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FCAF3D: Fully Convolutional Anchor-Free 3D Object Detection

FCAF3D: Fully Convolutional Anchor-Free 3D Object Detection

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 eliminate 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.

Introduction

We implement FCAF3D and provide the result and checkpoints on the ScanNet and SUN RGB-D dataset.

Results and models

ScanNet

Backbone Mem (GB) Inf time (fps) AP@0.25 AP@0.5 Download
MinkResNet34 10.5 15.7 69.7(70.7*) 55.2(56.0*) model | log

SUN RGB-D

Backbone Mem (GB) Inf time (fps) AP@0.25 AP@0.5 Download
MinkResNet34 6.3 17.9 63.8(63.8*) 47.3(48.2*) model | log

S3DIS

Backbone Mem (GB) Inf time (fps) AP@0.25 AP@0.5 Download
MinkResNet34 23.5 10.9 67.4(64.9*) 45.7(43.8*) model | log

Note

  • We report the results across 5 train runs followed by 5 test runs. * means the results reported in the paper.
  • Inference time is given for a single NVidia RTX 4090 GPU. All models are trained on 2 GPUs.

Citation

@inproceedings{rukhovich2022fcaf3d,
  title={FCAF3D: Fully Convolutional Anchor-Free 3D Object Detection},
  author={Danila Rukhovich, Anna Vorontsova, Anton Konushin},
  booktitle={European conference on computer vision},
  year={2022}
}