DAPS-AGF: Depth-Aware Perceptual Similarity with Adaptive Gradient Filtering for Enhanced Outdoor Scene Reconstruction

Aqsa Yousaf, Arkajyoti Mitra, Paul Agbaje, Afia Anjum, Habeeb Olufowobi; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, 2025, pp. 2669-2677

3D Gaussian Splatting (3DGS) is a recent technique for real-time scene reconstruction. However, in large outdoor scenes, it often fails to reconstruct peripheral or distant regions accurately. These areas appear in fewer views and receive weaker multi-view supervision, leading to lower gradient signals, fewer Gaussians, and ultimately degraded structural quality. To address this limitation, we propose two complementary enhancements to the 3DGS pipeline. Our Depth-Aware Perceptual Similarity (DAPS) module uses monocular depth to strengthen optimization in weakly supervised regions, leading to sharper edges and improved reconstruction quality. Additionally, we introduce Adaptive Gradient Filtering (AGF), a dynamic densification mechanism that selectively clones Gaussians based on gradient statistics, ensuring visually faithful reconstruction without excessive memory growth. We curate a challenging outdoor benchmark by selecting scenes from the Tanks & Temples and Mip-NeRF 360 datasets, designed to test reconstruction quality and memory efficiency under real-world visual complexity. Experimental results show DAPS improves SSIM by 3.05%, PSNR by 3.94%, and reduces LPIPS by 17.78%, while AGF cuts memory usage by 38.5% over baseline 3DGS without sacrificing image quality.