12 accepted papers
Point cloud classification, crucial for discriminative 3D shape analysis, has witnessed significant progress through the application of deep learning. A significant research focus has been on aggregating local point cloud features. A key limitation of previous methods lies in their inherent opacity,…
We propose a 3D Gaussian splatting-based framework for outdoor relighting that leverages intrinsic image decomposition to precisely integrate sunlight, sky radiance, and indirect lighting from unconstrained photo collections. Unlike prior methods that compress the per-image global illumination into…
Mixed autonomy traffic systems face significant security challenges when malicious agents disrupt coordination between autonomous and human-driven vehicles. We present Malicious Agent Detection and Isolation (MADI), a framework addressing two critical forms of disruptive behavior: path order violati
Neural Radiance Fields (NeRF) have shown significant potential in encoding complex 3D geometries and visual properties, making them a promising alternative to traditional dense SLAM systems. We propose Proud-SLAM, a dense SLAM framework that merges a neural point-based hybrid scene representation. O…
In this letter, we address the critical security concerns in multi-agent systems, where illegal infiltration is commonly used to convert agents into malicious entities. Existing research predominantly focuses on explicit malicious attack patterns. Our work introduces a covert deception attack framew
Accurate pose estimation over SE(3) is fundamentally crucial for numerous perception tasks, including camera re-localization. While existing learning-based methods estimated from a series of RGB images have significantly improved the accuracy of pose, the majority of models still face one or two lim…
The accurate segmentation of the right and left ventricles with limited labeled data is a challenging task in echocardiographic data analysis. To fully leverage the easily accessible unlabeled data, we propose a label-decoupling teacher-student framework (LDTSF) based on semi-supervised learning. Sp…
In this paper, we study the problem of jointly estimating the optical flow and scene flow from synchronized 2D and 3D data. Previous methods either employ a complex pipeline that splits the joint task into independent stages, or fuse 2D and 3D information in an "early-fusion" or "late-fusion" manner…
Accurate absolute pose regression is one of the key challenges in robotics and computer vision. Existing direct regression methods suffer from two limitations. First, some noisy scenarios such as poor illumination conditions are likely to result in the uncertainty of pose estimation. Second, the out…
Fast and accurate pose estimation is essential for many robotic applications such as SLAM, manipulation, and 3D point registration. Existing solutions to this problem suffer from either high computation overhead due to the nonlinear features or accuracy loss due to linear approximation. In this pape…
Multi-robot cooperation requires agents to make decisions that are consistent with the shared goal without disregarding action-specific preferences that might arise from asymmetry in capabilities and individual objectives. To accomplish this goal, we propose a method named SLiCC: Stackelberg Learnin…
Self-driving vehicles are being increasingly deployed in the wild. One of the most important next hurdles for autonomous driving is how such vehicles will optimally interact with one another and with their surroundings. In this paper, we consider the lane changing problem that is fundamental to road…