The recent advancements in computer vision have seen a significant shift towards more efficient and scalable methods for 3D scene understanding and object pose estimation. Innovations in monocular depth estimation and semantic scene completion are paving the way for more robust and computationally efficient solutions, particularly in autonomous driving and augmented reality applications. The integration of multi-modal data, such as radar and camera inputs, is being explored to enhance the accuracy and reliability of occupancy predictions. Additionally, the use of advanced neural network architectures, such as conditional variational autoencoders and triplane-based deformable attention mechanisms, is demonstrating superior performance over traditional methods. These developments are not only improving the state-of-the-art in specific metrics but also reducing the computational footprint, making these technologies more viable for real-world deployment. Notably, the incorporation of temporal information and the optimization of existing models through knowledge distillation are emerging as key strategies for enhancing model performance and stability.