3D Reconstruction with Spatial Memory
Authors: name: Hengyi Wang, name: Lourdes Agapito

We present Spann3R, a novel approach for dense 3D reconstruction from ordered
or unordered image collections. Built on the DUSt3R paradigm, Spann3R uses a
transformer-based architecture to directly regress pointmaps from images
without any prior knowledge of the scene or camera parameters. Unlike DUSt3R,
which predicts per image-pair pointmaps each expressed in its local coordinate
frame, Spann3R can predict per-image pointmaps expressed in a global coordinate
system, thus eliminating the need for optimization-based global alignment. The
key idea of Spann3R is to manage an external spatial memory that learns to keep
track of all previous relevant 3D information. Spann3R then queries this
spatial memory to predict the 3D structure of the next frame in a global
coordinate system. Taking advantage of DUSt3R's pre-trained weights, and
further fine-tuning on a subset of datasets, Spann3R shows competitive
performance and generalization ability on various unseen datasets and can
process ordered image collections in real time. Project page:
https://hengyiwang.github.io/projects/spanner