How did you arrive at this research area?

The rendered results are with lowreconstruction fidelity for NeRF [1]and NeRF-W [7]only with6and12training views. For NeRF [1]with18training views, the result shows higher fidelity, but the undesired reflection is also finally rendered (labeled by green box).

The omnipresence of NP-hard combinatorial optimization problems (COPs) compels domain experts to engage in trial-and-error heuristic design. The long-standing endeavor of design automation has gained new momentum with the rise of large language models (LLMs).

There is no simple heuristic to reliably connect an object and the visualeffectsitgenerates.

Existing deep learning based solutions typically restore the target layers individually, or with some concerns at the end of the output, barely taking into account the interaction across thetwostreams/branches. Inorder toutilize information more efficiently, this work presents a general yet simple interactive strategy, namely your trash is my treasure(YTMT), for constructing dual-stream decomposition networks.