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原文摘要

Recent 3D foundation models extend to novel view synthesis by carrying a Gaussian head on the representation they already use for reconstruction. However, their rendering quality lags far behind their reconstruction accuracy, because the renderer reads a geometry and a representation that are both optimized for metric reconstruction. Prior methods alleviate this by updating the backbone weights, but they thereby sacrifice the metric predictions the model was built for. To this end, we propose RDGSplat, which decodes a geometry dedicated to rendering from a frozen 3D foundation model while keeping its metric predictions exact. In particular, we devise Render-Dedicated Geometry Decoding, which duplicates the pretrained decoders and optimizes the duplicates under photometric supervision alone. Then, a Target-Pose Conditioned Adapter is introduced to adapt the representation those decoders read towards rendering, conditioned on the predicted target pose. Extensive experiments show that RDGSplat improves novel view synthesis for three feed-forward models of two architectures on four benchmarks, with every pretrained weight frozen. On RE10K, it raises WM2.0 from 20.918 to 25.149dB by training 205.5M added parameters against a frozen 1.4B backbone, and leaves its predicted depth and pose unchanged.