In this work, we aim to estimate 3D bounding boxes by voting to object centers and then groups and aggregates the votes to generate 3D box proposals and semantic classes of objects. However, due to the sparse and unstructured nature of the point clouds, we face some challenges when directly predicting bounding box from the vote feature: the sparse vote feature may lack some necessary semantic and context information. To address the challenges, we propose a vote feature enhancement network that aims to encode semantic-aware information and aggravate global context for the vote feature. Specifically, we learn the point-wise semantic information and supplement it to the vote feature, and we also encode the pairwise relations to collect the global context. Experiments on two large datasets of real 3D scans, ScanNet and SUN RGB-D, demonstrate that our method can achieve excellent 3D detection results.
Paper
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Enhanced Vote Network for 3D Object Detection in Point Clouds
Semantic Scholar · Computer Science · 2021
Abstract
In this work, we aim to estimate 3D bounding boxes by voting to object centers and then groups and aggregates the votes to generate 3D box proposals and semantic classes of objects. However, due to the sparse and unstructured nature of the point clouds, we face some challenges when directly predicting bounding box from the vote feature: the sparse vote feature may lack some necessary semantic and context information. To address the challenges, we propose a vote feature enhancement network that aims to encode semantic-aware information and aggravate global context for the vote feature. Specifically, we learn the point-wise semantic information and supplement it to the vote feature, and we also encode the pairwise relations to collect the global context. Experiments on two large datasets of real 3D scans, ScanNet and SUN RGB-D, demonstrate that our method can achieve excellent 3D detection results.