Robust and efficient local feature matching plays a crucial role in applications such as SLAM and visual localization for robotics. Despite great progress, it is still very challenging to extract robust and discriminative visual features in scenarios with drastic lighting changes, low texture areas, or repetitive patterns. In this paper, we propose a new lightweight network called LiftFeat, which lifts the robustness of raw descriptor by aggregating 3D geometric feature. Specifically, we first adopt a pre-trained monocular depth estimation model to generate pseudo surface normal label, supervising the extraction of 3D geometric feature in terms of predicted surface normal. We then design a 3D geometry-aware feature lifting module to fuse surface normal feature with raw 2D descriptor feature. Integrating such 3D geometric feature enhances the discriminative ability of 2D feature description in extreme conditions. Extensive experimental results on relative pose estimation, homography estimation, and visual localization tasks, demonstrate that our LiftFeat outperforms some lightweight state-of-the-art methods. Code will be released at: https://github.com/lyp-deeplearning/LiftFeat.

Blind Image Quality Assessment (BIQA) methods refer to algorithms that predict image quality scores without reference images. This challenging area is crucial for preprocessing and optimizing visual tasks. Since BIQA is a small-sample task, it has become a common practice to apply transfer learning by using models pre-trained on other visual tasks, such as semantic recognition, to BIQA. This paper is interested in whether a backbone that performs better in semantic recognition also improves predictive accuracy in BIQA tasks. Comparative experiments showed that different semantic backbones with varying precision exhibit minimal differences in PLCC (Pearson Linear Correlation Coefficient) and SRCC (Spearman Rank Order Correlation Coefficient). However, further comparison shows that the semantic backbone does enhance the model’s IQA abilities, which traditional metrics fail to capture due to their oversight of quality differences between images. We term this ability as Image Quality Difference Perception Ability (IQDP Ability). Based on this, we propose an IQDP comparison method and a new metric, which can effectively compare and measure a model’s IQDP Ability, supplementing traditional metrics and providing an effective means of identifying superior models. • Semantic backbones offer insignificant PLCC/SRCC improvement in IQA performance. • IQDP highlights ‘Image Quality Difference Perception Ability’ that PLCC/SRCC miss. • A new metric describes the quality difference perception ability of BIQA models.

Annotating 3D medical images demands expert knowledge and is time-consuming. As a result, semi-supervised learning (SSL) approaches have gained significant interest in 3D medical image segmentation. The significant size differences among various organs in the human body lead to imbalanced class distribution, which is a major challenge in the real-world application of these SSL approaches. To address this issue, we develop a novel Shape Transformation driven by Active Contour (STAC), that enlarges smaller organs to alleviate imbalanced class distribution across different organs. Inspired by curve evolution theory in active contour methods, STAC employs a signed distance function (SDF) as the level set function, to implicitly represent the shape of organs, and deforms voxels in the direction of the steepest descent of SDF (i.e., the normal vector). To ensure that the voxels far from expansion organs remain unchanged, we design an SDF-based weight function to control the degree of deformation for each voxel. We then use STAC as a data-augmentation process during the training stage. Experimental results on two benchmark datasets demonstrate that the proposed method significantly outperforms some state-of-the-art methods. Source code is publicly available at https://github.com/GuGuLL123/STAC.

Blind Image Quality Assessment (BIQA) is a challenging research topic that is critical for preprocessing and optimizing downstream vision tasks such as semantic recognition and image restoration. However, there has been a significant disconnect between BIQA research and other vision tasks. The primary cause of such disconnect is the incompatibility of existing BIQA models with other vision tasks, resulting in significant computational complexity. To address this issue, we propose a model-agnostic semantic-quality compatible framework that can simultaneously generate quality and semantic predictions. By incorporating a lightweight learning architecture, we demonstrate that a parameter-fixed semantic-oriented backbone can predict the perceptual quality of images as accurately as models trained end-to-end. We systematically study the major components of our framework, and our experimental results demonstrate the superiority of our model in terms of both complexity and accuracy. The source code of this work is available at https://github.com/MaxiaoyuHehe/SQCFNet.