The field of computer graphics is witnessing significant advancements in neural rendering and 3D reconstruction. Researchers are exploring innovative techniques to improve the efficiency and quality of these processes. One notable direction is the development of resource-aware frameworks that can render complex scenes in real-time on mobile devices. Another area of focus is the improvement of front-end performance in web applications through modular rendering and adaptive hydration strategies. Additionally, neural parametric mixtures are being used to encode target distributions for path guiding algorithms, allowing for more accurate and efficient rendering. Hypothesis testing-based methods are also being employed to determine optimal kernel radii for unbiased kernel estimation. Furthermore, generative-free methods are being proposed for occlusion removal in 3D scene reconstruction, and meta-continual learning strategies are being developed to improve the learning speed and quality of neural fields. Noteworthy papers in this area include: NeRFlex, which achieves real-time high-quality rendering of complex scenes on mobile devices. DeclutterNeRF, which introduces a generative-free method for occlusion removal in 3D scene reconstruction. Meta-Continual Learning of Neural Fields, which proposes a novel strategy for overcoming the limitations of existing methods for continual learning of neural fields.