# Radar Voxel Fusion for 3D Object Detection
## 5 Discussion  
The RVF-Net early fusion approach proves its effectiveness by outperforming the lidar baseline by 5.1%. Additional measures have been taken to increase the overall detection score. Data augmentation especially increased the AP score for all networks. The novel loss, introduced in Section 3.3, improves both the AP score and notably the orientation error of the networks. Empirically, the additional classification loss mitigates the discontinuity problem in the yaw regression, even though classifications are discontinuous decisions on their own. Furthermore, the paper shows that the early fusion approach is especially beneficial in inclement weather conditions. The radar features, while not being dense enough for an accurate object detection on their own, contribute positively to the detection result when \begin{table}
\begin{tabular}{l c} \hline \hline
**Network Input** & **Inference Time** \\ \hline Lidar & 0.042 s \\ Radar & 0.02 s \\ Lidar, RGB & 0.045 s \\ Lidar, Radar & 0.044 s \\ RVF-Net & 0.044 s \\ Simulated Depth Cam, Radar & 0.061 s \\ RVF-Net, 10 sweeps & 0.063 s \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Inference times of different sensor input configurations on the NVIDIA Titan Xp GPU. \begin{table}
\begin{tabular}{l c} \hline \hline
**Network** & **nuScenes** \\ \hline Tracked Lidar & 40.01\% \\ Tracked Late Fusion & 33.29\% \\ Tracked Early Fusion (RVF-Net) & 47.09\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: AP scores of the tracked sensor inputs. The early fusion RVF-Net outperforms the late fusion by a great margin. processed with an additional sensor input. It is interesting to note that the addition of RGB data increases the performance of the lidar, radar, and camera fusion approach, while it does not increase the performance of the lidar and RGB fusion. We assume that the early fusion performs most reliably when more different input data and interdependencies are present. In addition to increasing robustness and enabling autonomous driving in inclement weather scenarios, we assume that early fusion schemes can be advantageous for special use cases such as mining applications, where dust oftentimes limits lidar and camera detection ranges. When comparing our network to the official detection scores on the nuScenes data set, we have to take into account that our approach is evaluated on the validation split and not on the official test split. The hyperparameters of the network, however, were not optimized on the validation split, so that it serves as a valid test set. We assume that the complexity of the data in the frontal field of view does not differ significantly from the full 360 degree view. We therefore assume that the detection AP of our approach scales with the scores provided by other authors on the validation split. To benchmark our network on the test split, a 360 degree coverage of the input data would be needed. Though there are no conceptual obstacles in the way, we decided against the additional implementation overhead due to the general shortcomings of the radar data provided in the nuScenes data set [7; 43] and no expected new insights from the additional sensor coverage. The validation split suffices to evaluate the applicability of the proposed early fusion network. On the validation split, our approach outperforms several single sensor or fusion object detection algorithms. For example, the CenterFusion approach [19], which achieves 48.4% AP for the car class on the nuScenes validation split. In the literature, only [PERSON] [6] fuses all three sensor modalities. Our fusion approach surpasses their score of 45% AP on the validation split and 48% AP on the test split. On the other hand, further object detection methods, such as the leading lidar-only method CenterPoint [14], outperform even our best network in the ablation studies by a great margin. The two stage network uses center points to match detection candidates and performs an additional bounding box refinement to achieve an AP score of 87% on the test split.