |   ✓    |   ✗   |    ✓    |  ✗  |     ✗      |
-|  FreeAnchor   |   ✗    |     ✗      |   ✗    |   ✗   |    ✓    |  ✗  |     ✗      |
-|    VoteNet    |   ✗    |     ✓      |   ✗    |   ✗   |    ✗    |  ✗  |     ✗      |
-|    H3DNet     |   ✗    |     ✓      |   ✗    |   ✗   |    ✗    |  ✗  |     ✗      |
-|     3DSSD     |   ✗    |     ✓      |   ✗    |   ✗   |    ✗    |  ✗  |     ✗      |
-|    Part-A2    |   ✗    |     ✗      |   ✓    |   ✗   |    ✗    |  ✗  |     ✗      |
-|    MVXNet     |   ✓    |     ✗      |   ✓    |   ✗   |    ✗    |  ✗  |     ✗      |
-|  CenterPoint  |   ✗    |     ✗      |   ✓    |   ✗   |    ✗    |  ✗  |     ✗      |
-|      SSN      |   ✗    |     ✗      |   ✗    |   ✗   |    ✓    |  ✗  |     ✗      |
-|   ImVoteNet   |   ✓    |     ✓      |   ✗    |   ✗   |    ✗    |  ✗  |     ✗      |
-|    FCOS3D     |   ✓    |     ✗      |   ✗    |   ✗   |    ✗    |  ✗  |     ✗      |
-|  PointNet++   |   ✗    |     ✓      |   ✗    |   ✗   |    ✗    |  ✗  |     ✗      |
-| Group-Free-3D |   ✗    |     ✓      |   ✗    |   ✗   |    ✗    |  ✗  |     ✗      |
-|  ImVoxelNet   |   ✓    |     ✗      |   ✗    |   ✗   |    ✗    |  ✗  |     ✗      |
-|    PAConv     |   ✗    |     ✓      |   ✗    |   ✗   |    ✗    |  ✗  |     ✗      |
-|     DGCNN     |   ✗    |     ✗      |   ✗    |   ✓   |    ✗    |  ✗  |     ✗      |
-|     SMOKE     |   ✗    |     ✗      |   ✗    |   ✗   |    ✗    |  ✓  |     ✗      |
-|      PGD      |   ✓    |     ✗      |   ✗    |   ✗   |    ✗    |  ✗  |     ✗      |
-|   MonoFlex    |   ✗    |     ✗      |   ✗    |   ✗   |    ✗    |  ✓  |     ✗      |
-|    SA-SSD     |   ✗    |     ✗      |   ✓    |   ✗   |    ✗    |  ✗  |     ✗      |
-|    FCAF3D     |   ✗    |     ✗      |   ✗    |   ✗   |    ✗    |  ✗  |     ✓      |
-|    PV-RCNN    |   ✗    |     ✗      |   ✓    |   ✗   |    ✗    |  ✗  |     ✗      |
-|    SPVCNN     |   ✗    |     ✗      |   ✗    |   ✗   |    ✗    |  ✗  |     ✗      |
+|               | ResNet | PointNet++ | SECOND | DGCNN | RegNetX | DLA | MinkResNet | MinkUNet |
+| :-----------: | :----: | :--------: | :----: | :---: | :-----: | :-: | :--------: | :------: |
+|    SECOND     |   ✗    |     ✗      |   ✓    |   ✗   |    ✗    |  ✗  |     ✗      |    ✗     |
+| PointPillars  |   ✗    |     ✗      |   ✓    |   ✗   |    ✓    |  ✗  |     ✗      |    ✗     |
+|  FreeAnchor   |   ✗    |     ✗      |   ✗    |   ✗   |    ✓    |  ✗  |     ✗      |    ✗     |
+|    VoteNet    |   ✗    |     ✓      |   ✗    |   ✗   |    ✗    |  ✗  |     ✗      |    ✗     |
+|    H3DNet     |   ✗    |     ✓      |   ✗    |   ✗   |    ✗    |  ✗  |     ✗      |    ✗     |
+|     3DSSD     |   ✗    |     ✓      |   ✗    |   ✗   |    ✗    |  ✗  |     ✗      |    ✗     |
+|    Part-A2    |   ✗    |     ✗      |   ✓    |   ✗   |    ✗    |  ✗  |     ✗      |    ✗     |
+|    MVXNet     |   ✓    |     ✗      |   ✓    |   ✗   |    ✗    |  ✗  |     ✗      |    ✗     |
+|  CenterPoint  |   ✗    |     ✗      |   ✓    |   ✗   |    ✗    |  ✗  |     ✗      |    ✗     |
+|      SSN      |   ✗    |     ✗      |   ✗    |   ✗   |    ✓    |  ✗  |     ✗      |    ✗     |
+|   ImVoteNet   |   ✓    |     ✓      |   ✗    |   ✗   |    ✗    |  ✗  |     ✗      |    ✗     |
+|    FCOS3D     |   ✓    |     ✗      |   ✗    |   ✗   |    ✗    |  ✗  |     ✗      |    ✗     |
+|  PointNet++   |   ✗    |     ✓      |   ✗    |   ✗   |    ✗    |  ✗  |     ✗      |    ✗     |
+| Group-Free-3D |   ✗    |     ✓      |   ✗    |   ✗   |    ✗    |  ✗  |     ✗      |    ✗     |
+|  ImVoxelNet   |   ✓    |     ✗      |   ✗    |   ✗   |    ✗    |  ✗  |     ✗      |    ✗     |
+|    PAConv     |   ✗    |     ✓      |   ✗    |   ✗   |    ✗    |  ✗  |     ✗      |    ✗     |
+|     DGCNN     |   ✗    |     ✗      |   ✗    |   ✓   |    ✗    |  ✗  |     ✗      |    ✗     |
+|     SMOKE     |   ✗    |     ✗      |   ✗    |   ✗   |    ✗    |  ✓  |     ✗      |    ✗     |
+|      PGD      |   ✓    |     ✗      |   ✗    |   ✗   |    ✗    |  ✗  |     ✗      |    ✗     |
+|   MonoFlex    |   ✗    |     ✗      |   ✗    |   ✗   |    ✗    |  ✓  |     ✗      |    ✗     |
+|    SA-SSD     |   ✗    |     ✗      |   ✓    |   ✗   |    ✗    |  ✗  |     ✗      |    ✗     |
+|    FCAF3D     |   ✗    |     ✗      |   ✗    |   ✗   |    ✗    |  ✗  |     ✓      |    ✗     |
+|    PV-RCNN    |   ✗    |     ✗      |   ✓    |   ✗   |    ✗    |  ✗  |     ✗      |    ✗     |
+|   MinkUNet    |   ✗    |     ✗      |   ✗    |   ✗   |    ✗    |  ✗  |     ✗      |    ✓     |
+|    SPVCNN     |   ✗    |     ✗      |   ✗    |   ✗   |    ✗    |  ✗  |     ✗      |    ✓     |
 
