9 citations · 22 across the 12 of their papers we have counts for
13 papers
GARD-GS: Geometry-Guided Anchor-Regularized Gaussian Splatting Distillation
Puyuan Zhang, Jianming Huang, Wenkai Ye +1
Dense colored LiDAR maps provide accurate city-scale geometry, but lifting them into 3D Gaussian Splatting (3DGS) retains millions of primitives, making the resulting models costly…
A Real-Time Multi-Model Parametric Representation of Point Clouds
Yuan Gao, Wei Dong
In recent years, parametric representations of point clouds have been widely applied in tasks such as memory-efficient mapping and multi-robot collaboration. Highly adaptive models…
BEVRender: Vision-based Cross-view Vehicle Registration in Off-road GNSS-denied Environment
Lihong Jin, Wei Dong, Wenshan Wang +1
We introduce BEVRender, a novel learning based approach for the localization of ground vehicles in Global Navigation Satellite System(GNSS)-denied off-road scenarios. These environ…
Data-Centric Evolution in Autonomous Driving: A Comprehensive Survey of Big Data System, Data Mining, and Closed-Loop Technologies
Lincan Li, Wei Shao, Wei Dong +4
The aspiration of the next generation's autonomous driving (AD) technology relies on the dedicated integration and interaction among intelligent perception, prediction, planning, a…
Fast Monocular Scene Reconstruction with Global-Sparse Local-Dense Grids
Wei Dong, Chris Choy, Charles Loop +3
Indoor scene reconstruction from monocular images has long been sought after by augmented reality and robotics developers. Recent advances in neural field representations and monoc…
Revisiting LiDAR Registration and Reconstruction: A Range Image Perspective
Wei Dong, Kwonyoung Ryu, Michael Kaess +1
Spinning LiDAR data are prevalent for 3D vision tasks. Since LiDAR data is presented in the form of point clouds, expensive 3D operations are usually required. This paper revisits…
ASH: A Modern Framework for Parallel Spatial Hashing in 3D Perception
Wei Dong, Yixing Lao, Michael Kaess +1
We present ASH, a modern and high-performance framework for parallel spatial hashing on GPU. Compared to existing GPU hash map implementations, ASH achieves higher performance, sup…
Self-supervised Geometric Perception
Heng Yang, Wei Dong, Luca Carlone +1
We present self-supervised geometric perception (SGP), the first general framework to learn a feature descriptor for correspondence matching without any ground-truth geometric mode…
Compositional Scalable Object SLAM
Akash Sharma, Wei Dong, Michael Kaess
We present a fast, scalable, and accurate Simultaneous Localization and Mapping (SLAM) system that represents indoor scenes as a graph of objects. Leveraging the observation that a…
Deep Global Registration
Christopher Choy, Wei Dong, Vladlen Koltun
We present Deep Global Registration, a differentiable framework for pairwise registration of real-world 3D scans. Deep global registration is based on three modules: a 6-dimensiona…
Guided Feature Selection for Deep Visual Odometry
Fei Xue, Qiuyuan Wang, Xin Wang +3
We present a novel end-to-end visual odometry architecture with guided feature selection based on deep convolutional recurrent neural networks. Different from current monocular vis…
PSDF Fusion: Probabilistic Signed Distance Function for On-the-fly 3D Data Fusion and Scene Reconstruction
Wei Dong, Qiuyuan Wang, Xin Wang +1
We propose a novel 3D spatial representation for data fusion and scene reconstruction. Probabilistic Signed Distance Function (Probabilistic SDF, PSDF) is proposed to depict uncert…
An Efficient Volumetric Mesh Representation for Real-time Scene Reconstruction using Spatial Hashing
Wei Dong, Jieqi Shi, Weijie Tang +2
Mesh plays an indispensable role in dense real-time reconstruction essential in robotics. Efforts have been made to maintain flexible data structures for 3D data fusion, yet an eff…