Confidence-Aware Dense Visual Correspondence

• Confidence-aware dense visual correspondence integrates explicit confidence signals to distinguish reliable matches from ambiguous ones caused by occlusions, repetitive patterns, and intra-class variations.
• It employs strategies such as predictive distributions, match-distribution sharpness, and consistency-derived masks to refine training, optimize geometric estimation, and filter unreliable supervision.
• Empirical results across semantic matching, optical flow, and 3D registration demonstrate that confidence-based methods improve performance metrics and stability in diverse visual tasks.

Confidence-aware dense visual correspondence denotes a family of methods that estimate dense correspondences together with an explicit reliability signal, typically a pixel-wise, point-wise, or query-wise confidence map, uncertainty estimate, or confidence-weighted distribution over matches. Across semantic correspondence, optical flow, geometric matching, dense two-view structure from motion, 3D shape correspondence, point-cloud registration, and robot manipulation, the common objective is to distinguish reliable matches from ambiguous ones caused by intra-class variation, occlusion, repetitive texture, topology variation, or symmetry, and then to use that reliability signal for training, refinement, filtering, or downstream geometric estimation (Kim et al., 2022, Truong et al., 2021, Chen et al., 2023).

1. Problem setting and conceptual scope

Dense semantic visual correspondence seeks a mapping between pixels or small patches across images such that semantically corresponding parts match even under viewpoint changes, deformation, appearance changes, and background clutter. In a standard image-to-image formulation, a feature extractor produces descriptor tensors $D_s, D_t \in \mathbb{R}^{h \times w \times d}$, and a matching cost volume is constructed by cosine similarity,

$\mathcal{C}(i,j) \;=\; D_s(i)^{\top} D_t(j),$

yielding a $4$D cost volume of shape $h \times w \times h \times w$ (Kim et al., 2022). Related formulations appear in semantic matching and dense flow, where the task is to estimate a per-pixel displacement field $F(x) = (u(x), v(x))$ relating two images $I_1, I_2$ (Truong et al., 2021), and in dense object descriptors, where a pixel descriptor function $f$ maps each pixel to a descriptor vector and matching is performed by nearest neighbor in descriptor space (Hadjivelichkov et al., 2021).

Confidence awareness becomes necessary because dense matching is intrinsically ambiguous. The cited work attributes failures to repetitive textures, occlusions, intra-class variations, homogeneous regions, large displacements, non-rigid deformation, background clutter, and pseudo-label noise in weakly or self-supervised training (Kim et al., 2022, Truong et al., 2021, Huang et al., 2020). In dense two-view structure from motion, the issue is posed explicitly as the difficulty of using per-pixel optical flow correspondences for accurate pose estimation because perfect per-pixel correspondence is “difficult, if not impossible, to establish” (Chen et al., 2023).

The same principle extends beyond 2D image pairs. In topology-varying dense 3D shape correspondence, an implicit function produces a part embedding vector for each 3D point, and the method reports that, during inference, a user-selected source point can be accompanied by a confidence score indicating whether there is a correspondence on the target shape (Liu et al., 2020). In rigid point-cloud registration, a dense soft alignment map induces per-source-point confidence scores used for confidence-aware sampling and consensus voting (Ginzburg et al., 2021). In deformable garment manipulation, the model predicts a calibrated distribution over dense correspondences from a deformed observation to a canonical garment image, and action commitment is gated by confidence derived from that distribution (Sunil et al., 4 Sep 2025).

2. Representations of confidence and uncertainty

One major line of work represents confidence through explicit predictive distributions. PDC-Net models the per-pixel flow as a constrained mixture of bi-variate Laplace components sharing a common mean $\mu(x)$,

$$p(f \mid x) = \sum_{k=1}^K \pi_k(x)\,\mathcal{L}(f; \mu(x), \sigma_k^2(x)),$$

and defines a probability-of-correctness confidence

$$c_R(x) = P(\|y-\mu(x)\|_\infty < R) = \sum_{k=1}^K \pi_k(x)\,[1-\exp(-\sqrt{2}R/\sigma_k(x))]^2.$$

This probability is presented as better aligned with downstream usage than raw variance and is used to identify occlusions, motion boundaries, and homogeneous regions (Truong et al., 2021). DTV-SfM similarly treats optical flow as a per-pixel conditional distribution and defines confidence as probability mass within a radius $\mathcal{C}(i,j) \;=\; D_s(i)^{\top} D_t(j),$0 around the mean flow,

$\mathcal{C}(i,j) \;=\; D_s(i)^{\top} D_t(j),$1

using the resulting map as an aleatoric uncertainty estimate for weighted geometric optimization (Chen et al., 2023).

