22 accepted papers
Radiotherapy (RT) is a cornerstone of cancer treatment. Following RT, patient-reported outcomes (PROs) collected via standardized questionnaires are crucial for monitoring patients
Visual Active Tracking (VAT) aims to control cameras to follow a target in 3D space, which is critical for applications like drone navigation and security surveillance. However, it faces two key bottlenecks in real-world deployment: confusion from visually similar distractors caused by insufficient
Point cloud quality assessment (PCQA) is essential for reliable 3D visual applications. While point-based methods face challenges in characterizing distortions due to point cloud disorder, projection-based approaches offer better efficiency but suffer from geometric distortion insensitivity and text
Learning a human-like driving policy from large-scale driving demonstrations is promising, but the uncertainty and non-deterministic nature of planning make it challenging. Existing learning-based planning methods follow a deterministic paradigm to directly regress the action, failing to cope with t…
Inconsistency of distributions in human actions and camera viewpoints can lead to significant deviations when the pre-trained 3D pose estimators are tested on cross-datasets. In practical applications, the estimators usually follow the standard full fine-tuning paradigm on the target dataset, which…
Ensuring safe and socially compliant driving is essential for autonomous vehicle planning. However, one of the significant challenges remains the performance bottleneck caused by interaction uncertainty in complex traffic scenarios. Traditional planning algorithms typically account for all traffic p
Shape part segmentation is a critical task in computer graphics and robotics. However, traditional supervised methods rely heavily on large amounts of labeled data, which poses significant challenges in many real-world scenarios where such data is often scarce or difficult to obtain. To address this…
In autonomous driving, accurately identifying traffic participants that may influence vehicle behavior is crucial for effective system planning. To address this challenge, we propose a fault-tolerant mechanism for detecting action-induced objects, which significantly improves decision-making perform…
Existing end-to-end autonomous driving (AD) algorithms typically follow the Imitation Learning (IL) paradigm, which faces challenges such as causal confusion and an open-loop gap. In this work, we propose RAD, a 3DGS-based closed-loop Reinforcement Learning (RL) framework for end-to-end Autonomous D…
The research and development of dense crowd detection technology have always been one of the hot and challenging topics in the field of computer vision. DETR-like models have shown good performance in both training efficiency and inference capabilities. Nevertheless, as the optimization proceeds, th…
In offline reinforcement learning, the challenge of out-of-distribution (OOD) is pronounced. To address this, existing methods often constrain the learned policy through policy regularization. However, these methods often suffer from the issue of unnecessary conservativeness, hampering policy improv…
Predicting human motion requires addressing dependencies and errors for pose forecasting from sequences. The transformer’s self-attention aids this, but its complexity poses computational challenges. We present an efficient DeformMLP network without self-attention, using fully connected layers. Defo…
In video-based point cloud compression (V-PCC), point clouds are generated as videos via patch projection to be compressed using video coding techniques. However, a large number of filled empty pixels in the videos creates a fake context, which reduces the noise prediction accuracy in compression ar…
With the growing applicability of multi-view multi-person 3D human pose estimation across diverse scenarios, the impact of external environmental factors and occlusion on accuracy has garnered substantial attention. In this research, we introduce a novel approach to multi-view multi-person 3D human…
Physics-informed machine learning (PIML) has established itself as a new scientific paradigm which enables the seamless integration of observational data with partial differential equation (PDE) based physics models. A powerful tool for the analysis, reduction and solution of PDEs is the Lie symmetr…
Cross-modal optical flow estimation aims to predict motion fields between two frames collected from different modalities, recently attracting intensive attention. However, a substantial yet challenging problem is how to match images across a large modal discrepancy. In this paper, we propose a modal…
Recently, some studies have shown that semantic and distortion representations both benefit the evaluation of image quality. However, the images of existing synthetic distortion databases are annotated with subjective quality scores and distortion types, lacking labels with semantic objects. Therefo…
Scene flow estimation is critical for real-world vision problems such as autonomous driving and augmented reality. Due to the popularity of 3D LiDAR sensors, scene flow estimation from 3D point clouds arouses increasing attention. Existing methods usually use a flow embedding-based layer to find cor…
Spiking cameras have shown promising advantages for optical flow estimation in high-speed scenarios. The recent work SCFlow [1] attempts to train an optical flow model using spike frames based on a multi-scale flow reconstruction loss. However, only using the flow reconstruction loss is unable to ef…
The human vision system is highly adapted to extract structural information from the viewed scenes. The irregularity of point clouds makes the extraction of structural information containing both color and geometry an important challenge for point cloud quality assessment (PCQA). This paper proposes…
To satisfy the diversity of tactile patterns during Physical Human Robot Interaction(PHRI), this paper proposes a method to recognize human tactile motion using a spherical handle equipped with tactile sensors. The method first exploits convolutional neural networks as universal feature extractors,…