Advanced Driver Assistance Systems (ADAS) are ubiquitous in present day vehicles. Among various ADAS systems, parking assistance systems offering novel views of the vehicle's surroundings, such as, 360o and bird's-eye views are becoming a part of all modern vehicles. Presenting novel views involves multiple steps of image processing including, image/video capture, image registration and visualization. Capturing surrounding view requires the placement of multiple cameras on the vehicle as a first step. Precise placement and calibration of the cameras is important as minor errors may lead to significant artefacts during the subsequent steps of image registration and visualization. Camera placement optimization (CPO) aims to optimize the poses of multiple cameras with an objective to increase the overall coverage of the target area, and/or to reduce the cost of the multiple-camera system. CPO can also eliminate the requirement for camera calibration, as the precise pose of the cameras is already estimated during the optimization step. Although CPO problems are well-studied for surveillance scenarios, there exists a dearth of literature in the context of applications to vehicle surrounding view capture. Compared to surveillance scenarios, CPO problems for vehicle surround view capture need to address additional challenges posed by the complex, non-convex structure of vehicles, and the requirement of a high degree of accuracy in the estimated camera's pose. CPO problems are simulated in discrete space by sampling the continuous space. Although modelling in discrete space is the favoured approach for their simplicity, few works use continuous space models or a mix of both for added accuracy. The scope of this work includes CPO problem formulation for surround-view coverage for vehicles, CPO problem definitions in discrete as well as continuous space domains, and proposing a new heuristic algorithm to improve the performance of existing optimization algorithms. Firstly, new contributions are made towards formulating the CPO problem for surround-view coverage using a 3D discrete space model. A novel multi-resolution heuristic optimization algorithm is proposed to significantly improve the performance of existing discrete optimization algorithms. The CPO problem is then reformulated in the continuous space domain and compared against the discrete-space variant to highlight improved accuracy. Lastly, a super-voxel segmentation method, which was tailored for use in the \multi-resolution optimization method, is introduced and validated on well-known 3D point cloud datasets. Experiments and simulation results on high-resolution 3D models of a variety of vehicles show that the proposed methods are effective in optimizing camera poses of multiple cameras for vehicle surround-view, meeting the demands of real-world scenarios in a reasonable amount of time.