Abstract
In recent years, machine learning has led to significant advances in computer graphics and 3D scene understanding, enabling photorealistic novel view synthesis and accurate 3D reconstruction from 2D images. While implicit methods like neural radiance fields (NeRFs) achieve impressive fidelity, explicit approaches such as voxel grids and 3D Gaussian Splatting offer faster training and inference yet remain limited by memory footprint and sensitivity to occlusion. We introduce Cloning-Plenoxels (Clonoxels), a voxel-based framework that automatically detects repeated objects within a scene and enables information sharing between them, improving reconstruction quality and reducing storage requirements. Clonoxels leverages semantic and instance segmentation, rigid alignment, and voxel-by-voxel matching with lighting-aware corrections, producing volumetric representations that are robust to occlusion. Experiments using synthetic and real-world data demonstrate enhanced recovery of hidden structures, lower-memory scene representation, and denoised 3D geometry reconstruction. Our framework offers a practical path toward more efficient and accurate reconstruction of challenging, complex scenes.