A second line uses match-distribution sharpness. Cycle-Correspondence Loss computes a softmax matching distribution

$\mathcal{C}(i,j) \;=\; D_s(i)^{\top} D_t(j),$2

predicts coordinates by spatial expectation, and interprets the variances $\mathcal{C}(i,j) \;=\; D_s(i)^{\top} D_t(j),$3 of the marginals as confidence. The cycle loss is then weighted by

$\mathcal{C}(i,j) \;=\; D_s(i)^{\top} D_t(j),$4

with $\mathcal{C}(i,j) \;=\; D_s(i)^{\top} D_t(j),$5, and high-variance keypoints are pruned by a quantile rule (Adrian et al., 2024). In robotic garment correspondence, no separate confidence head is used; confidence is computed directly from the correspondence distribution by

$\mathcal{C}(i,j) \;=\; D_s(i)^{\top} D_t(j),$6

with optional region-level aggregation

$\mathcal{C}(i,j) \;=\; D_s(i)^{\top} D_t(j),$7

so that occluded or ambiguous pixels yield broad, low-confidence distributions (Sunil et al., 4 Sep 2025).

A third line uses consistency-derived masks or scores. Joint learning of feature extraction and cost aggregation computes forward and backward flows from Winner-Take-All matches and applies a forward-backward consistency test with $\mathcal{C}(i,j) \;=\; D_s(i)^{\top} D_t(j),$8 and $\mathcal{C}(i,j) \;=\; D_s(i)^{\top} D_t(j),$9 to obtain a binary mask $4$0 of non-occluded, more reliable matches (Kim et al., 2022). CAMNet constructs self-supervised binary confidence labels by thresholding flow error against synthetic ground-truth flow,

$4$1

and trains a dense confidence head to approximate the probability that a predicted semantic flow is correct (Huang et al., 2020).

A fourth line uses evidential uncertainty. SURE predicts Normal-Inverse-Gamma parameters for sub-pixel offset regression and decomposes predictive variance into aleatoric and epistemic terms,

$4$2

with total uncertainty obtained by averaging across axes. In the reported experiments, matches are retained if $4$3 and $4$4, with quantile thresholds set to $4$5 (Li et al., 5 Mar 2026).

Finally, some methods encode confidence directly in classifier posteriors or similarity peaks. “Dense Semantic Correspondence where Every Pixel is a Classifier” models each source pixel by an exemplar LDA classifier and uses the posterior

$4$6

as a globally interpretable confidence map without additional calibration (Bristow et al., 2015). Deep Weighted Consensus defines per-source-point confidence by the peak of a dense soft alignment map,

$4$7

clamps negative peaks to zero, and normalizes these values to a sampling distribution for consensus registration (Ginzburg et al., 2021).

3. Confidence-aware learning objectives

Confidence enters training either by masking unreliable supervision or by scaling error contributions. In weakly supervised semantic correspondence, the feature extractor and cost aggregation module are trained jointly with dense InfoNCE-style losses restricted to forward-backward consistent pixels. For the feature extractor,

$4$8

and analogous losses are defined for the aggregation module and for cross-supervision between the two modules (Kim et al., 2022). The paper reports that removing joint learning drops PCK from $4$9 to $h \times w \times h \times w$0, and removing confidence-aware loss drops PCK to $h \times w \times h \times w$1 on PF-PASCAL (Kim et al., 2022).

In CAMNet, the total generator objective combines semantic alignment, confidence supervision, and adversarial supervision,

$h \times w \times h \times w$2

with reported hyperparameters $h \times w \times h \times w$3, $h \times w \times h \times w$4, $h \times w \times h \times w$5, and $h \times w \times h \times w$6 (Huang et al., 2020). The confidence loss is a cross-entropy objective on self-supervised binary confidence labels, while the adversarial term uses a PatchGAN discriminator to assess the realism of warped images (Huang et al., 2020).

