RPC-GS: Gaussian Splatting with native RPC Rendering for Satellite Imagery

2026-06-04

AI Summary

This work addresses the geometric reconstruction errors introduced by existing Gaussian splatting methods that rely on perspective or affine camera approximations of Rational Polynomial Coefficient (RPC) models. We propose, for the first time, a Gaussian splatting framework natively supporting RPC cameras, directly projecting Gaussian means and covariances through the RPC model to eliminate approximation errors. Our key innovations include integrating the RPC model into the splatting pipeline, designing a Jacobian-based robust covariance projection method, and introducing ray-based depth modeling to overcome the lack of explicit depth in RPC formulations. Evaluated on the DFC2019 and IARPA2016 datasets, our approach reduces mean elevation errors by 29.6%/63.8% and 9.9%/37.9%, respectively, compared to conventional methods, significantly advancing 3D reconstruction accuracy.

Abstract

We present RPC-GS, the first Gaussian Splatting framework for satellite imagery that operates natively with Rational Polynomial Camera (RPC) models. The RPC model is the de facto standard for representing the complex imaging geometry of modern pushbroom satellite sensors. To simplify rendering, prior satellite Gaussian Splatting methods replace the RPC model with perspective or affine camera approximations, leading to geometric errors during reconstruction. RPC-GS avoids these approximations by projecting Gaussian means and covariances directly through the RPC model during the splatting process. We embed the RPC model in a chain of carefully selected geo-coordinate transformations representing a mapping from splatting-suitable scene coordinates to image coordinates. To map the Gaussian covariance matrices, we derive a numerically robust Jacobian-based covariance projection for the (partially nonlinear) coordinate transformations. Since RPCs lack an explicit notion of camera depth, we integrate a metric ray-based depth formulation. We benchmark RPC, perspective, and affine camera models in a unified framework, with our native RPC renderer consistently achieving the lowest reconstruction error on leading satellite benchmark datasets, improving mean altitude error over perspective and affine approximations by 29.6% and 63.8% on DFC2019, and by 9.9% and 37.9% on IARPA2016. We release our code to support future research of Gaussian Splatting in the satellite imaging domain.

Problem

• Gaussian Splatting
• Satellite Imagery
• RPC Model
• Geometric Error
• Camera Model Approximation

Innovation

• Gaussian Splatting
• Rational Polynomial Camera (RPC)
• Satellite Imagery
• Covariance Projection
• Geo-coordinate Transformation