1 citations · 1 across the 10 of their papers we have counts for
11 papers
ProjFormer: Point Cloud Completion via Geometric-Projective Transformer and Cross-Modal Semantic Constraints
Sheng Liu, Meng Wang, Ruihui Li +3
Point cloud completion is inherently ill-posed due to severe sparsity and ambiguity in partial observations. Existing multi-view methods alleviate this by incorporating 2D semantic…
Geospatial-Prior Guidance for 3D Semantic Scene Completion
Meng Wang, Shougao Zhang, Wenzhe He +4
Inferring complete 3D geometry and semantics from onboard images remains challenging because occlusions and restricted fields of view leave large scene regions underconstrained. Al…
MCI-SQL: Text-to-SQL with Metadata-Complete Context and Intermediate Correction
Qin Wang, Youhuan Li, Suixi Lin +5
Text-to-SQL aims to translate natural language queries into SQL statements. Existing methods typically follow a pipeline of pre-processing, schema linking, candidate SQL generation…
All Cities are Equal: A Unified Human Mobility Generation Model Enabled by LLMs
Bo Liu, Tong Li, Zhu Xiao +4
Synthetic human mobility generation is gaining traction as an ethical and practical approach to supporting the data needs of intelligent urban systems. Existing methods perform wel…
LiNeXt: Revisiting LiDAR Completion with Efficient Non-Diffusion Architectures
Wenzhe He, Xiaojun Chen, Ruiqi Wang +5
3D LiDAR scene completion from point clouds is a fundamental component of perception systems in autonomous vehicles. Previous methods have predominantly employed diffusion models f…
VLScene: Vision-Language Guidance Distillation for Camera-Based 3D Semantic Scene Completion
Meng Wang, Huilong Pi, Ruihui Li +3
Camera-based 3D semantic scene completion (SSC) provides dense geometric and semantic perception for autonomous driving. However, images provide limited information making the mode…
Vision-based 3D Semantic Scene Completion via Capture Dynamic Representations
Meng Wang, Fan Wu, Yunchuan Qin +3
The vision-based semantic scene completion task aims to predict dense geometric and semantic 3D scene representations from 2D images. However, the presence of dynamic objects in th…
Learning Temporal 3D Semantic Scene Completion via Optical Flow Guidance
Meng Wang, Fan Wu, Ruihui Li +3
3D Semantic Scene Completion (SSC) provides comprehensive scene geometry and semantics for autonomous driving perception, which is crucial for enabling accurate and reliable decisi…
PAR: Prompt-Aware Token Reduction Method for Efficient Large Multimodal Models
Yingen Liu, Fan Wu, Ruihui Li +2
Multimodal large language models (MLLMs) demonstrate strong performance across visual tasks, but their efficiency is hindered by significant computational and memory demands from p…
eCIL-MU: Embedding based Class Incremental Learning and Machine Unlearning
Zhiwei Zuo, Zhuo Tang, Bin Wang +2
New categories may be introduced over time, or existing categories may need to be reclassified. Class incremental learning (CIL) is employed for the gradual acquisition of knowledg…
Machine unlearning through fine-grained model parameters perturbation
Zhiwei Zuo, Zhuo Tang, Kenli Li +1
Machine unlearning techniques, which involve retracting data records and reducing influence of said data on trained models, help with the user privacy protection objective but incu…