We lifted 12 functions out of this paper's own repositories and ran 3 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| nopqaq/test3r | canonical | 3 of 12 |
| Function | Status | Where it lives |
|---|---|---|
| drop_path | Ran | nopqaq/test3r/croco/models/blocks.py pointer only (licence: NONE) · get_code("f9a1900525331e0f") |
| get_1d_sincos_pos_embed_from_grid | Ran | nopqaq/test3r/croco/models/pos_embed.py pointer only (licence: NONE) · get_code("4bdd36eab04c3f62") |
| pair | Ran | nopqaq/test3r/croco/models/dpt_block.py pointer only (licence: NONE) · get_code("6ba8cee9f5daea41") |
| croco_args_from_ckpt | Not yet run | nopqaq/test3r/croco/models/croco_downstream.py pointer only (licence: NOASSERTION) · get_code("e5b33c5eb8aed38c") |
| get_2d_sincos_pos_embed | Not yet run | nopqaq/test3r/croco/models/pos_embed.py pointer only (licence: NOASSERTION) · get_code("1ffc5791052dedc3") |
| get_2d_sincos_pos_embed_from_grid | Not yet run | nopqaq/test3r/croco/models/pos_embed.py pointer only (licence: NONE) · get_code("4fd80de79832745d") |
| get_frame_number | Not yet run | nopqaq/test3r/datasets_preprocess/preprocess_scannetpp.py pointer only (licence: NONE) · get_code("13dde5d55f6cfae8") |
| load_model | Not yet run | nopqaq/test3r/dust3r/model.py pointer only (licence: NOASSERTION) · get_code("1e3f140b86bb9a08") |
| load_sfm | Not yet run | nopqaq/test3r/datasets_preprocess/preprocess_scannetpp.py pointer only (licence: NONE) · get_code("f99cd4b374e7e664") |
| make_fusion_block | Not yet run | nopqaq/test3r/croco/models/dpt_block.py pointer only (licence: NOASSERTION) · get_code("bd02fbc95de3472f") |
| make_scratch | Not yet run | nopqaq/test3r/croco/models/dpt_block.py pointer only (licence: NOASSERTION) · get_code("45c2e73b37565105") |
| pose_from_qwxyz_txyz | Not yet run | nopqaq/test3r/datasets_preprocess/preprocess_scannetpp.py pointer only (licence: NONE) · get_code("51a7bb739612fff2") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
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.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2506.13750")
get_code_for_paper("2506.13750")
have("2506.13750")
Connect an agent — have() is free.