## 2 Related Works
### Cooperative 3D Object Detection  
With the growing attention surrounding smart roads [17], cooperative object detection has gained significant interest. Novel datasets and benchmarks such as DAIR-V2X [18], TUMTraf-V2X [3] and V2V4 Real [19] have been created to investigate ideas regarding cooperative perception. These datasets contain time-sequences of data gathered by calibrated and synchronized sensors such as LiDAR, camera, RADAR and even event cameras in the case of TUMTraf. The vehicles in the field of view of the recording equipment are carefully and manually labeled with 3D bounding boxes to provide training data and an  
Figure 1: This figure depicts a frame from our experiments on the ZalaZONE proving ground. The semantic keypoint estimates are visible (top) alongside the 6 DOF pose estimate of the _Semantic Mesh Model_’s vertices as reconstructed by our novel approach and localized by the **camera-only** multi-view pose estimation. evaluation platform for deep learning models. While DAIR and TUMTraf focus on vehicle-to-everything cooperative perception, containing annotated LiDAR point clouds from both vehicle and infrastructure mounted sensors, V2V4 real focuses on vehicle-to-vehicle cooperative perception only. On these datasets, researchers tested raw-data sharing techniques, object level sensor and feature level fusion methods. By far the most well-performing methods on these benchmarks are the various feature-level learning models that authors have proposed in the last two years, because they balance the bandwidth required to transmit data and the sharing of crucial information for robust object detection. PillarGRID, for example, ref. [20] fuses 3D LiDAR data from infrastructure and vehicle-mounted sensors by the cooperative preprocessing of point clouds, pillar-wise voxelization and feature extraction, the grid-wise deep fusion of features, and CNN-based augmented 3D object detection. SiCP [6] introduces an effective lightweight feature fusion framework that facilitates efficient LiDAR feature fusion from multiple vehicles while preserving essential gradient information, ensuring robust detection even when cooperative data are unavailable. The most outstanding performance, however, may be reached through multi-modal setups. CoBEVfusion [21] is such a framework. By introducing a dual window-based cross-attention module to effectively fuse multi-modal, multi-station data using a bird's eye view representation (BEV), it significantly advances the state of the art in cooperative perception. In case of highly asynchronous data, CoBEVFlow [22] demonstrates robustness through introducing a BEV flow mechanism to model motion in a scene, thus allowing temporal feature alignment. While these datasets and algorithms collectively advance the field of cooperative 3D object detection, merely developing advanced deep learning models that perform with a high mean-average-precision (mAP) score on established datasets does not make the implementation of such systems easy in the real world. Empirical studies show that deep-learning-based cooperative (and single-view) perception algorithms suffer significantly degraded performance when applied to new domains [23]. Although methods such as the one [PERSON] et al. published in [24] exist to mitigate the issues of heterogeneous LiDAR point cloud data, and there are methods for automatic data annotation for teacher-student model configurations, e.g., [25], to the best of our knowledge, no current method exists that is capable of generalizing _without_ retraining to specific contexts. This is especially true for image-based detectors, because they infer depth from context cues even though they do mitigate the need for expensive LiDAR sensors [26].