 **Note:** All the about **300+ models, methods of 40+ papers** in 2D detection supported by [MMDetection](https://github.com/open-mmlab/mmdetection/blob/3.x/docs/en/model_zoo.md) can be trained or used in this codebase.
 
diff --git a/README_zh-CN.md b/README_zh-CN.md
index 31c13c0b81..2b1a71d904 100644
--- a/README_zh-CN.md
+++ b/README_zh-CN.md
@@ -110,6 +110,7 @@ MMDetection3D 是一个基于 PyTorch 的目标检测开源工具箱，下一代
         <li><a href="configs/dgcnn">DGCNN (TOG'2019)</a></li>
         <li>DLA (CVPR'2018)</li>
         <li>MinkResNet (CVPR'2019)</li>
+        <li><a href="configs/minkunet">MinkUNet (CVPR'2019)</a></li>
       </ul>
       </td>
       <td>
@@ -193,6 +194,11 @@ MMDetection3D 是一个基于 PyTorch 的目标检测开源工具箱，下一代
         </ul>
       </td>
       <td>
+        <li><b>室外</b></li>
+        <ul>
+          <li><a href="configs/minkunet">MinkUNet (CVPR'2019)</a></li>
+          <li><a href="configs/spvcnn">SPVCNN (ECCV'2020)</a></li>
+        </ul>
         <li><b>室内</b></li>
         <ul>
           <li><a href="configs/pointnet2">PointNet++ (NeurIPS'2017)</a></li>
@@ -207,32 +213,33 @@ MMDetection3D 是一个基于 PyTorch 的目标检测开源工具箱，下一代
   </tbody>
 </table>
 
