Search Results for author:
Found 14 papers, 3 papers with code
GaussianGrasper: 3D Language Gaussian Splatting for Open-vocabulary Robotic Grasping
In particular, we propose an Efficient Feature Distillation (EFD) module that employs contrastive learning to efficiently and accurately distill language embeddings derived from foundational models.
Analyzing Emissions and Energy Efficiency at Unsignalized Real-world Intersections Under Mixed Traffic Control
With at least 30% RVs, CO and HC emissions are reduced by up to 42% and 43%, respectively.
Model2Scene: Learning 3D Scene Representation via Contrastive Language-CAD Models Pre-training
In this paper, we propose Model2Scene, a novel paradigm that learns free 3D scene representation from Computer-Aided Design (CAD) models and languages.
Learning to Control and Coordinate Mixed Traffic Through Robot Vehicles at Complex and Unsignalized Intersections
In contrast, without RVs, congestion starts to develop when the traffic demand reaches as low as 200 vehicles per hour.
Deep Anomaly Detection and Search via Reinforcement Learning
Semi-supervised Anomaly Detection (AD) is a kind of data mining task which aims at learning features from partially-labeled datasets to help detect outliers.
CD and PMD Effect on Cyclostationarity-Based Timing Recovery for Optical Coherent Receivers
Timing recovery is critical for synchronizing the clocks at the transmitting and receiving ends of a digital coherent communication system.
A Concept and Argumentation based Interpretable Model in High Risk Domains
Interpretability has become an essential topic for artificial intelligence in some high-risk domains such as healthcare, bank and security.
Towards 3D Scene Understanding by Referring Synthetic Models
Promising performance has been achieved for visual perception on the point cloud.
An Intelligent Self-driving Truck System For Highway Transportation
Recently, there have been many advances in autonomous driving society, attracting a lot of attention from academia and industry.
Referring Self-supervised Learning on 3D Point Cloud
In this paper, we study a new problem named Referring Self-supervised Learning (RSL) on 3D scene understanding: Given the 3D synthetic models with labels and the unlabeled 3D real scene scans, our goal is to distinguish the identical semantic objects on an unseen scene according to the referring synthetic 3D models.
Pixel Codec Avatars
Telecommunication with photorealistic avatars in virtual or augmented reality is a promising path for achieving authentic face-to-face communication in 3D over remote physical distances.
F-CAD: A Framework to Explore Hardware Accelerators for Codec Avatar Decoding
Creating virtual avatars with realistic rendering is one of the most essential and challenging tasks to provide highly immersive virtual reality (VR) experiences.
A Collaborative Visual SLAM Framework for Service Robots
We present a collaborative visual simultaneous localization and mapping (SLAM) framework for service robots.
Comparison of Machine Learning Methods for Predicting Karst Spring Discharge in North China
Simulations showed that MLP reduced MSE, MAE, and RMSE to 0. 0010, 0. 0254, and 0. 0318, respectively.
