20 accepted papers
Graph-based multi-view clustering, with its ability to mine potential associations between samples, has attracted extensive attention. To capture high-order correlations, tensor-based frameworks have been introduced to model multiple graphs jointly. Although these methods have achieved promising per
Multi-view multi-label classification (MVMLC) aims to utilize both consensus and complementarity information to predict potentially relevant labels for samples. Existing MVMLC approaches typically focus on either feature-level fusion, which integrates complementary features for more expressive repre
Underwater environments impose severe challenges to visual-inertial odometry systems, as strong light attenuation, marine snow and turbidity, together with weakly exciting motions, degrade inertial observability and cause frequent tracking failures over long-term operation. While tightly coupled aco
Physics obeys strict symmetries like rotational equivariance. However, the standard Transformer architectures widely used in physics foundation models do not enforce these constraints by construction. We introduce ReViT, a rotationally equivariant Vision Transformer framework for neural PDE solvers …
Recently, large language models (LLMs) have been explored widely for 3D scene understanding. Among them, training-free approaches are gaining attention for their flexibility and generalization over training-based methods. However, they typically struggle with accuracy and efficiency in practical dep
Recently, there has been growing interest in using low-cost sensor combinations, such as cameras and IMUs, to achieve accurate localization within pre-built pointcloud maps. In this paper, we propose a novel hybrid visual-Lidar mapping and visual-only re-localization framework, specifically designed
Graph contrastive learning (GCL) aims to learn self-supervised representations by distinguishing positive and negative sample pairs generated from multiple augmented graph views. Despite showing promising performance, GCL still suffers from two critical biases: (1) ***Similarity estimation bias*** a…
Most existing visual-inertial odometry (VIO) initialization methods rely on accurate pre-calibrated extrinsic parameters. However, during long-term use, irreversible structural deformation caused by temperature changes, mechanical squeezing, etc. will cause changes in extrinsic parameters, especiall
Point cloud segmentation has a wide range of applications in autonomous driving, augmented reality and virtual reality. Multi-modal fusion strategies have received increasing attention in point cloud segmentation recently. Despite the success, existing methods usually generate unnecessary informatio…
Floorplan reconstruction provides structural priors essential for reliable indoor robot navigation and high-level scene understanding. However, existing approaches either require time-consuming offline processing with a complete map, or rely on expensive sensors and substantial computational resourc
Single-view 3D reconstruction is a fundamental problem in computer vision, having a significant impact on downstream tasks such as autonomous driving, virtual reality and augmented reality. However, existing single-view reconstruction methods are unable to reconstruct the regions outside the input f…
When simulating partial differential equations, hybrid solvers combine coarse numerical solvers with learned correctors. They promise accelerated simulations while adhering to physical constraints. However, as shown in our theoretical framework, directly applying learned corrections to solver output…
Existing multi-session visual simultaneous localization and mapping (SLAM) systems struggle severely to achieve robust localization and map merging under extreme viewpoint and illumination variations, particularly when handling completely opposite viewpoints and drastic day-night lighting changes. T
In multi-view multi-label classification (MVML), each object is described by several heterogeneous views while annotated with multiple related labels. The key to learn from such complicate data lies in how to fuse cross-view features and explore multi-label correlations, while accordingly obtain cor…
Planar structures, prevalent in man-made environments, can be observed by a camera for significant periods of time due to their large spatial presence. These structures provide strong planar regularities for Simultaneous Localization and Mapping (SLAM) systems, facilitating long-term navigation. The
Semi-supervised learning (SSL) has recently demonstrated great success in medical image segmentation, significantly enhancing data efficiency with limited annotations. However, despite its empirical benefits, there are still concerns in the literature about the theoretical foundation and explanation…
Point and line features are complementary in Visual-Inertial Odometry (VIO) or Visual-Inertial Simultaneous Localization And Mapping (VI-SLAM) systems. The advantage of combining these two types of features relies on their proper weighting in the cost function, usually set by their uncertainty. Comp…
Man-made buildings exhibit structural regularity, which can provide strongly geometrical constraints for Visual-Inertial Odometry (VIO) systems. To make full use of the structural information, we propose a new structural regularity aided VIO with novel coordinate alignment and line triangulation und
Weak texture and motion blur are always challenging problems for visual-inertial odometry (VIO) systems. To improve accuracy of VIO systems in the challenging scenes, we propose a point-line-based VIO system with novel feature hybrids and with novel predicting-matching for long line track. Point-lin
Line segment features become popular in SLAM community. Usually, line-based SLAM systems utilize local appearance descriptors for line segment tracking. However, traditional descriptor-based line segment tracking algorithms suffer from the problem that accuracy and speed cannot be possessed simultan…