-|               | ResNet | PointNet++ | SECOND | DGCNN | RegNetX | DLA | MinkResNet |
-| :-----------: | :----: | :--------: | :----: | :---: | :-----: | :-: | :--------: |
-|    SECOND     |   ✗    |     ✗      |   ✓    |   ✗   |    ✗    |  ✗  |     ✗      |
-| PointPillars  |   ✗    |     ✗      |   ✓    |   ✗   |    ✓    |  ✗  |     ✗      |
-|  FreeAnchor   |   ✗    |     ✗      |   ✗    |   ✗   |    ✓    |  ✗  |     ✗      |
-|    VoteNet    |   ✗    |     ✓      |   ✗    |   ✗   |    ✗    |  ✗  |     ✗      |
-|    H3DNet     |   ✗    |     ✓      |   ✗    |   ✗   |    ✗    |  ✗  |     ✗      |
-|     3DSSD     |   ✗    |     ✓      |   ✗    |   ✗   |    ✗    |  ✗  |     ✗      |
-|    Part-A2    |   ✗    |     ✗      |   ✓    |   ✗   |    ✗    |  ✗  |     ✗      |
-|    MVXNet     |   ✓    |     ✗      |   ✓    |   ✗   |    ✗    |  ✗  |     ✗      |
-|  CenterPoint  |   ✗    |     ✗      |   ✓    |   ✗   |    ✗    |  ✗  |     ✗      |
-|      SSN      |   ✗    |     ✗      |   ✗    |   ✗   |    ✓    |  ✗  |     ✗      |
-|   ImVoteNet   |   ✓    |     ✓      |   ✗    |   ✗   |    ✗    |  ✗  |     ✗      |
-|    FCOS3D     |   ✓    |     ✗      |   ✗    |   ✗   |    ✗    |  ✗  |     ✗      |
-|  PointNet++   |   ✗    |     ✓      |   ✗    |   ✗   |    ✗    |  ✗  |     ✗      |
-| Group-Free-3D |   ✗    |     ✓      |   ✗    |   ✗   |    ✗    |  ✗  |     ✗      |
-|  ImVoxelNet   |   ✓    |     ✗      |   ✗    |   ✗   |    ✗    |  ✗  |     ✗      |
-|    PAConv     |   ✗    |     ✓      |   ✗    |   ✗   |    ✗    |  ✗  |     ✗      |
-|     DGCNN     |   ✗    |     ✗      |   ✗    |   ✓   |    ✗    |  ✗  |     ✗      |
-|     SMOKE     |   ✗    |     ✗      |   ✗    |   ✗   |    ✗    |  ✓  |     ✗      |
-|      PGD      |   ✓    |     ✗      |   ✗    |   ✗   |    ✗    |  ✗  |     ✗      |
-|   MonoFlex    |   ✗    |     ✗      |   ✗    |   ✗   |    ✗    |  ✓  |     ✗      |
-|    SA-SSD     |   ✗    |     ✗      |   ✓    |   ✗   |    ✗    |  ✗  |     ✗      |
-|    FCAF3D     |   ✗    |     ✗      |   ✗    |   ✗   |    ✗    |  ✗  |     ✓      |
-|    PV-RCNN    |   ✗    |     ✗      |   ✓    |   ✗   |    ✗    |  ✗  |     ✗      |
-|    SPVCNN     |   ✗    |     ✗      |   ✗    |   ✗   |    ✗    |  ✗  |     ✗      |
+|               | ResNet | PointNet++ | SECOND | DGCNN | RegNetX | DLA | MinkResNet | MinkUNet |
+| :-----------: | :----: | :--------: | :----: | :---: | :-----: | :-: | :--------: | :------: |
