Image Matching Filtering and Refinement by Planes and Beyond

Dense image matching establishes pixel-wise correspondences and underpins broad applications in computer vision and photogrammetry. However, extending dense matching to global-scale remote sensing remains challenging because image pairs may differ in acquisition time, season, viewpoint, spatial resolution, and land-cover state. The resulting large geometric offsets, partial overlap, and intrinsically unmatchable regions make direct dense correspondence prediction unreliable and inefficient. We thus reformulate dense matching as localization-and-registration: first localizing the matchable overlap and affine geometry, then refining dense residuals within the aligned frame. Based on this formulation, we propose LoRetta, a foundation model coupling matchability-aware affine localization with guided dense registration. We also introduce LEVIR-GM, a global-scale multi-temporal optical matching benchmark with dataset-native matchability labels (103K aligned, 827K augmented pairs, six continents, five years, 0.5-1024 m resolution). We further establish a unified evaluation protocol for sparse, semi-dense, and dense matchers. On LEVIR-GM, LoRetta achieves an area under the curve (AUC) of 83.3%, outperforming the strongest baseline RoMa v2 by 1.6 points, with larger percentage of correct keypoints (PCK) gains of 6.5 and 8.2 points at 1 and 2 pixels, while reducing inference latency by 47.8%. Astronaut-to-satellite and unmanned aerial vehicle (UAV)-to-satellite geolocalization experiments further demonstrate its transferability as a reusable geometric aligner.

Field robots operating across time of day and sensing modalities require accurate image correspondences within onboard time and resource budgets. However, accuracy and runtime reported for individual methods on a single device provide limited guidance for choosing a matcher and its configuration on a target platform. We present MatcherCompass, a deployment-aware benchmark for choosing local feature matchers in field robotics. Under common input and pose-evaluation procedures, we compare nine classical and learned matching pipelines across four image resolutions and supported numerical precisions. Four visual conditions cover viewpoint variation, day--night matching in visible and thermal imagery, and daytime visible--thermal matching. We evaluate pose accuracy using the area under the error--recall curve (AUC) at $5^\circ$, $10^\circ$, and $20^\circ$, and measure runtime, GPU memory, and energy per image pair on four GPU platforms spanning workstation and onboard computers. The results show that changes in hardware, input resolution, and numerical precision can move a matcher across a runtime budget boundary, altering the feasible choices. We organize the measurements into a selection guide that returns all configurations satisfying user-specified time and resource limits, together with their accuracy under the selected visual condition. MatcherCompass provides measured evidence for choosing matching pipelines that fit a robot's sensing conditions and computing hardware. Project page: https://matchercompass.github.io/.

The deep-learning based image matching networks can now handle significantly larger variations in viewpoints and illuminations while providing matched pairs of pixels with sub-pixel precision. These networks have been trained with ground-based image datasets and, implicitly, their performance is optimized for the pinhole camera geometry. Consequently, you get suboptimal performance when such networks are used to match satellite images since those images are synthesized as a moving satellite camera records one line at a time of the points on the ground. In this paper, we present EpiMask, a semi-dense image matching network for satellite images that (1) Incorporates patch-wise affine approximations to the camera modeling geometry; (2) Uses an epipolar distance-based attention mask to restrict cross-attention to geometrically plausible regions; and (3) That fine-tunes a foundational pretrained image encoder for robust feature extraction. Experiments on the SatDepth dataset demonstrate up to 30% improvement in matching accuracy compared to re-trained ground-based models.