Traditional 3D reconstruction methods, like photogrammetry, often struggle to render reflective and translucent surfaces such as glass, water, and liquids. These limitations are particularly evident in objects like jars, which uniquely combine reflective and translucent qualities while containing diverse contents, from pickles to wet specimens. Photogrammetry’s reliance on point clouds with polygon and texture meshes falls short in capturing these materials, leaving reflective and view-dependent surfaces poorly represented. Advancements like radiance fields and 3D Gaussian Splatting have revolutionized this space. Radiance fields, such as Neural Radiance Fields (NeRFs), use neural networks to generate realistic 3D representations of objects by synthesizing views from any arbitrary angle. NeRFs model view-dependent lighting effects, enabling them to capture intricate details like reflections that shift with the viewing angle. Their approach involves querying 5D coordinates—spatial location and viewing direction—to compute volume density and radiance, allowing for photorealistic novel views of complex scenes through differentiable volume rendering. Complementing NeRFs, 3D Gaussian Splatting uses gaussian "blobs" in a point cloud, enabling smooth transitions and accurate depictions of challenging materials. Together, these innovations provide an unprecedented ability to create detailed 3D models of objects like jars, faithfully capturing their reflective, translucent, and complex properties.
