11 accepted papers
Reinforcement learning (RL) has demonstrated remarkable success in text and image generation, yet its potential in 3D generation remains largely unexplored. Existing attempts typically rely on offline direct preference optimization (DPO) method, which suffers from low training efficiency and limited
The generation of quadrilateral-dominant meshes is a cornerstone of professional 3D content creation. However, existing generative models generate quad meshes by first generating triangle meshes and then merging triangles into quadrilaterals with some specific rules, which typically produces quad m…
Formulating a multi-robot obstacle avoidance policy is essential for enabling safe and efficient navigation in multi-robot environments, forming a critical component of the effective operation of multi-robot systems. Recently, reinforcement learning has been applied to improve the performance of dec
Panoptic 3D reconstruction from a monocular video is a fundamental perceptual task in robotic scene understanding. However, existing efforts suffer from inefficiency in terms of inference speed and accuracy, limiting their practical applicability. We present EPRecon, an efficient real-time panoptic
Existing language instruction-guided online 3D reconstruction systems mainly rely on explicit instructions or queryable maps, showing inadequate capability to handle implicit and complex instructions. In this paper, we first introduce a reasoning reconstruction task. This task inputs an implicit ins…
Homography estimation is fundamental to various vision tasks. Iteration-based methods have recently achieved significant success in this field. However, errors introduced during iterations can lead to increased image deformation. Existing methods often focus on capturing local correspondences in the…
Navigating robots through dynamic multi-robot environments, avoiding collisions with both other robots and obstacles, has emerged as a central challenge in robotics. The existing approaches fall short in allowing the policy network to effectively capture spatial-temporal reciprocal collision avoidan…
Generalizing visual reinforcement learning is fundamental to robot visual navigation, involving the acquisition of a policy from interactions with source environments to facilitate adaptation to analogous, yet unfamiliar target environments. Recent advancements capitalize on data augmentation techni…
Lane keeping, as a fundamental functionality of autonomous navigation, remains a challenging task for autonomous robots and vehicles. Recently, spiking neural networks (SNNs) have gained attention and research interest due to their biological plausibility and application potential on neuromorphic pr…
Accurate microcalcification (mC) detection is of great importance due to its high proportion in early breast cancers. Most of the previous mC detection methods belong to discriminative models, where classifiers are exploited to distinguish mCs from other backgrounds. However, it is still challenging…
Surveillance cameras have been widely used in different scenes. Accordingly, a demanding need is to recognize a person under different cameras, which is called person re-identification. This topic has gained increasing interests in computer vision recently. However, less attention has been paid to v…