Event keypoint detection has garnered significant attention due to its ability to establish spatial associations through matching, which is fundamental for various computer vision tasks. However, achieving robust event keypoint detection remains challenging due to the difficulty in balancing the exploitation of event data and compatibility with established algorithms. Moreover, the limited use of co-visible information often results in excessive keypoint detection in non-matching regions, leading to incorrect matches. To address these challenges, we propose a novel Co-visible Focused 3D-guided 2D Event Keypoint Detection Network (EV-CGNet), which mainly consists of a 3D-guided 2D feature prototype learning (G2PL) module and a co-visible region-focused detector and descriptor learning (CDDL) module. The proposed method enjoys several merits. First, the proposed G2PL module can leverage fine-grained spatio-temporal cues from event points to guide the learning of event frame feature prototypes. Second, the proposed CDDL module can focus keypoint detection on co-visible regions, ensuring accurate matches. Comprehensive experimental evaluations on six challenging benchmarks show that our method significantly outperforms state-of-the-art event keypoint detection methods.
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cs.CV
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表
Gaussian Splatting has emerged as a popular 3D representation technique, but still struggles with appearance inconsistencies, especially in dark scenes that require active illumination (e.g., camera flashes or co-moving light sources) to capture usable images, leading to dramatic local appearance fluctuations.While existing methods mainly focus on modeling global appearance changes for in-the-wild scenes, such as those caused by different times of day or weather conditions, they fail to handle the severe variations present in dark scenes with moving light sources.In this paper, we propose a novel Gaussian Splatting-based approach for constructing scene representations in dark scenes where active light sources are rigidly attached to the camera and move together with it.Within this framework, we introduce an illumination-weighted loss function that drives the representation toward the underlying unlit scene. Furthermore, instead of adjusting the illumination of each individual Gaussian as in prior work, we employ a tile-based shading scheme that operates directly on the rendered images, greatly reducing computational cost while explicitly separating illumination from intrinsic scene appearance.Additionally, we further refine the learned Gaussian representation by combining the recovered unlit scene appearance with an advanced geometric prior model, which significantly improves geometric accuracy.Experimental results demonstrate that our method achieves superior reconstruction quality in challenging environments compared to state-of-the-art techniques.
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cs.CV
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表