Test3R：在測試時學習重建3D模型

摘要

如DUSt3R這類密集匹配方法，通過回歸成對點雲圖來進行三維重建。然而，依賴於成對預測及其有限的泛化能力，本質上限制了全局幾何一致性。在本研究中，我們提出了Test3R，一種出奇簡單的測試時學習技術，顯著提升了幾何精度。利用圖像三元組(I_1,I_2,I_3)，Test3R從成對圖像(I_1,I_2)和(I_1,I_3)生成重建結果。其核心思想是通過自監督目標在測試時優化網絡：最大化相對於共同圖像I_1的這兩次重建之間的幾何一致性。這確保了模型無論輸入如何，都能產生跨對一致的輸出。大量實驗證明，我們的方法在三維重建和多視圖深度估計任務上顯著超越了以往最先進的方法。此外，它具備通用性且幾乎無需額外成本，使其易於應用於其他模型，並以最小的測試時訓練開銷和參數佔用實現。代碼已公開於https://github.com/nopQAQ/Test3R。

English

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：在測試時學習重建3D模型

Test3R: Learning to Reconstruct 3D at Test Time

摘要

Support