facebookresearch/votenet
An end-to-end deep learning model for 3D object detection in point clouds using Hough voting and deep point set networks.
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VoteNet detects 3D objects directly from point cloud data by combining deep point set networks with Hough voting to overcome the challenge that object centroids can be far from surface points in sparse 3D data. The model achieves state-of-the-art results on ScanNet and SUN RGB-D datasets using purely geometric information without relying on color images.
Frequently asked
- What is facebookresearch/votenet?
- An end-to-end deep learning model for 3D object detection in point clouds using Hough voting and deep point set networks.
- Is votenet open source?
- Yes — facebookresearch/votenet is open source, released under the MIT license.
- What language is votenet written in?
- facebookresearch/votenet is primarily written in Python.
- How popular is votenet?
- facebookresearch/votenet has 1.8k stars on GitHub.
- Where can I find votenet?
- facebookresearch/votenet is on GitHub at https://github.com/facebookresearch/votenet.