Related papers: FCAF3D: Fully Convolutional Anchor-Free 3D Object Detection

FCAF3D: Fully Convolutional Anchor-Free 3D Object Detection

URL: http://arxiv.org/abs/2112.00322v1
Date: Wed, 1 Dec 2021 07:28:52 GMT
Title: FCAF3D: Fully Convolutional Anchor-Free 3D Object Detection
Authors: Danila Rukhovich, Anna Vorontsova, Anton Konushin
Abstract summary: We present FCAF3D - a first-in-class fully convolutional anchor-free indoor 3D object detection method. It is a simple yet effective method that uses a voxel representation of a point cloud and processes voxels with sparse convolutions. It can handle large-scale scenes with minimal runtime through a single fully convolutional feed-forward pass.
Score: 3.330229314824913
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Recently, promising applications in robotics and augmented reality have attracted considerable attention to 3D object detection from point clouds. In this paper, we present FCAF3D - a first-in-class fully convolutional anchor-free indoor 3D object detection method. It is a simple yet effective method that uses a voxel representation of a point cloud and processes voxels with sparse convolutions. FCAF3D can handle large-scale scenes with minimal runtime through a single fully convolutional feed-forward pass. Existing 3D object detection methods make prior assumptions on the geometry of objects, and we argue that it limits their generalization ability. To get rid of any prior assumptions, we propose a novel parametrization of oriented bounding boxes that allows obtaining better results in a purely data-driven way. The proposed method achieves state-of-the-art 3D object detection results in terms of mAP@0.5 on ScanNet V2 (+4.5), SUN RGB-D (+3.5), and S3DIS (+20.5) datasets. The code and models are available at https://github.com/samsunglabs/fcaf3d.

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