  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, field-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.