Test-time optimization offers a distinct use of confidence. Deep Matching Prior defines a confidence-aware contrastive loss by computing a positive softmax probability

$h \times w \times h \times w$7

then gating each sample with

$h \times w \times h \times w$8

and optimizing

$h \times w \times h \times w$9

With $F(x) = (u(x), v(x))$0 and $F(x) = (u(x), v(x))$1, low-confidence samples produce no gradient, which the paper attributes to more stable convergence when optimizing on a single image pair (Hong et al., 2021).

Descriptor learning in multi-object scenes uses confidence at a different granularity. “Fully Self-Supervised Class Awareness in Dense Object Descriptors” defines an object-level dissimilarity confidence

$F(x) = (u(x), v(x))$2

and scales the negative non-match loss by that value,

$F(x) = (u(x), v(x))$3

This does not yield pixel-level confidence at inference, but it uses confidence-aware training to reduce cross-object false matches in clutter (Hadjivelichkov et al., 2021).

Distributional supervision is another recurring pattern. In reactive garment manipulation, the model predicts

$F(x) = (u(x), v(x))$4

and is trained against a multimodal Gaussian-mixture target

$F(x) = (u(x), v(x))$5

by minimizing

$F(x) = (u(x), v(x))$6

This directly accommodates cloth symmetries and yields calibrated probabilities used as confidence (Sunil et al., 4 Sep 2025).

4. Refinement, filtering, and geometry-aware use of confidence

A central use of confidence is to refine initial matches rather than merely score them. CAMNet predicts a base semantic flow $F(x) = (u(x), v(x))$7, estimates a dense confidence map $F(x) = (u(x), v(x))$8, and produces an updated flow $F(x) = (u(x), v(x))$9 through a confidence-aware refinement network. The final flow is a confidence-gated fusion,

$I_1, I_2$0

so that reliable base predictions are preserved while low-confidence regions are updated (Huang et al., 2020).

DualRC-Net uses a coarse-to-fine confidence mechanism. A coarse $I_1, I_2$1D correlation tensor is refined by a learnable neighborhood-consensus module, and the refined coarse scores are then projected to fine resolution as a confidence mask $I_1, I_2$2. Fine-resolution scores are multiplied by this mask,

$I_1, I_2$3

which restricts fine-resolution matching to high-confidence candidates and avoids expensive fine-scale $I_1, I_2$4D convolution (Li et al., 2020). The paper reports average runtime per image pair of approximately $I_1, I_2$5 for DualRC-Net, versus $I_1, I_2$6 for Sparse-NCNet and $I_1, I_2$7 for NCNet, with GPU memory approximately $I_1, I_2$8 for DualRC-Net, $I_1, I_2$9 for Sparse-NCNet, and $f$0 for NCNet (Li et al., 2020).

In geometric estimation, confidence typically becomes a weight. DTV-SfM defines weighted dense bundle adjustment from flow confidence and an inlier mask,

$f$1

with confidence threshold $f$2, then minimizes forward and backward reprojection residuals in a weighted objective (Chen et al., 2023). The method also exploits bidirectional consistency,

$f$3

as a masking or weighting signal (Chen et al., 2023).

PDC-Net uses confidence to select dense inliers for pose estimation. The reported procedure thresholds $f$4, estimates an essential matrix with RANSAC and a $f$5-point solver, and recovers rotation and translation by standard decomposition (Truong et al., 2021). A plausible implication is that confidence acts as a learned inlier prior for downstream robust estimators.

Consensus-based 3D registration uses confidence even more explicitly. Deep Weighted Consensus normalizes per-point confidences to a categorical sampling distribution,

$f$6

samples only confident source points, repeatedly solves small rigid alignments by SVD, and selects the transform with the lowest Chamfer distance (Ginzburg et al., 2021). The paper’s ablation reports that replacing confidence sampling with uniform sampling degrades rotation RMSE from $f$7 to $f$8 on ModelNet40 and from $f$9 to $\mu(x)$0 on FAUST (Ginzburg et al., 2021).

