Title: Test3R: Learning to Reconstruct 3D at Test Time
Venue: NeurIPS 2025
Accepted: yes
Keywords: 3D reconstruction
Abstract: 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 \textbf{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.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes Test3R, which improves 3D reconstruction accuracy through test-time training. Specifically, it optimizes consistency across image pairs using a triplet consistency objective, enhancing overall reconstruction quality. The method employs prompt tuning, adjusting only the prompt parameters, resulting in relatively low computational cost. Test3R surpasses some previous methods across several challenging datasets and tasks without relying on camera parameters. Test3R demonstrates strong generalization capabilities and can be integrated into various reconstruction frameworks based on DUSt3R.\n\nSTRENGTHS AND WEAKNESSES:\n# Strengths\n\n1. The paper is clearly written and easy to follow.\n\n2. The technical design of the proposed method is reasonable.\n\n# Weaknesses\n\n1. Although the triplet consistency-based optimization objective is technically sound, it is not an optimal approach since it does not fully exploit the information available in test scenes. A more effective approach might directly leverage images for optimization, as demonstrated by methods like 3DGS.\n\n2. Recent feed-forward approaches such as VGGT can handle reconstruction with multiple input views without needing pair-wise depth estimation, theoretically avoiding the inconsistency issues discussed regarding DUSt3R. However, the authors lack discussion and comparison with such methods.\n\n3. The proposed method requires per-scene optimization, which, even when tuning only part of the parameters, inevitably reduces speed. The paper seems to lack a thorough analysis and comparison of reconstruction speed, which is critical. If the method is slow, it would be beneficial for the authors to include comparisons with other per-scene optimization methods such as some 3DGS based methods.\n\n4. Overall, the proposed approach is relatively simple, essentially adding per-scene optimization as a post-processing step to DUSt3R. Moreover, the chosen optimization objective is straightforward but clearly suboptimal, and the Visual Prompt Tuning component essentially utilizes a mature fine-tuning strategy. Thus, I consider the novelty and technical contribution of this paper to be very limited.\n\n5. There are minor issues, such as the typo \"recomstruction\" on line 39.\n\nQUESTIONS:\nAll my concerns have been detailed in the weaknesses section.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes Test3R, a test-time fine-tuning framework aimed at enhancing the performance of feedforward reconstruction methods such as DUSt3R. The authors observe that the output point map of the same image varies when paired with different images. To address this inconsistency, they introduce a prompt tuning strategy applied at test time. Specifically, prompt tuning is implemented by appending and fine-tuning additional tokens. Experimental results demonstrate that the proposed method improves the performance of the base models.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**:\n\n- Test-time fine-tuning is an effective approach for improving the performance of feedforward reconstruction models.\n- The proposed method consistently demonstrates improvements over baseline models.\n\n**Weaknesses**:\n\n- [W1] The authors repeatedly claim that their method is nearly cost-free; however, prompt tuning clearly incurs computational cost. No runtime analysis or comparison is provided.\n    \n- [W2] The motivation for using prompt tuning is not sufficiently ablated. While test-scene fine-tuning can naturally improve performance, it remains unclear why prompt tuning is specifically needed. There are lots of other fine-tuning methods that can be used, such as fine-tuning or LoRA.    \n\n- [W3] The authors claim the method is “universally applicable and nearly cost-free,” yet all experiments are conducted on DUSt3R/MASt3R/MonST3R, which share the same architecture. It is unclear how well the method generalizes to other models like CUT3R or VGGT, which support more input images.\n\n- [W4] Since the method operates directly on image pairs, qualitative results showing outputs before and after applying Test3R would help demonstrate its effectiveness.\n\n- [W5] Other writing issues:\n  - Line 39 typo: \"reconstruction.\"\n  - Section 4.1 is redundant and overlaps with content in Lines 31–43.\n  - The method operates on only two image pairs, making the ellipsis in the left part of Fig. 2 unnecessary.\n  - Line 232 mentions using DUST3R as the backbone, while Line 64 refers to MAST3R and MonST3R. This inconsistency should be clarified.\n\nQUESTIONS:\nMost important questions:\n- [Same as W1] Provide runtime analysis.\n- [Same as W2] How does the used prompt tuning compare to full fine-tuning or LoRA? \n- [Same as W4] Directly compare on image pairs with our without test time fine-tuning.\n \n\n- [Q1] The method is limited to handling three input views. However, in Sec. 5.1, experiments are conducted with 3–5 and 2–4 views. How is this discrepancy addressed?\n- [Q2] Can the method be extended to handle a larger number of input views, such as more than 10?\n- [Q3] In Fig. 1, the point maps are aligned using only ICP, as DUST3R outputs lack consistent scale. This undermines the reliability of the visualizations. Additionally, no comparable visualization of Test3R is provided.