Neural Radiance Field 3D Capture with Semantic Masks

The patent applies preliminary action by performing denoising operations during the training phase of the neural network model rather than as a separate post-processing step. The model learns to predict clean mesh representations directly from noisy input data, effectively performing the clean-up action beforehand. This resolves the contradiction by maintaining automation while improving mesh quality through integrated preprocessing within the training objective.

The patent replaces the mechanical manual clean-up process with a neural network-based automated system. Instead of artists manually editing meshes, a trained deep learning model automatically denoises and refines mesh representations during training. This substitution eliminates the time-consuming manual intervention while maintaining or improving mesh quality, directly resolving the time-quality trade-off.

The patent applies segmentation by separating the representation of shape and radiance into distinct neural network components. Instead of a single unified NeRF model that conflates shape and appearance information, the system uses separate networks or distinct representation streams that can be independently optimized. This segmentation resolves the shape-radiance ambiguity by preventing the entanglement of geometric and photometric information, while maintaining the ability to handle unbounded scenes through the underlying NeRF formulation.