Learning to Produce Semi-dense Correspondences for Visual Localization

This study addresses the challenge of performing visual localization in demanding conditions such as night-time scenarios, adverse weather, and seasonal changes. While many prior studies have focused on improving image-matching performance to facilitate reliable dense keypoint matching between images, existing methods often heavily rely on predefined feature points on a reconstructed 3D model. Consequently, they tend to overlook unobserved keypoints during the matching process. Therefore, dense keypoint matches are not fully exploited, leading to a notable reduction in accuracy, particularly in noisy scenes. To tackle this issue, we propose a novel localization method that extracts reliable semi-dense 2D-3D matching points based on dense keypoint matches. This approach involves regressing semi-dense 2D keypoints into 3D scene coordinates using a point inference network. The network utilizes both geometric and visual cues to effectively infer 3D coordinates for unobserved keypoints from the observed ones. The abundance of matching information significantly enhances the accuracy of camera pose estimation, even in scenarios involving noisy or sparse 3D models. Comprehensive evaluations demonstrate that the proposed method outperforms other methods in challenging scenes and achieves competitive results in large-scale visual localization benchmarks. The code will be available.

TopicFM+: Boosting Accuracy and Efficiency of Topic-Assisted Feature Matching

This study tackles the challenge of image matching in difficult scenarios, such as scenes with significant variations or limited texture, with a strong emphasis on computational efficiency. Previous studies have attempted to address this challenge by encoding global scene contexts using Transformers. However, these approaches suffer from high computational costs and may not capture sufficient high-level contextual information, such as structural shapes or semantic instances. Consequently, the encoded features may lack discriminative power in challenging scenes. To overcome these limitations, we propose a novel image-matching method that leverages a topic-modeling strategy to capture high-level contexts in images. Our method represents each image as a multinomial distribution over topics, where each topic represents a latent semantic instance. By incorporating these topics, we can effectively capture comprehensive context information and obtain discriminative and high-quality features. Additionally, our method effectively matches features within corresponding semantic regions by estimating the covisible topics. To enhance the efficiency of feature matching, we have designed a network with a pooling-and-merging attention module. This module reduces computation by employing attention only on fixed-sized topics and small-sized features. Through extensive experiments, we have demonstrated the superiority of our method in challenging scenarios. Specifically, our method significantly reduces computational costs while maintaining higher image-matching accuracy compared to state-of-the-art methods. The code will be updated soon at https://github.com/TruongKhang/TopicFM

TopicFM: Robust and Interpretable Feature Matching with Topic-assisted

Finding correspondences across images is an important task in many visual applications. Recent state-of-the-art methods focus on end-to-end learning-based architectures designed in a coarse-to-fine manner. They use a very deep CNN or multi-block Transformer to learn robust representation, which requires high computation power. Moreover, these methods learn features without reasoning about objects, shapes inside images, thus lacks of interpretability. In this paper, we propose an architecture for image matching which is efficient, robust, and interpretable. More specifically, we introduce a novel feature matching module called TopicFM which can roughly organize same spatial structure across images into a topic and then augment the features inside each topic for accurate matching. To infer topics, we first learn global embedding of topics and then use a latent-variable model to detect-then-assign the image structures into topics. Our method can only perform matching in co-visibility regions to reduce computations. Extensive experiments in both outdoor and indoor datasets show that our method outperforms the recent methods in terms of matching performance and computational efficiency. The code is available at https://github.com/TruongKhang/TopicFM.

Curvature-guided dynamic scale networks for Multi-view Stereo

Multi-view stereo (MVS) is a crucial task for precise 3D reconstruction. Most recent studies tried to improve the performance of matching cost volume in MVS by designing aggregated 3D cost volumes and their regularization. This paper focuses on learning a robust feature extraction network to enhance the performance of matching costs without heavy computation in the other steps. In particular, we present a dynamic scale feature extraction network, namely, CDSFNet. It is composed of multiple novel convolution layers, each of which can select a proper patch scale for each pixel guided by the normal curvature of the image surface. As a result, CDFSNet can estimate the optimal patch scales to learn discriminative features for accurate matching computation between reference and source images. By combining the robust extracted features with an appropriate cost formulation strategy, our resulting MVS architecture can estimate depth maps more precisely. Extensive experiments showed that the proposed method outperforms other state-of-the-art methods on complex outdoor scenes. It significantly improves the completeness of reconstructed models. As a result, the method can process higher resolution inputs within faster run-time and lower memory than other MVS methods. Our source code is available at url{https://github.com/TruongKhang/cds-mvsnet}.