11 citations · 29 across the 42 of their papers we have counts for
15 papers · 1 filter
SoPE: Spherical Coordinate-Based Positional Embedding for Enhancing Spatial Perception of 3D LVLMs
Guanting Ye, Qiyan Zhao, Wenhao Yu +7
3D Large Vision-Language Models (3D LVLMs) built upon Large Language Models (LLMs) have achieved remarkable progress across various multimodal tasks. However, their inherited posit…
C^2ROPE: Causal Continuous Rotary Positional Encoding for 3D Large Multimodal-Models Reasoning
Guanting Ye, Qiyan Zhao, Wenhao Yu +4
Recent advances in 3D Large Multimodal Models (LMMs) built on Large Language Models (LLMs) have established the alignment of 3D visual features with LLM representations as the domi…
ElectricSight: 3D Hazard Monitoring for Power Lines Using Low-Cost Sensors
Xingchen Li, LiDian Wang, Yu Sheng +6
Protecting power transmission lines from potential hazards involves critical tasks, one of which is the accurate measurement of distances between power lines and potential threats,…
SpatialSplat: Efficient Semantic 3D from Sparse Unposed Images
Yu Sheng, Jiajun Deng, Xinran Zhang +4
A major breakthrough in 3D reconstruction is the feedforward paradigm to generate pixel-wise 3D points or Gaussian primitives from sparse, unposed images. To further incorporate se…
Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving
Shumin Wang, Zhuoran Yang, Lidian Wang +7
The significant achievements of pre-trained models leveraging large volumes of data in the field of NLP and 2D vision inspire us to explore the potential of extensive data pre-trai…
OG-Gaussian: Occupancy Based Street Gaussians for Autonomous Driving
Yedong Shen, Xinran Zhang, Yifan Duan +5
Accurate and realistic 3D scene reconstruction enables the lifelike creation of autonomous driving simulation environments. With advancements in 3D Gaussian Splatting (3DGS), previ…
Map++: Towards User-Participatory Visual SLAM Systems with Efficient Map Expansion and Sharing
Xinran Zhang, Hanqi Zhu, Yifan Duan +5
Constructing precise 3D maps is crucial for the development of future map-based systems such as self-driving and navigation. However, generating these maps in complex environments,…
LFP: Efficient and Accurate End-to-End Lane-Level Planning via Camera-LiDAR Fusion
Guoliang You, Xiaomeng Chu, Yifan Duan +4
Multi-modal systems enhance performance in autonomous driving but face inefficiencies due to indiscriminate processing within each modality. Additionally, the independent feature l…
Perception Helps Planning: Facilitating Multi-Stage Lane-Level Integration via Double-Edge Structures
Guoliang You, Xiaomeng Chu, Yifan Duan +6
When planning for autonomous driving, it is crucial to consider essential traffic elements such as lanes, intersections, traffic regulations, and dynamic agents. However, they are…
CORP: A Multi-Modal Dataset for Campus-Oriented Roadside Perception Tasks
Beibei Wang, Zijian Yu, Lu Zhang +8
Numerous roadside perception datasets have been introduced to propel advancements in autonomous driving and intelligent transportation systems research and development. However, it…
CalibFormer: A Transformer-based Automatic LiDAR-Camera Calibration Network
Yuxuan Xiao, Yao Li, Chengzhen Meng +3
The fusion of LiDARs and cameras has been increasingly adopted in autonomous driving for perception tasks. The performance of such fusion-based algorithms largely depends on the ac…
EdgeCalib: Multi-Frame Weighted Edge Features for Automatic Targetless LiDAR-Camera Calibration
Xingchen Li, Yifan Duan, Beibei Wang +5
In multimodal perception systems, achieving precise extrinsic calibration between LiDAR and camera is of critical importance. Previous calibration methods often required specific t…
Bi-LRFusion: Bi-Directional LiDAR-Radar Fusion for 3D Dynamic Object Detection
Yingjie Wang, Jiajun Deng, Yao Li +6
LiDAR and Radar are two complementary sensing approaches in that LiDAR specializes in capturing an object's 3D shape while Radar provides longer detection ranges as well as velocit…
: Transferring Visual Representations for Reinforcement Learning via Prompting
Guoliang You, Xiaomeng Chu, Yifan Duan +4
It is important for deep reinforcement learning (DRL) algorithms to transfer their learned policies to new environments that have different visual inputs. In this paper, we introdu…
OA-DET3D: Embedding Object Awareness as a General Plug-in for Multi-Camera 3D Object Detection
Xiaomeng Chu, Jiajun Deng, Jianmin Ji +3
The recent advance in multi-camera 3D object detection is featured by bird's-eye view (BEV) representation or object queries. However, the ill-posed transformation from image-plane…