Search Results for author:
Found 20 papers, 5 papers with code
VINGS-Mono: Visual-Inertial Gaussian Splatting Monocular SLAM in Large Scenes
The framework comprises four main components: VIO Front End, 2D Gaussian Map, NVS Loop Closure, and Dynamic Eraser.
SparseTem: Boosting the Efficiency of CNN-Based Video Encoders by Exploiting Temporal Continuity
To address these issues, we propose a memory-efficient scheduling method to eliminate memory overhead and an online adjustment mechanism to minimize accuracy degradation.
Inf-MLLM: Efficient Streaming Inference of Multimodal Large Language Models on a Single GPU
In this paper, we introduce Inf-MLLM, an efficient inference framework for MLLMs, which enable streaming inference of MLLM on a single GPU with infinite context.
OccLLaMA: An Occupancy-Language-Action Generative World Model for Autonomous Driving
Then, we build a unified multi-modal vocabulary for vision, language and action.
AutoVCoder: A Systematic Framework for Automated Verilog Code Generation using LLMs
Recently, the use of large language models (LLMs) for software code generation, e. g., C/C++ and Python, has proven a great success.
O2V-Mapping: Online Open-Vocabulary Mapping with Neural Implicit Representation
Online construction of open-ended language scenes is crucial for robotic applications, where open-vocabulary interactive scene understanding is required.
HGS-Mapping: Online Dense Mapping Using Hybrid Gaussian Representation in Urban Scenes
However, integrating 3DGS into a street-view dense mapping framework still faces two challenges, including incomplete reconstruction due to the absence of geometric information beyond the LiDAR coverage area and extensive computation for reconstruction in large urban scenes.
DeepPointMap: Advancing LiDAR SLAM with Unified Neural Descriptors
Point clouds have shown significant potential in various domains, including Simultaneous Localization and Mapping (SLAM).
OpenAnnotate3D: Open-Vocabulary Auto-Labeling System for Multi-modal 3D Data
In the era of big data and large models, automatic annotating functions for multi-modal data are of great significance for real-world AI-driven applications, such as autonomous driving and embodied AI.
MARS: Exploiting Multi-Level Parallelism for DNN Workloads on Adaptive Multi-Accelerator Systems
Along with the fast evolution of deep neural networks, the hardware system is also developing rapidly.
Fast Segment Anything
In this paper, we propose a speed-up alternative method for this fundamental task with comparable performance.
Ranked #4 on
Zero-Shot Instance Segmentation
on LVIS v1.0 val
FlowMap: Path Generation for Automated Vehicles in Open Space Using Traffic Flow
However, how to cognize the ``road'' for automated vehicles where there is no well-defined ``roads'' remains an open problem.
Learning to Predict Vehicle Trajectories with Model-based Planning
Predicting the future trajectories of on-road vehicles is critical for autonomous driving.
FP-Stereo: Hardware-Efficient Stereo Vision for Embedded Applications
To reduce the design effort and achieve the right balance, we propose FP-Stereo for building high-performance stereo matching pipelines on FPGAs automatically.
PiP: Planning-informed Trajectory Prediction for Autonomous Driving
Moreover, our approach enables a novel pipeline which couples the prediction and planning, by conditioning PiP on multiple candidate trajectories of the ego vehicle, which is highly beneficial for autonomous driving in interactive scenarios.
Efficient Uncertainty-aware Decision-making for Automated Driving Using Guided Branching
Decision-making in dense traffic scenarios is challenging for automated vehicles (AVs) due to potentially stochastic behaviors of other traffic participants and perception uncertainties (e. g., tracking noise and prediction errors, etc.).
Robotics
Safe Trajectory Generation for Complex Urban Environments Using Spatio-temporal Semantic Corridor
Planning safe trajectories for autonomous vehicles in complex urban environments is challenging since there are numerous semantic elements (such as dynamic agents, traffic lights and speed limits) to consider.
An Efficient B-spline-Based Kinodynamic Replanning Framework for Quadrotors
Our framework starts with an efficient B-spline-based kinodynamic (EBK) search algorithm which finds a feasible trajectory with minimum control effort and time.
Predicting Vehicle Behaviors Over An Extended Horizon Using Behavior Interaction Network
In this paper, we uncover that clues to vehicle behaviors over an extended horizon can be found in vehicle interaction, which makes it possible to anticipate the likelihood of a certain behavior, even in the absence of any clear maneuver pattern.
Online Vehicle Trajectory Prediction using Policy Anticipation Network and Optimization-based Context Reasoning
In this paper, we present an online two-level vehicle trajectory prediction framework for urban autonomous driving where there are complex contextual factors, such as lane geometries, road constructions, traffic regulations and moving agents.
