11 accepted papers
6D pose estimation is a key technology in computer vision and robotic manipulation. However, many methods remain heavily dependent on CAD models that are difficult to obtain. Object-level 3D reconstruction provides an alternative route, and 3D Gaussian Splatting (3DGS) shows convincing potential owi
In structured light systems, the accuracy of measurement notably diminishes when assessing complex texture objects, especially encountering boundaries between various colors. To address this challenge, this paper meticulously analyzes and establishes an error model, elaborating the correlation betwe…
The pretrain-finetune paradigm of pre-training a model on large amounts of image and text data and then fine-tuning the model for a specific task has led to significant progress in many 2D image and natural language processing tasks.Similarly, the use of pre-training methods in point cloud data can…
In structured light systems, measurement accuracy tends to decline significantly when evaluating complex textured surfaces, particularly at boundaries between different colors. To address this issue, this paper conducts a detailed analysis to develop an error model that illustrates the relationship…
Hybrid monocular visual odometry, which combines the advantages of learning-based and geometry-based methods, has gained widespread attention for its superior robustness and accuracy. Recent works use networks to predict optical flow to establish pixel correspondences between images while retaining
With the rapidly increasing demand for oriented object detection (OOD), recent research involving weakly-supervised detectors for learning OOD from point annotations has gained great attention. In this paper, we rethink this challenging task setting with the layout among instances and present Point2…
Object pose estimation in open-world scenarios is a critical challenge in robotics, virtual reality, and autonomous driving. In this letter, we introduce SamPose, a novel framework designed to achieve model-free 6DoF pose estimation of any target object in open-world environments using only a single
With the rapidly increasing demand for oriented object detection (OOD) recent research involving weakly-supervised detectors for learning rotated box (RBox) from the horizontal box (HBox) has attracted more and more attention. In this paper we explore a more challenging yet label-efficient setting n…
With the rapidly increasing demand for oriented object detection, e.g. in autonomous driving and remote sensing, the recently proposed paradigm involving weakly-supervised detector H2RBox for learning rotated box (RBox) from the more readily-available horizontal box (HBox) has shown promise. This pa…
With the vigorous development of computer vision, oriented object detection has gradually been featured. In this paper, a novel differentiable angle coder named phase-shifting coder (PSC) is proposed to accurately predict the orientation of objects, along with a dual-frequency version (PSCD). By map…
Point cloud completion aims at predicting dense complete 3D shapes from sparse incomplete point clouds captured from 3D sensors or scanners. It plays an essential role in various applications such as autonomous driving, 3D reconstruction, augmented reality, and robot navigation. Existing point cloud