Paper Info
Title
Temp-Frustum Net: 3D Object Detection with Temporal Fusion
Abstract
3D object detection is a core component of automated driving systems. State-of-the-art methods fuse RGB imagery and LiDAR point cloud data frame-by-frame for 3D bounding box regression. However, frame-by-frame 3D object detection suffers from noise, lield-of-view obstruction, and sparsity. We propose a novel Temporal Fusion Module (TFM) to use information from previous time-steps to mitigate these problems. First, a state-of-the-art frustum network extracts point cloud features from raw RGB and LiDAR point cloud data frame-by-frame. Then, our TFM module fuses these features with a recurrent neural network. As a result, 3D object detection becomes robust against single frame failures and transient occlusions. Experiments on the KITTI object tracking dataset show the efficiency of the proposed TFM, where we obtain 6%, 4%, and 6% improvements on Car, Pedestrian, and Cyclist classes, respectively, compared to frame-by-frame baselines. Furthermore, ablation studies reinforce that the subject of improvement is temporal fusion and show the effects of different placements of TFM in the object detection pipeline. Our code is open-source and available at https://github.com/emecercelik/Temp-Frustum-Net.git.
Year
DOI
Venue
2021
10.1109/IV48863.2021.9575392
2021 32ND IEEE INTELLIGENT VEHICLES SYMPOSIUM (IV)
DocType
ISSN
Citations 
Conference
1931-0587
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Emeç Erçelik100.34
Ekim Yurtsever262.38
Alois Knoll Knoll31700271.32