EAGS: Error-Aware Gaussian Splatting with Dual-Confidence-Guided Modeling for Uncalibrated Driving Scenes

Feed-forward 3D reconstruction for autonomous driving hasdemonstrated strong performance and promising generalization capabil-ities. However, pixel-level methods often suffer from multi-view inconsis-tencies and content duplication. While voxel-level approaches alleviatethese issues by predicting 3D Gaussian primitives in voxel space, theyremain limited by resolution and computational overhead. We introducean efficient feed-forward framework for scene reconstruction from uncal-ibrated images. The key insight is the explicit decoupling of instanta-neous generation necessity and long-term geometric persistence duringGaussian generation. Specifically, we introduce Generation Confidencedriven by reprojection errors to enable demand-aware Gaussian expan-sion, and Reuse Confidence constructed from depth uncertainty and tem-poral decay to model the long-term stability. To balance fidelity andsparsity, we employ differentiable voxelization to aggregate neighboringfeatures and suppress spatial redundancy, followed by confidence-awaresoft-gating pruning. Gaussian distillation is further introduced to enablea compact set of backbone Gaussians to approximate the full scene repre-sentation. Extensive experiments on multiple datasets demonstrate thatour method achieves strong reconstruction quality and robustness whilemaintaining a highly compact scene representation.