Comments (2)
As i understood your question, you want to give an input and see the inference results on the trained network. What i did is as follows:
After training the model and saving the training checkpoint, go to the demo.py file and edit the section where you must specify the path to the checkpoint and the point cloud on which you'd like to run inference. Particularely this part:
if FLAGS.dataset == 'sunrgbd':
sys.path.append(os.path.join(ROOT_DIR, 'sunrgbd'))
from sunrgbd_detection_dataset import DC # dataset config
checkpoint_path = os.path.join(demo_dir, 'pretrained_votenet_on_sunrgbd.tar')
pc_path = os.path.join(demo_dir, 'input_pc_sunrgbd.ply')
Replace the dataset, its path, the saved checkpoint path as well as the pc_path (your input point cloud) with where you saved those (when specifying the training flags for instance). Afterwards , run the demo.py file with the appropriate flags, a couple of files will be thrown out in the "dump_dir" specified in the demo.py file. You can view these files with MeshLab. Typically, .ply files will be thrown out, these include the original point cloud, the thrown bounding boxes, votes etc., you can open these together in MeshLab with Crtl + i.
from votenet.
As i understood your question, you want to give an input and see the inference results on the trained network. What i did is as follows: After training the model and saving the training checkpoint, go to the demo.py file and edit the section where you must specify the path to the checkpoint and the point cloud on which you'd like to run inference. Particularely this part:
if FLAGS.dataset == 'sunrgbd': sys.path.append(os.path.join(ROOT_DIR, 'sunrgbd')) from sunrgbd_detection_dataset import DC # dataset config checkpoint_path = os.path.join(demo_dir, 'pretrained_votenet_on_sunrgbd.tar') pc_path = os.path.join(demo_dir, 'input_pc_sunrgbd.ply')
Replace the dataset, its path, the saved checkpoint path as well as the pc_path (your input point cloud) with where you saved those (when specifying the training flags for instance). Afterwards , run the demo.py file with the appropriate flags, a couple of files will be thrown out in the "dump_dir" specified in the demo.py file. You can view these files with MeshLab. Typically, .ply files will be thrown out, these include the original point cloud, the thrown bounding boxes, votes etc., you can open these together in MeshLab with Crtl + i.
I have find the workaround before, and compare with yours, the two both use the function finally
parse_predictions() #which is in models/ap_helper.py
I think the author had put the transformer function in this file
from votenet.
Related Issues (20)
- Calculating Heading angle and votes HOT 1
- 3D bounding box IoU error - buggy Convex Hull Intersection
- Why not use pointNet ++ (PointnetSAModuleMSG) but only use pointNet(PointnetSAModule)?
- 自定义数据集 HOT 3
- KeyError: 'loss' HOT 1
- how to run .m files in SUN RGB-D step2 on ubuntu?use matlab? HOT 2
- RuntimeError: Error compiling objects for extension when trying to compile Pointnet2 CUDA layers HOT 2
- How I can use meshlab? HOT 1
- Can RealSense be used to do real-time Votenet? HOT 1
- About the difference between conf_thresh and DUMP_F_THRESH
- Coloring different object categories HOT 1
- five empty folder
- Convert model to CoreML
- rewrite cuda opertaor
- Large loss and nan loss
- Model convert issues HOT 1
- sunrgbd dataset perparation HOT 1
- Votenet on Nvidia Jetson Nano 4GB HOT 1
- how to train votenet on my own pcd data
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from votenet.