+|    SECOND     |   ✗    |     ✗      |   ✓    |   ✗   |    ✗    |  ✗  |     ✗      |    ✗     |
+| PointPillars  |   ✗    |     ✗      |   ✓    |   ✗   |    ✓    |  ✗  |     ✗      |    ✗     |
+|  FreeAnchor   |   ✗    |     ✗      |   ✗    |   ✗   |    ✓    |  ✗  |     ✗      |    ✗     |
+|    VoteNet    |   ✗    |     ✓      |   ✗    |   ✗   |    ✗    |  ✗  |     ✗      |    ✗     |
+|    H3DNet     |   ✗    |     ✓      |   ✗    |   ✗   |    ✗    |  ✗  |     ✗      |    ✗     |
+|     3DSSD     |   ✗    |     ✓      |   ✗    |   ✗   |    ✗    |  ✗  |     ✗      |    ✗     |
+|    Part-A2    |   ✗    |     ✗      |   ✓    |   ✗   |    ✗    |  ✗  |     ✗      |    ✗     |
+|    MVXNet     |   ✓    |     ✗      |   ✓    |   ✗   |    ✗    |  ✗  |     ✗      |    ✗     |
+|  CenterPoint  |   ✗    |     ✗      |   ✓    |   ✗   |    ✗    |  ✗  |     ✗      |    ✗     |
+|      SSN      |   ✗    |     ✗      |   ✗    |   ✗   |    ✓    |  ✗  |     ✗      |    ✗     |
+|   ImVoteNet   |   ✓    |     ✓      |   ✗    |   ✗   |    ✗    |  ✗  |     ✗      |    ✗     |
+|    FCOS3D     |   ✓    |     ✗      |   ✗    |   ✗   |    ✗    |  ✗  |     ✗      |    ✗     |
+|  PointNet++   |   ✗    |     ✓      |   ✗    |   ✗   |    ✗    |  ✗  |     ✗      |    ✗     |
+| Group-Free-3D |   ✗    |     ✓      |   ✗    |   ✗   |    ✗    |  ✗  |     ✗      |    ✗     |
+|  ImVoxelNet   |   ✓    |     ✗      |   ✗    |   ✗   |    ✗    |  ✗  |     ✗      |    ✗     |
+|    PAConv     |   ✗    |     ✓      |   ✗    |   ✗   |    ✗    |  ✗  |     ✗      |    ✗     |
+|     DGCNN     |   ✗    |     ✗      |   ✗    |   ✓   |    ✗    |  ✗  |     ✗      |    ✗     |
+|     SMOKE     |   ✗    |     ✗      |   ✗    |   ✗   |    ✗    |  ✓  |     ✗      |    ✗     |
+|      PGD      |   ✓    |     ✗      |   ✗    |   ✗   |    ✗    |  ✗  |     ✗      |    ✗     |
+|   MonoFlex    |   ✗    |     ✗      |   ✗    |   ✗   |    ✗    |  ✓  |     ✗      |    ✗     |
+|    SA-SSD     |   ✗    |     ✗      |   ✓    |   ✗   |    ✗    |  ✗  |     ✗      |    ✗     |
+|    FCAF3D     |   ✗    |     ✗      |   ✗    |   ✗   |    ✗    |  ✗  |     ✓      |    ✗     |
+|    PV-RCNN    |   ✗    |     ✗      |   ✓    |   ✗   |    ✗    |  ✗  |     ✗      |    ✗     |
+|   MinkUNet    |   ✗    |     ✗      |   ✗    |   ✗   |    ✗    |  ✗  |     ✗      |    ✓     |
+|    SPVCNN     |   ✗    |     ✗      |   ✗    |   ✗   |    ✗    |  ✗  |     ✗      |    ✓     |
 