5. Empirical regimes and benchmark evidence

In semantic correspondence benchmarks, the confidence-aware joint learning framework reports PF-PASCAL PCK values of $\mu(x)$1 for “Ours w/NCNet” and $\mu(x)$2 for “Ours w/CATs,” with the latter listed as the highest among the reported PF-PASCAL results. On TSS, “Ours w/CATs” reaches an average of $\mu(x)$3, compared with $\mu(x)$4 for CATs and $\mu(x)$5 for NCNet (Kim et al., 2022). CAMNet reports PF-PASCAL test PCK values of $\mu(x)$6, $\mu(x)$7, and $\mu(x)$8 at $\mu(x)$9, exceeding the cited self-supervised SFNet baseline at all three thresholds, and PF-WILLOW test PCK values of $$p(f \mid x) = \sum_{k=1}^K \pi_k(x)\,\mathcal{L}(f; \mu(x), \sigma_k^2(x)),$$0, $$p(f \mid x) = \sum_{k=1}^K \pi_k(x)\,\mathcal{L}(f; \mu(x), \sigma_k^2(x)),$$1, and $$p(f \mid x) = \sum_{k=1}^K \pi_k(x)\,\mathcal{L}(f; \mu(x), \sigma_k^2(x)),$$2 (Huang et al., 2020).

In dense flow and geometric matching, PDC-Net reports MegaDepth PCK-1 of $$p(f \mid x) = \sum_{k=1}^K \pi_k(x)\,\mathcal{L}(f; \mu(x), \sigma_k^2(x)),$$3, PCK-3 of $$p(f \mid x) = \sum_{k=1}^K \pi_k(x)\,\mathcal{L}(f; \mu(x), \sigma_k^2(x)),$$4, and PCK-5 of $$p(f \mid x) = \sum_{k=1}^K \pi_k(x)\,\mathcal{L}(f; \mu(x), \sigma_k^2(x)),$$5, with multi-scale inference improving these to $$p(f \mid x) = \sum_{k=1}^K \pi_k(x)\,\mathcal{L}(f; \mu(x), \sigma_k^2(x)),$$6, $$p(f \mid x) = \sum_{k=1}^K \pi_k(x)\,\mathcal{L}(f; \mu(x), \sigma_k^2(x)),$$7, and $$p(f \mid x) = \sum_{k=1}^K \pi_k(x)\,\mathcal{L}(f; \mu(x), \sigma_k^2(x)),$$8 (Truong et al., 2021). On KITTI-2015 training splits, it reports AEPE $$p(f \mid x) = \sum_{k=1}^K \pi_k(x)\,\mathcal{L}(f; \mu(x), \sigma_k^2(x)),$$9 and F1 $$c_R(x) = P(\|y-\mu(x)\|_\infty < R) = \sum_{k=1}^K \pi_k(x)\,[1-\exp(-\sqrt{2}R/\sigma_k(x))]^2.$$0, surpassing RAFT’s cited F1 of $$c_R(x) = P(\|y-\mu(x)\|_\infty < R) = \sum_{k=1}^K \pi_k(x)\,[1-\exp(-\sqrt{2}R/\sigma_k(x))]^2.$$1 despite being trained for geometric matching (Truong et al., 2021). DTV-SfM reports runtime on a GTX 1080 Ti of flow $$c_R(x) = P(\|y-\mu(x)\|_\infty < R) = \sum_{k=1}^K \pi_k(x)\,[1-\exp(-\sqrt{2}R/\sigma_k(x))]^2.$$2, RANSAC $$c_R(x) = P(\|y-\mu(x)\|_\infty < R) = \sum_{k=1}^K \pi_k(x)\,[1-\exp(-\sqrt{2}R/\sigma_k(x))]^2.$$3, weighted bundle adjustment $$c_R(x) = P(\|y-\mu(x)\|_\infty < R) = \sum_{k=1}^K \pi_k(x)\,[1-\exp(-\sqrt{2}R/\sigma_k(x))]^2.$$4, depth $$c_R(x) = P(\|y-\mu(x)\|_\infty < R) = \sum_{k=1}^K \pi_k(x)\,[1-\exp(-\sqrt{2}R/\sigma_k(x))]^2.$$5, for a total of approximately $$c_R(x) = P(\|y-\mu(x)\|_\infty < R) = \sum_{k=1}^K \pi_k(x)\,[1-\exp(-\sqrt{2}R/\sigma_k(x))]^2.$$6 per pair, and states that it achieves state-of-the-art camera pose results on YFCC100M and ScanNet while also improving depth accuracy (Chen et al., 2023).

