# RDGSplat

RDGSplat introduces a two-pass framework that decodes a render-dedicated geometry from a frozen 3D foundation model by duplicating pretrained decoders and optimizing them under photometric supervision alone, together with a target-pose conditioned adapter that reformulates the representation those decoders read, while the model's metric predictions remain unchanged. Across four benchmarks and three feed-forward backbones, it improves novel view synthesis, raising WM2.0 on RE10K from 20.918 to 24.266 dB with 205.5 M added trainable parameters, and the depth and pose the same model predicts are bit-exact.

RDGSplat introduces a two-pass framework that decodes a render-dedicated geometry from a frozen 3D foundation model by duplicating pretrained decoders and optimizing them under photometric supervision alone, together with a target-pose conditioned adapter that reformulates the representation those decoders read, while the model's metric predictions remain unchanged. Across four benchmarks and three feed-forward backbones, it improves novel view synthesis, raising WM2.0 on RE10K from 20.918 to 24.266 dB with 205.5 M added trainable parameters, and the depth and pose the same model predicts are bit-exact.