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 5, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces Test3R, a novel approach for performing test-time training on pairwise 3D reconstruction models, such as DUSt3R and MASt3R. The authors observe that DUSt3R produces inconsistent reference-view reconstructions when paired with different source views, which can negatively affect the final global alignment needed for a coherent 3D reconstruction. To address this issue, Test3R leverages visual prompt tuning, introducing a few learnable continuous tokens at test time. The method optimizes cross-view consistency of the reference-view pointmap as its training objective. Test3R is primarily evaluated on DUSt3R, demonstrating improvements in both multi-view depth estimation and 3D reconstruction. Additionally, the authors show that the approach generalizes to other pairwise architectures, such as MASt3R and MonST3R.\n\nSTRENGTHS AND WEAKNESSES:\nStrength:\n1. The idea of using test-time training through visual prompt tuning for 3D reconstruction is both interesting and novel. It offers an alternative to classical optimization-based methods, such as bundle adjustment, for improving 3D reconstruction at test time.\n2. The experiments show that the proposed method improves the baseline, DUSt3R on both multi-view depth estimation and 3D reconstruction. Moreover, the proposed method also generalizes well to other pairwise methods, including MASt3R and MonST3R.\n\nWeaknesses:\n1. Incomplete evaluation: The authors only conduct experiments on depth estimation and 3D reconstruction. The reviewer expects additional results on camera pose estimation.\n2. Ablation study: It would be helpful to include a comparison with (naive or LoRa-based) test-time finetuning approaches that adapt the entire model or only the encoder, without introducing extra tokens.\n3. The proposed method appears to be specifically designed for pairwise methods, whereas current state-of-the-art approaches are predominantly multi-view models, such as VGGT and CUT3R. This focus may limit the broader applicability and impact of the proposed approach.\n\nQUESTIONS:\n1. At test time, are the triplets processed sequentially or in batches? If they are processed in batches, what batch size is used?\n2. The reviewer is curious whether the proposed method is compatible with using correspondences (e.g., from an off-the-shelf model), where the training objective is to enforce that points from corresponding pixels are close to each other. This extension could potentially make the approach work with multi-view methods as well.\n\nLIMITATIONS:\nThe primary limitation of the proposed method is that it is currently only compatible with pairwise approaches. The authors also discuss additional limitations in the appendix.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 4, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces Test3R, a test-time learning technique designed to improve the geometric consistency of pairwise dense matching methods like DUSt3R for 3D reconstruction. The authors identify that methods based on pairwise predictions can produce pointmaps that are imprecise and mutually inconsistent when a reference image is paired with different source views. Test3R addresses this by enforcing geometric consistency at test time. For a given image triplet \n(I1​,I2​,I3​), the method generates two reconstructions relative to the common image I1​ (from pairs (I1​,I2​) and (I1​,I3​)) and then optimizes the network via a self-supervised loss to maximize the geometric alignment between them. This optimization is performed efficiently using prompt tuning. Experiments show that this approach significantly improves the performance of DUSt3R on 3D reconstruction and multi-view depth estimation tasks, achieving state-of-the-art results.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. The core idea of Test3R is simple yet effective. Enforcing consistency between two different pairwise predictions that share a common view is an effective self-supervision signal that directly targets the problem with pairwise reconstruction methods.\n2.  The method demonstrates significant performance improvements over its baseline (DUSt3R) and achieves state-of-the-art or competitive results on multiple challenging benchmarks for both 3D reconstruction and multi-view depth estimation.\n3. The use of visual prompt tuning for the test-time optimization makes sense. It allows the model to adapt to new scenes while keeping the pretrained backbone frozen, making the process far more efficient than fine-tuning the entire network. The paper also demonstrates good generalization by applying the technique to other models like MAST3R and MonST3R.\n\nWeaknesses:\n1. The main concern is the practical cost of performing optimization at inference time. While the paper claims the method is \"nearly cost-free\", it lacks a detailed analysis of the actual test-time overhead. For any given scene, the model must run multiple forward/backward passes to optimize the prompts. This could be a significant drawback for applications requiring fast inference, and the paper does not quantify this trade-off (e.g., time per scene vs. baseline).\n2. The method relies on sampling image triplets from the test set, but the strategy for this selection is not discussed. The performance could be sensitive to which triplets are chosen (e.g., wide vs. narrow baseline pairs), how many triplets are used for optimization, and how they are sampled. This underspecified component makes the method less reproducible and its robustness unclear.\n\nQUESTIONS:\n1. Could you provide a more detailed analysis of the test-time computational overhead? For example, for a typical scene in the 7Scenes or DTU datasets, how many optimization iterations are performed, and what is the total processing time compared to a single inference pass of the original DUSt3R? This is critical for understanding the practical viability of the method.\n\n2. How are the image triplets (I1​,I2​,I3​) selected from the test set? Is the process random, or is there a specific strategy involved? How does the performance of Test3R vary with different numbers of triplets or different selection strategies (e.g., prioritizing wider baselines)?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}