## 2 Related Works
### Pose Estimation and Shape Reconstruction  
Pose estimation of rigid objects such as vehicles using camera sensors and 3D models can address the aforementioned problems by reducing reliance on LiDAR sensors and extensive 3D annotation processes. Leveraging a known 3D model or point cloud of an object allows accurate estimation of its pose provided corresponding 2D-3D landmarks can be precisely identified and located in images. In multiple view geometry in computer vision [27], this problem is referred to as the Perspective N Point (PNP) problem. The landmarks, historically, are keypoints detected by feature detectors such as SIFT [28] and ORB [29] to which a 3D point corresponds. The associated 3D point cloud is usually reconstructed by leveraging the geometry of a camera motion or calibrated multi-view computer vision. Provided four or more non-coplanar keypoints can be re-identified, the pose of the camera with respect to the keypoints can be unambiguously solved [30]. This principle is frequently used in Structure from Motion [27], V-SLAM (Visual Simultaneous Localization and Mapping) [31] and industrial pose estimation applications [32]. Traditional, feature-based keypoints, however, cannot easily be re-identified later in different lighting conditions or from significantly different viewpoints. Semantic keypoints, detected via neural networks [33; 34], provide a robust alternative. These category-specific keypoints carry human-understandable semantic information, which makes them invariant to changes in viewpoint and lighting. The neural network-based detection of these, although far more robust and meaningful, is significantly noisier, making classical triangulation-based reconstruction and pose estimation impractical. The earliest method of using these keypoints to estimate the pose of rigid bodies has been explored in recent years, and it was initially suggested by [PERSON] et al. [35]. Their approach employs a deformable CAD model for each object category and solves the pose estimation problem while also allowing regularized deformation of the model, thereby reducing failure rates stemming from model and semantic keypoint detection inaccuracies. This enables improved performance compared to exact solvers such as Epnp [30], which fail in cases of significant model error, occlusion or 2D keypoint noise [14]. Although this reduces the failure rate, it also introduces an ambiguity between the object's pose and its model's deformation. [PERSON] et al. [36] implemented a related idea using a lookup table of 3D CAD models. This works very well for their application in simulated datasets provided the CAD model is available but lacks a real-world method to reconstruct the appropriate model for the lookup table.  
The current state of the art in vehicle shape and pose estimation from images uses deep learning. In the seminal work Apollo3D [14], the authors use human-annotated 2D semantic keypoints in images and corresponding highly accurate human-made CAD models for creating a semi-automatic annotation process for vehicle poses in images. Building on this dataset and benchmark, several other models were proposed, such as Gsnet [37], Mono3D++ [38], Autoshape [39] and BAAM [13], which have progressively increased the accuracy and reliability [14]. Nonetheless, despite the impressive performance, these methods suffer from limited generalization outside their training conditions, as highlighted by our previous work [15]. Even though approaches like Autoshape provide automated annotation capabilities by combining LiDAR and camera inputs, they remain dependent on LiDAR, restricting practicality in scenarios where only camera setups are available.