 **注意：**[MMDetection](https://github.com/open-mmlab/mmdetection/blob/3.x/docs/zh_cn/model_zoo.md) 支持的基于 2D 检测的 **300+ 个模型，40+ 的论文算法**在 MMDetection3D 中都可以被训练或使用。
 
diff --git a/configs/spvcnn/README.md b/configs/spvcnn/README.md
index b285e8dd75..f2242805f7 100644
--- a/configs/spvcnn/README.md
+++ b/configs/spvcnn/README.md
@@ -20,11 +20,11 @@ We implement SPVCNN with TorchSparse backend and provide the result and checkpoi
 
 ### SemanticKITTI
 
-|   Method   | Lr schd | Mem (GB) | mIoU |         Download         |
-| :--------: | :-----: | :------: | :--: | :----------------------: |
-| SPVCNN-W16 |   15e   |   3.9    | 61.9 | [model](<>) \| [log](<>) |
-| SPVCNN-W20 |   15e   |   4.2    | 62.7 | [model](<>) \| [log](<>) |
-| SPVCNN-W32 |   15e   |   5.4    | 64.3 | [model](<>) \| [log](<>) |
+|   Method   | Lr schd | Mem (GB) | mIoU |                                                                                                                                  Download                                                                                                                                   |
+| :--------: | :-----: | :------: | :--: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| SPVCNN-W16 |   15e   |   3.9    | 61.9 | [model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/spvcnn/spvcnn_w16_8xb2-15e_semantickitti_20230321_011645-a2734d85.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/spvcnn/spvcnn_w16_8xb2-15e_semantickitti_20230321_011645.log) |
+| SPVCNN-W20 |   15e   |   4.2    | 62.7 | [model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/spvcnn/spvcnn_w20_8xb2-15e_semantickitti_20230321_011649-519e7eff.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/spvcnn/spvcnn_w20_8xb2-15e_semantickitti_20230321_011649.log) |
+| SPVCNN-W32 |   15e   |   5.4    | 64.3 | [model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/spvcnn/spvcnn_w32_8xb2-15e_semantickitti_20230308_113324-f7c0c5b4.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/spvcnn/spvcnn_w32_8xb2-15e_semantickitti_20230308_113324.log) |
 
 **Note:** We follow the implementation in SPVNAS original [repo](https://github.com/mit-han-lab/spvnas) and W16\\W20\\W32 indicates different number of channels.
 
diff --git a/configs/spvcnn/metafile.yml b/configs/spvcnn/metafile.yml
index cc4b1444ef..51e5c5d06c 100644
--- a/configs/spvcnn/metafile.yml
+++ b/configs/spvcnn/metafile.yml
@@ -10,8 +10,8 @@ Collections:
       Title: 'Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution'
     README: configs/spvcnn/README.md
     Code:
-      URL:
-      Version: v1.1.0rc3
+      URL: https://github.com/open-mmlab/mmdetection3d/blob/1.1/mmdet3d/models/backbones/spvcnn_backone.py#L22
+      Version: v1.1.0rc4
 
 Models:
   - Name: spvcnn_w16_8xb2-15e_semantickitti
@@ -26,7 +26,7 @@ Models:
         Dataset: SemanticKITTI
         Metrics:
           mIOU: 61.7
-    Weights:
+    Weights: https://download.openmmlab.com/mmdetection3d/v1.0.0_models/spvcnn/spvcnn_w16_8xb2-15e_semantickitti_20230321_011645-a2734d85.pth
 
   - Name: spvcnn_w20_8xb2-15e_semantickitti
     In Collection: SPVCNN
@@ -40,7 +40,7 @@ Models:
         Dataset: SemanticKITTI
         Metrics:
           mIOU: 62.9
-    Weights:
+    Weights: https://download.openmmlab.com/mmdetection3d/v1.0.0_models/spvcnn/spvcnn_w20_8xb2-15e_semantickitti_20230321_011649-519e7eff.pth
 
   - Name: spvcnn_w32_8xb2-15e_semantickitti
     In Collection: SPVCNN
@@ -53,5 +53,5 @@ Models:
       - Task: 3D Semantic Segmentation
         Dataset: SemanticKITTI
         Metrics:
-          mIOU: 63.4
-    Weights:
+          mIOU: 64.3
+    Weights: https://download.openmmlab.com/mmdetection3d/v1.0.0_models/spvcnn/spvcnn_w32_8xb2-15e_semantickitti_20230308_113324-f7c0c5b4.pth
diff --git a/model-index.yml b/model-index.yml
index 15653d2049..28f121dd9d 100644
--- a/model-index.yml
+++ b/model-index.yml
@@ -23,6 +23,6 @@ Import:
   - configs/smoke/metafile.yml
   - configs/ssn/metafile.yml
   - configs/votenet/metafile.yml
-  - configs/spvcnn/metafile.yml
   - configs/pv_rcnn/metafile.yml
   - configs/fcaf3d/metafile.yml
+  - configs/spvcnn/metafile.yml

