Static Scene Reconstruction

Static scene reconstruction aims to create accurate 3D models of unchanging environments from multiple images or sensor data, primarily focusing on achieving photorealistic rendering and efficient storage. Current research emphasizes novel algorithms like Gaussian splatting and neural radiance fields (NeRFs), often incorporating techniques to handle noisy data and improve robustness to real-world challenges such as varying lighting conditions. These advancements are crucial for applications in autonomous driving, robotics, and virtual/augmented reality, enabling more realistic simulations and improved scene understanding for these systems.