In self-supervised descriptor learning, Cycle-Correspondence Loss reports that “CCL + Identical View” achieves PCK@10 of $$c_R(x) = P(\|y-\mu(x)\|_\infty < R) = \sum_{k=1}^K \pi_k(x)\,[1-\exp(-\sqrt{2}R/\sigma_k(x))]^2.$$7, AUC$$c_R(x) = P(\|y-\mu(x)\|_\infty < R) = \sum_{k=1}^K \pi_k(x)\,[1-\exp(-\sqrt{2}R/\sigma_k(x))]^2.$$8 of $$c_R(x) = P(\|y-\mu(x)\|_\infty < R) = \sum_{k=1}^K \pi_k(x)\,[1-\exp(-\sqrt{2}R/\sigma_k(x))]^2.$$9, and normalized mean pixel error of $\mathcal{C}(i,j) \;=\; D_s(i)^{\top} D_t(j),$00, while the RGB-only self-supervised “Identical View” baseline reports PCK@10 of $\mathcal{C}(i,j) \;=\; D_s(i)^{\top} D_t(j),$01, AUC$\mathcal{C}(i,j) \;=\; D_s(i)^{\top} D_t(j),$02 of $\mathcal{C}(i,j) \;=\; D_s(i)^{\top} D_t(j),$03, and error of $\mathcal{C}(i,j) \;=\; D_s(i)^{\top} D_t(j),$04 (Adrian et al., 2024). In robot grasping, the same paper reports success rates of $\mathcal{C}(i,j) \;=\; D_s(i)^{\top} D_t(j),$05 for CCL, $\mathcal{C}(i,j) \;=\; D_s(i)^{\top} D_t(j),$06 for MO Collage Scenes, $\mathcal{C}(i,j) \;=\; D_s(i)^{\top} D_t(j),$07 for MO-maskless, and $\mathcal{C}(i,j) \;=\; D_s(i)^{\top} D_t(j),$08 for Identical View (Adrian et al., 2024).

In semi-dense uncertainty-refined matching, SURE reports relative pose AUC on ScanNet of $\mathcal{C}(i,j) \;=\; D_s(i)^{\top} D_t(j),$09 at $\mathcal{C}(i,j) \;=\; D_s(i)^{\top} D_t(j),$10, $\mathcal{C}(i,j) \;=\; D_s(i)^{\top} D_t(j),$11 at $\mathcal{C}(i,j) \;=\; D_s(i)^{\top} D_t(j),$12, and $\mathcal{C}(i,j) \;=\; D_s(i)^{\top} D_t(j),$13 at $\mathcal{C}(i,j) \;=\; D_s(i)^{\top} D_t(j),$14, compared with $\mathcal{C}(i,j) \;=\; D_s(i)^{\top} D_t(j),$15, $\mathcal{C}(i,j) \;=\; D_s(i)^{\top} D_t(j),$16, and $\mathcal{C}(i,j) \;=\; D_s(i)^{\top} D_t(j),$17 for E-LoFTR. On MegaDepth it reports $\mathcal{C}(i,j) \;=\; D_s(i)^{\top} D_t(j),$18, $\mathcal{C}(i,j) \;=\; D_s(i)^{\top} D_t(j),$19, and $\mathcal{C}(i,j) \;=\; D_s(i)^{\top} D_t(j),$20, versus $\mathcal{C}(i,j) \;=\; D_s(i)^{\top} D_t(j),$21, $\mathcal{C}(i,j) \;=\; D_s(i)^{\top} D_t(j),$22, and $\mathcal{C}(i,j) \;=\; D_s(i)^{\top} D_t(j),$23 for E-LoFTR, while running in $\mathcal{C}(i,j) \;=\; D_s(i)^{\top} D_t(j),$24 compared with $\mathcal{C}(i,j) \;=\; D_s(i)^{\top} D_t(j),$25 for E-LoFTR (Li et al., 5 Mar 2026).

In 3D registration, Deep Weighted Consensus reports ModelNet40 random-split RMSE$\mathcal{C}(i,j) \;=\; D_s(i)^{\top} D_t(j),$26 and RMSE$\mathcal{C}(i,j) \;=\; D_s(i)^{\top} D_t(j),$27, FAUST full-spectrum RMSE$\mathcal{C}(i,j) \;=\; D_s(i)^{\top} D_t(j),$28 and RMSE$\mathcal{C}(i,j) \;=\; D_s(i)^{\top} D_t(j),$29, and shows negligible degradation across the full rotation spectrum, from $\mathcal{C}(i,j) \;=\; D_s(i)^{\top} D_t(j),$30 to $\mathcal{C}(i,j) \;=\; D_s(i)^{\top} D_t(j),$31 RMSE in the rotation-resilience study (Ginzburg et al., 2021). In garment manipulation, the confidence-aware dense correspondence model reports real suspended-image canonical-region classification of $\mathcal{C}(i,j) \;=\; D_s(i)^{\top} D_t(j),$32 for the best suspended-only model, “safe” decisions $\mathcal{C}(i,j) \;=\; D_s(i)^{\top} D_t(j),$33 for the best combined model in the forward direction, table-scene correct-region identification $\mathcal{C}(i,j) \;=\; D_s(i)^{\top} D_t(j),$34, and safe decisions $\mathcal{C}(i,j) \;=\; D_s(i)^{\top} D_t(j),$35 over $\mathcal{C}(i,j) \;=\; D_s(i)^{\top} D_t(j),$36 trials (Sunil et al., 4 Sep 2025).

