The recent advancements in computer vision and graphics have seen significant progress in handling complex object properties such as transparency, reflectivity, and glossiness. Researchers are increasingly focusing on developing frameworks that can accurately reconstruct and interpret these properties, which are crucial for tasks such as depth inpainting, inverse rendering, and object pose estimation. Notably, the integration of neural representations, such as Neural Radiance Fields (NeRF), is proving to be a powerful tool for enhancing the realism and accuracy of rendered scenes, particularly for transparent objects. Additionally, the use of 3D Gaussian Splatting in inverse rendering is being refined to better handle the complexities of glossy surfaces, with new methods incorporating material priors to improve geometry and material reconstruction. These developments collectively push the boundaries of what is possible in 3D imaging and scene understanding, enabling more robust and versatile applications in both research and industry.

Noteworthy Papers:

• A diffusion-based framework for depth inpainting of transparent and reflective objects demonstrates robust adaptability and effectiveness.
• A novel mesh-based representation for inverse rendering achieves state-of-the-art visual quality and accurate material properties.
• An innovative 3D-GS-based inverse rendering framework for glossy objects shows high-fidelity geometry and material reconstruction.