ConMax3D: Frame Selection for 3D Reconstruction Through Concept Maximization

Akash Malhotra, Nacera seghouani, Gilbert Badaro, Christophe Blaya

Abstract:

This paper proposes a novel best frames selection algorithm, ConMax3D, for multiview 3D reconstruction
that utilizes image segmentation and clustering to identify and maximize concept diversity. This method aims
to improve the accuracy and interpretability of selecting frames for a photorealistic 3D model generation with
NeRF or 3D Gaussian Splatting without relying on camera pose information. We evaluate ConMax3D on the
LLFF dataset and show that it outperforms current state-of-the-art baselines, with improvements in PSNR of
up to 43.65%, while retaining computational efficiency.