Novel view synthesis (NVS) of urban scenes enables the exploration of cities virtually and interactively, which can further be used for urban planning, navigation, digital tourism, etc. However, many current NVS methods require a large amount of images from known views as input and are sensitive to intrinsic and extrinsic camera parameters. In this paper, we propose a new unified framework for NVS of urban scenes with fewer required views via the integration of scene priors and the joint optimization of camera parameters under an geometric constraint along with NeRF weights. The integration of scene priors makes full use of the priors from the neighbor reference views to reduce the number of required known views. The joint optimization can correct the errors in camera parameters, which are usually derived from algorithms like Structure-from-Motion (SfM), and then further improves the quality of the generated novel views. Experiments show that our method achieves about 25.375 dB and 25.512 dB in average in terms of peak signal-to-noise (PSNR) on synthetic and real data, respectively. It outperforms popular state-of-the-art methods (i.e., BungeeNeRF and MegaNeRF) by about 2–4 dB in PSNR. Notably, our method achieves better or competitive results than the baseline method with only one third of the known view images required for the baseline. The code and dataset are available at https://github.com/Dongber/PriNeRF.

The usage of parallel elastic actuators (PEA) in legged robots could potentially enhance the joints and increase energy efficiency by providing extra torques. However, the current design that adopts tension springs or spiral springs usually requires additional working space for PEA add-ons and enlarges size and mass too much. Besides, they often tune the spring parameters especially the spring constant by hand, failing to achieve optimal performance when considering multiple objectives. To tackle these issues, this article designs a compact dual-slide PEA (DS-PEA) leg that adopts a compression spring structure. Through integrating with a dual-slide mechanism, the PEA elements are attached tightly to the transmission, resulting in a small-size and light-weighted design. Furthermore, we adopt a multiobjective optimization method, i.e., multi-Pareto fronts quantify, to automatically choose the proper spring constant. Simulation and hardware experiments demonstrate that peak torque, motor power, and cost of transport for motion tracking are all largely reduced, even when working at multiple trajectories. Extensive hopping experiments further validate the dynamic motion capability and the energy efficiency of the delicate design. The compact DS-PEA leg will be used in a quadruped robot shortly.