Test3R: Learning to Reconstruct 3D at Test Time
Dense matching methods like DUSt3R regress pairwise pointmaps for 3D
reconstruction. However, the reliance on pairwise prediction and the limited
generalization capability inherently restrict the global geometric consistency.
In this work, we introduce Test3R, a surprisingly simple test-time learning
technique that significantly boosts geometric accuracy. Using image triplets
(I_1,I_2,I_3), Test3R generates reconstructions from pairs (I_1,I_2) and
(I_1,I_3). The core idea is to optimize the network at test time via a
self-supervised objective: maximizing the geometric consistency between these
two reconstructions relative to the common image I_1. This ensures the model
produces cross-pair consistent outputs, regardless of the inputs. Extensive
experiments demonstrate that our technique significantly outperforms previous
state-of-the-art methods on the 3D reconstruction and multi-view depth
estimation tasks. Moreover, it is universally applicable and nearly cost-free,
making it easily applied to other models and implemented with minimal test-time
training overhead and parameter footprint. Code is available at
https://github.com/nopQAQ/Test3R.
6850d08d5e07650ecce8908b
Yuheng Yuan
claimed_verified
2025-06-17T15:13:10.277Z
67ac56c90f861b63617d153d
/avatars/96f5e60031fe4fe677159dccfecaa65c.svg
Yuan Yuheng
user
nopyyh
6850d08d5e07650ecce8908c
Qiuhong Shen
claimed_verified
2025-06-17T07:21:24.996Z
643a6e89a856622f9788bf67
/avatars/419c0379f072295b27d4bfe2f8fb946d.svg
qiuhong shen
user
florinshum
6850d08d5e07650ecce8908d
Shizun Wang
6850d08d5e07650ecce8908e
Xingyi Yang
6850d08d5e07650ecce8908f
Xinchao Wang
Test3R: Learning to Reconstruct 3D at Test Time
Dense matching methods like DUSt3R regress pairwise pointmaps for 3D
reconstruction. However, the reliance on pairwise prediction and the limited
generalization capability inherently restrict the global geometric consistency.
In this work, we introduce Test3R, a surprisingly simple test-time learning
technique that significantly boosts geometric accuracy. Using image triplets
(I_1,I_2,I_3), Test3R generates reconstructions from pairs (I_1,I_2) and
(I_1,I_3). The core idea is to optimize the network at test time via a
self-supervised objective: maximizing the geometric consistency between these
two reconstructions relative to the common image I_1. This ensures the model
produces cross-pair consistent outputs, regardless of the inputs. Extensive
experiments demonstrate that our technique significantly outperforms previous
state-of-the-art methods on the 3D reconstruction and multi-view depth
estimation tasks. Moreover, it is universally applicable and nearly cost-free,
making it easily applied to other models and implemented with minimal test-time
training overhead and parameter footprint. Code is available at
https://github.com/nopQAQ/Test3R.
Test3R, a test-time learning technique for 3D reconstruction, enhances geometric accuracy by optimizing network consistency using self-supervised learning on image triplets.
DUSt3R dense matching 3D reconstruction geometric accuracy test-time learning image triplets self-supervised objective cross-pair consistency