votenet

We have hosted the application votenet in order to run this application in our online workstations with Wine or directly.

Quick description about votenet:

VoteNet is a 3D object detection framework for point clouds that combines deep point set networks with a Hough voting mechanism to localize and classify objects in 3D space. It tackles the challenge that object centroids in 3D scenes often don�t lie on any input surface point by having each point �vote� for potential object centers; these votes are then clustered to propose object hypotheses. Once cluster centers are formed, the network regresses bounding boxes around them and classifies them. VoteNet works end-to-end: it learns the voting, aggregation, and bounding-box regression components jointly, enabling strong detection accuracy without relying on 2D proxies or voxelization. The codebase includes data preparation for indoor datasets (SUN RGB-D, ScanNet), training and evaluation scripts, and demo utilities to visualize predicted boxes over point clouds.

Features:

• Deep point set backbone (e.g. PointNet++) to extract features from raw point clouds
• Hough voting module: points propose object centers to overcome centroid regression challenges
• Clustering of votes to form object proposals and bounding box regression
• Joint end-to-end training of voting, regression, and classification heads
• Preprocessing, training, and evaluation scripts for SUN RGB-D and ScanNet datasets
• Visualization tools for rendering point clouds with predicted boxes

Programming Language: Python.
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