These results indicate that confidence awareness is not confined to a single benchmark family. It appears in semantic correspondence, optical flow, view-invariant descriptors, two-view geometry, semi-dense feature matching, point-cloud registration, and deformable manipulation, with different operational meanings but a recurring empirical role: suppressing erroneous matches and improving the quality of the retained subset.

6. Failure modes, misconceptions, and open directions

A common misconception is that confidence in dense correspondence is equivalent to raw matching similarity. Several cited methods explicitly reject that view. PDC-Net argues that a constrained mixture distribution provides a better calibrated probability-of-correctness than raw variance or heuristic distinctiveness scores (Truong et al., 2021). SURE is motivated by the claim that conventional models “rely solely on feature similarity, lacking an explicit mechanism to estimate the reliability of predicted matches, leading to overconfident errors” (Li et al., 5 Mar 2026). The LDA-based classifier formulation likewise distinguishes calibrated posterior probabilities from uncalibrated similarity metrics such as cosine similarity or normalized cross-correlation (Bristow et al., 2015).

Another misconception is that confidence is only useful at inference. Several systems use confidence primarily as a training-time mechanism. In weakly supervised semantic correspondence, confidence masks filter unreliable pseudo labels (Kim et al., 2022). In self-supervised class-aware descriptors, object-level confidence weights negative non-match loss (Hadjivelichkov et al., 2021). In Cycle-Correspondence Loss, confidence arises from heatmap variance and is used to prune and scale cycle errors during training, with no separate confidence head (Adrian et al., 2024). This suggests that confidence-aware correspondence is as much about optimizing supervision quality as about downstream abstention.

The limitations are also consistent across domains. Residual label noise can persist in highly repetitive or heavily occluded regions, and confidence hyperparameters such as $\mathcal{C}(i,j) \;=\; D_s(i)^{\top} D_t(j),$37, $\mathcal{C}(i,j) \;=\; D_s(i)^{\top} D_t(j),$38, temperature $\mathcal{C}(i,j) \;=\; D_s(i)^{\top} D_t(j),$39 or $\mathcal{C}(i,j) \;=\; D_s(i)^{\top} D_t(j),$40, and threshold values affect the precision-recall trade-off of retained matches (Kim et al., 2022, Chen et al., 2023). PDC-Net notes that its mixture is unimodal around $\mathcal{C}(i,j) \;=\; D_s(i)^{\top} D_t(j),$41 and therefore cannot explicitly represent multiple plausible matches except by increasing variance (Truong et al., 2021). CCL reports that very low overlap between images increases pruning and weakens the training signal (Adrian et al., 2024). Garment manipulation reports that confidence can be overestimated in challenging states and identifies improved uncertainty estimation, including temperature calibration and ensembles, as a future direction (Sunil et al., 4 Sep 2025). The topology-varying 3D correspondence paper states in its abstract that the model can indicate whether correspondence exists on the target shape, which is especially beneficial for man-made objects with different part constitutions; this suggests that abstention under structural mismatch is a central property of confidence-aware correspondence in 3D as well (Liu et al., 2020).

Across the literature, a stable pattern emerges. Confidence may be binary or probabilistic, learned or induced from distributions, local or object-level, aleatoric or epistemic, but it is repeatedly used to do one of four things: filter unreliable supervision, refine ambiguous matches, weight geometric optimization, or defer commitment when correspondence is uncertain. A plausible implication is that the field is converging from confidence-as-score toward confidence-as-control signal, where reliability estimates actively determine which correspondences participate in learning, which are propagated spatially, and which are trusted by downstream geometric or robotic systems (Huang et al., 2020, Chen et al., 2023, Sunil et al., 4 Sep 2025).