3D Gaussian Splatting Methods for Real-World Scenarios

Abstract. 3D Gaussian Splatting (3DGS) is an innovative solution for explicit point-based 3D representation where each point is represented as a Gaussian distribution. Using calibrated images and sparse point cloud for initialization, the scene is reconstructed by optimizing the Gaussian position, orientation, shape and appearance. In this contribution, we present a comprehensive overview of 3DGS methods available on complimentary radiance field reconstruction platforms namely, the original 3DGS implementation as reference, Splatfacto, 3DGS-MCMC and 3DGS-LumaAI to address a broader audience in industry and non-technical users as well. Being an indispensable part of our environment, we are particularly interested in vegetation since the irregular and complex shape of plants and trees, especially dense foliage can challenge the 3D reconstruction. We evaluate the geometric accuracy and completeness in two real-world scenarios, one occlusion-free indoor and one outdoor scenario where the object of interest is placed behind vegetation to investigate how the methods can reconstruct the underlying geometry behind occlusion. To investigate if 3DGS methods can challenge traditional and state-of-the-art 3D reconstruction approaches we compare the results with Multi-View Stereo (MVS) and Neural Radiance Fields (NeRFs). The evaluation is based on point cloud comparison against a ground truth mesh. Just behind MVS, the original 3DGS implementation achieves second best accuracy results outperforming NeRFs in both scenarios, making it the most accurate 3DGS method. 3DGS-MCMC achieves the best and third best completeness for each scenario respectively, making it competitive with MVS and NeRFs in real-world setting. Moreover, we demonstrate 3DGS ability to reliably reconstruct the geometry behind vegetation occlusion indicating the potential for large-scale forestry applications, allowing canopy reconstruction, biomass estimation and agricultural monitoring.