CoordUp : Upsampling Any Feature Beyond the Megapixel Regime

Vision foundation models produce semantically rich but spatially coarse features, limiting fine-detail recovery in dense prediction tasks. Existing feature upsamplers tackle this problem but are often tied to specific backbones or require materializing dense feature grids, making high-resolution inference costly. We introduce CoordUp, a lightweight, feature-agnostic upsampler that formulates feature upsampling as a coordinate-conditioned operator. Across five frozen backbones and three high-resolution datasets for semantic segmentation and depth estimation, it consistently improves boundary-sensitive metrics over previous encoder-agnostic upsamplers. It also transfers zero-shot to the final activations of DA3 and SAM3, improving boundary quality without retraining. Finally, its coordinate-based formulation allows CoordUp to scale gracefully with output resolution, enabling, for example, an 80 Mpx output using only 8.6 GB of GPU memory.