arXiv Computer Vision
Oct 1

Matisse: Evidence-Space Reasoning for Active 3D Reconstruction

Matisse is a training‑free framework that combines active 3D reconstruction with keyframe selection by using evidence from a pretrained generative 3D model. It estimates evidential uncertainty via cross‑attention on 3D latent tokens and derives an evidential information gain to guide view acquisition and keyframe selection, reducing redundant observations and supporting multi‑object scenes with occlusion‑aware aggregation. On GSO30, YCB‑V, and Replica, Matisse improves Chamfer distance by 12.7%, 3.8%, and 9.2% respectively, and speeds up end‑to‑end reconstruction by 1.5× compared to the best baseline.

By Xihang Yu, Kaichen Zhou, Lorenzo Shaikewitz, Cl\'ement Jambon, Xiao Zhan, Rajat Talak, Luca Carlone