From 33207d5183ea3bf26211b55e9018cc4d929e57d5 Mon Sep 17 00:00:00 2001 From: Yue Cao Date: Fri, 2 Apr 2021 08:30:07 +0800 Subject: [PATCH] Update README.md --- README.md | 16 +++++++--------- 1 file changed, 7 insertions(+), 9 deletions(-) diff --git a/README.md b/README.md index 415f47f..7f775d9 100644 --- a/README.md +++ b/README.md @@ -1,7 +1,8 @@ # Group-Free 3D Object Detection via Transformers -By [Ze Liu](https://github.com/zeliu98), [Zheng Zhang](https://github.com/stupidZZ) -, [Yue Cao](https://github.com/caoyue10), [Han Hu](https://github.com/ancientmooner), [Xin Tong](http://www.xtong.info/) +By [Ze Liu](https://github.com/zeliu98), [Zheng Zhang](https://github.com/stupidZZ), [Yue Cao](https://github.com/caoyue10), [Han Hu](https://github.com/ancientmooner), [Xin Tong](http://www.xtong.info/). + +This repo is the official implementation of ["Group-Free 3D Object Detection via Transformers"](https://arxiv.org/abs/2104.). ![teaser](doc/teaser.png) @@ -11,9 +12,6 @@ By [Ze Liu](https://github.com/zeliu98), [Zheng Zhang](https://github.com/stupid ## Introduction -This repo is the official implementation -of ["Group-Free 3D Object Detection via Transformers"](https://arxiv.org/abs/2104.). - Recently, directly detecting 3D objects from 3D point clouds has received increasing attention. To extract object representation from an irregular point cloud, existing methods usually take a point grouping step to assign the points to an object candidate so that a PointNet-like network could be used to derive object features from the grouped points. @@ -42,7 +40,7 @@ scripts on ScanNet and SUN RGB-D. ## Main Results -### Scannet V2 +### ScanNet V2 |Method | backbone | mAP@0.25 | mAP@0.5 | Model | |:---:|:---:|:---:|:---:|:---:| @@ -58,7 +56,7 @@ scripts on ScanNet and SUN RGB-D. | Ours(L12, O256) | PointNet++w2× |68.8 (68.3*) | 52.1 (51.1*) |[model](https://drive.google.com/file/d/1V6sFLFcqsp7YJ3-9AV2NqUhEGVkuNGWT/view?usp=sharing)| | Ours(L12, O512) | PointNet++w2× | 69.1 (68.8*) |52.8 (52.3*) |[model](https://drive.google.com/file/d/16NAEZqxPdBkxW7GGKGHe4-nDtfqL1htE/view?usp=sharing)| -### SUNRGBD +### SUN RGB-D |Method | backbone | inputs | mAP@0.25 | mAP@0.5 | Model | |:---:|:---:|:---:|:---:|:---:|:---:| @@ -96,7 +94,7 @@ For ScanNet, follow the [README](./scannet/README.md) under the `scannet` folder ## Usage -### Scannet +### ScanNet For `L6, O256` training: @@ -170,7 +168,7 @@ python eval_avg.py --num_point 50000 --width 2 --num_decoder_layers 12 --num_tar --dataset scannet --data_root [--dump_dir ] ``` -#### SUNRGBD +#### SUN RGB-D For `L6, O256` training: