The current image-based metrics, such as Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Learned Perceptual Image Patch Similarity (LPIPS), have limitations in evaluating the geometric accuracy of 3D reconstructions, especially in large-scale outdoor environments. These metrics primarily focus on pixel-level comparisons between rendered images and ground truth images, which may not capture the underlying geometry of complex 3D scenes accurately.
To address these limitations, new evaluation metrics can be developed to better capture the geometric fidelity of 3D reconstructions. One approach is to incorporate geometric-based metrics, such as Chamfer distance, Hausdorff distance, or Normal Consistency Error, which directly measure the discrepancies between reconstructed 3D models and ground truth point clouds. These metrics provide a more comprehensive assessment of the geometric accuracy of 3D reconstructions, taking into account shape, structure, and spatial relationships.
Moreover, researchers can explore the use of advanced geometric analysis techniques, such as mesh quality metrics, surface normal consistency checks, and volumetric evaluations, to assess the fidelity of reconstructed 3D models. By combining image-based metrics with geometric evaluations, a more holistic and accurate assessment of 3D reconstruction algorithms can be achieved, ensuring that the resulting models faithfully represent the underlying geometry of the scenes.