From ff85c4d2b9d39a8941515b4b9df6e4e3628dbc49 Mon Sep 17 00:00:00 2001
From: sunjiahao1999 <578431509@qq.com>
Date: Thu, 23 Mar 2023 15:08:57 +0800
Subject: [PATCH 5/6] add torchsparse link

---
 configs/spvcnn/README.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/configs/spvcnn/README.md b/configs/spvcnn/README.md
index f2242805f7..8918cffb86 100644
--- a/configs/spvcnn/README.md
+++ b/configs/spvcnn/README.md
@@ -14,7 +14,7 @@ Self-driving cars need to understand 3D scenes efficiently and accurately in ord
 
 ## Introduction
 
-We implement SPVCNN with TorchSparse backend and provide the result and checkpoints on SemanticKITTI datasets.
+We implement SPVCNN with [TorchSparse](https://github.com/mit-han-lab/torchsparse) backend and provide the result and checkpoints on SemanticKITTI datasets.
 
 ## Results and models
 
@@ -28,7 +28,7 @@ We implement SPVCNN with TorchSparse backend and provide the result and checkpoi
 
 **Note:** We follow the implementation in SPVNAS original [repo](https://github.com/mit-han-lab/spvnas) and W16\\W20\\W32 indicates different number of channels.
 
-**Note:** Due to TorchSparse backend, the model performance is relatively dependent on random seeds, and if random seeds are not specified the model performance will be different (± 1.5 mIoU).
+**Note:** Due to TorchSparse backend, the model performance is unstable with TorchSparse backend and may fluctuate by about 1.5 mIoU for different random seeds.
 
 ## Citation
 

From 2294135769bdb173d9b62cf4a21218aa68f8e5d5 Mon Sep 17 00:00:00 2001
From: sunjiahao1999 <578431509@qq.com>
Date: Wed, 29 Mar 2023 15:21:24 +0800
Subject: [PATCH 6/6] revise link

---
 configs/spvcnn/README.md    | 10 +++++-----
 configs/spvcnn/metafile.yml |  6 +++---
 2 files changed, 8 insertions(+), 8 deletions(-)

diff --git a/configs/spvcnn/README.md b/configs/spvcnn/README.md
index 8918cffb86..c2e903b21f 100644
--- a/configs/spvcnn/README.md
+++ b/configs/spvcnn/README.md
@@ -20,11 +20,11 @@ We implement SPVCNN with [TorchSparse](https://github.com/mit-han-lab/torchspars
 
