add vis example to fcos cfg page & vis command instruction (#582)

configs/fcos3d/README.md

@@ -25,11 +25,13 @@ Currently we first support the benchmark on the large-scale nuScenes dataset, wh
 }
 ```
 
+![demo image](../../resources/browse_dataset_mono.png)
+
 ## Usage
 
 ### Data Preparation
 
-After supporting FCOS3D and monocular 3D object detection in v0.13.0, the coco-style 2D json info files will include related annotations by default 
+After supporting FCOS3D and monocular 3D object detection in v0.13.0, the coco-style 2D json info files will include related annotations by default
 (see [here](https://github.com/open-mmlab/mmdetection3d/blob/master/tools/data_converter/nuscenes_converter.py#L333) if you would like to change the parameter).
 So you can just follow the data preparation steps given in the documentation, then all the needed infos are ready together.
 
@@ -46,6 +48,10 @@ You can turn on it by setting `flip=True` in the `test_pipeline`.
 
 Due to the scale and measurements of depth is different from those of other regression targets, we first train the model with depth weight equal to 0.2 for a more stable training procedure. For a stronger detector with better performance, please finetune the model with depth weight changed to 1.0 as shown in the [config](./fcos3d_r101_caffe_fpn_gn-head_dcn_2x8_1x_nus-mono3d_finetune.py). Note that the path of `load_from` needs to be changed to yours accordingly.
 
+### Visualizing prediction results
+
+We also provide visualization functions to show the monocular 3D detection results. Simply follow the [documentation](https://mmdetection3d.readthedocs.io/en/latest/1_exist_data_model.html#test-existing-models-on-standard-datasets) and use the `single-gpu testing` command. You only need to add the `--show` flag and specify `--show-dir` to store the visualization results.
+
 ## Results
 
 ### NuScenes