 ### SemanticKITTI
 
-|   Method   | Lr schd | Mem (GB) | mIoU |                                                                                                                                  Download                                                                                                                                   |
-| :--------: | :-----: | :------: | :--: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
-| SPVCNN-W16 |   15e   |   3.9    | 61.9 | [model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/spvcnn/spvcnn_w16_8xb2-15e_semantickitti_20230321_011645-a2734d85.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/spvcnn/spvcnn_w16_8xb2-15e_semantickitti_20230321_011645.log) |
-| SPVCNN-W20 |   15e   |   4.2    | 62.7 | [model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/spvcnn/spvcnn_w20_8xb2-15e_semantickitti_20230321_011649-519e7eff.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/spvcnn/spvcnn_w20_8xb2-15e_semantickitti_20230321_011649.log) |
-| SPVCNN-W32 |   15e   |   5.4    | 64.3 | [model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/spvcnn/spvcnn_w32_8xb2-15e_semantickitti_20230308_113324-f7c0c5b4.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/spvcnn/spvcnn_w32_8xb2-15e_semantickitti_20230308_113324.log) |
+|   Method   | Lr schd | Mem (GB) | mIoU |                                                                                                                                                                    Download                                                                                                                                                                     |
+| :--------: | :-----: | :------: | :--: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| SPVCNN-W16 |   15e   |   3.9    | 61.9 | [model](https://download.openmmlab.com/mmdetection3d/v1.1.0_models/spvcnn/spvcnn_w16_8xb2-15e_semantickitti/spvcnn_w16_8xb2-15e_semantickitti_20230321_011645-a2734d85.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.1.0_models/spvcnn/spvcnn_w16_8xb2-15e_semantickitti/spvcnn_w16_8xb2-15e_semantickitti_20230321_011645.log) |
+| SPVCNN-W20 |   15e   |   4.2    | 62.7 | [model](https://download.openmmlab.com/mmdetection3d/v1.1.0_models/spvcnn/spvcnn_w20_8xb2-15e_semantickitti/spvcnn_w20_8xb2-15e_semantickitti_20230321_011649-519e7eff.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.1.0_models/spvcnn/spvcnn_w20_8xb2-15e_semantickitti/spvcnn_w20_8xb2-15e_semantickitti_20230321_011649.log) |
+| SPVCNN-W32 |   15e   |   5.4    | 64.3 | [model](https://download.openmmlab.com/mmdetection3d/v1.1.0_models/spvcnn/spvcnn_w32_8xb2-15e_semantickitti/spvcnn_w32_8xb2-15e_semantickitti_20230308_113324-f7c0c5b4.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.1.0_models/spvcnn/pvcnn_w32_8xb2-15e_semantickitti/spvcnn_w32_8xb2-15e_semantickitti_20230308_113324.log)  |
 
 **Note:** We follow the implementation in SPVNAS original [repo](https://github.com/mit-han-lab/spvnas) and W16\\W20\\W32 indicates different number of channels.
 
diff --git a/configs/spvcnn/metafile.yml b/configs/spvcnn/metafile.yml
index 51e5c5d06c..5a541b34c0 100644
--- a/configs/spvcnn/metafile.yml
+++ b/configs/spvcnn/metafile.yml
@@ -26,7 +26,7 @@ Models:
         Dataset: SemanticKITTI
         Metrics:
           mIOU: 61.7
-    Weights: https://download.openmmlab.com/mmdetection3d/v1.0.0_models/spvcnn/spvcnn_w16_8xb2-15e_semantickitti_20230321_011645-a2734d85.pth
+    Weights: https://download.openmmlab.com/mmdetection3d/v1.1.0_models/spvcnn/spvcnn_w16_8xb2-15e_semantickitti/spvcnn_w16_8xb2-15e_semantickitti_20230321_011645-a2734d85.pth
 
   - Name: spvcnn_w20_8xb2-15e_semantickitti
     In Collection: SPVCNN
@@ -40,7 +40,7 @@ Models:
         Dataset: SemanticKITTI
         Metrics:
           mIOU: 62.9
-    Weights: https://download.openmmlab.com/mmdetection3d/v1.0.0_models/spvcnn/spvcnn_w20_8xb2-15e_semantickitti_20230321_011649-519e7eff.pth
+    Weights: https://download.openmmlab.com/mmdetection3d/v1.1.0_models/spvcnn/spvcnn_w20_8xb2-15e_semantickitti/spvcnn_w20_8xb2-15e_semantickitti_20230321_011649-519e7eff.pth
 
   - Name: spvcnn_w32_8xb2-15e_semantickitti
     In Collection: SPVCNN
@@ -54,4 +54,4 @@ Models:
         Dataset: SemanticKITTI
         Metrics:
           mIOU: 64.3
-    Weights: https://download.openmmlab.com/mmdetection3d/v1.0.0_models/spvcnn/spvcnn_w32_8xb2-15e_semantickitti_20230308_113324-f7c0c5b4.pth
+    Weights: https://download.openmmlab.com/mmdetection3d/v1.1.0_models/spvcnn/spvcnn_w32_8xb2-15e_semantickitti/spvcnn_w32_8xb2-15e_semantickitti_20230308_113324-f7c0c5b4.pth