activity
20192026
most citedBEVFusion: A Simple and Robust LiDAR-Camera Fusion Framework

202 citations · 250 across the 11 of their papers we have counts for

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Showing cs.CVShow all

9 papers · 1 filter

cs.CV2026

PhyMo: Learning Physical Dynamics with Accurate and Continuous Motion from Multi-View Videos

Shangjia Liu, Jinxi Li, Siyuan Zhou +2

Recovering dynamic 3D scene geometry, appearance, and the physical motion states that govern scene evolution from multi-view videos is important yet challenging. A key difficulty i…

cs.CV2026

GPOcc++: Unified Sparse Gaussian Occupancy Prediction with Visual Geometry Priors

Changqing Zhou, Yueru Luo, Yulan Guo +3

Accurate 3D scene understanding is fundamental to embodied intelligence and autonomous driving, where 3D occupancy provides a unified representation of objects, structures, and fre…

cs.CV2023★ 1 cited

Drone-NeRF: Efficient NeRF Based 3D Scene Reconstruction for Large-Scale Drone Survey

Zhihao Jia, Bing Wang, Changhao Chen

Neural rendering has garnered substantial attention owing to its capacity for creating realistic 3D scenes. However, its applicability to extensive scenes remains challenging, with…

cs.CV2023★ 4 cited

Deep Learning for Visual Localization and Mapping: A Survey

Changhao Chen, Bing Wang, Chris Xiaoxuan Lu +2

Deep learning based localization and mapping approaches have recently emerged as a new research direction and receive significant attentions from both industry and academia. Instea…

cs.CV2022★ 26 cited

DM-NeRF: 3D Scene Geometry Decomposition and Manipulation from 2D Images

Bing Wang, Lu Chen, Bo Yang

In this paper, we study the problem of 3D scene geometry decomposition and manipulation from 2D views. By leveraging the recent implicit neural representation techniques, particula…

cs.CV2022★ 202 cited

BEVFusion: A Simple and Robust LiDAR-Camera Fusion Framework

Tingting Liang, Hongwei Xie, Kaicheng Yu +6

Fusing the camera and LiDAR information has become a de-facto standard for 3D object detection tasks. Current methods rely on point clouds from the LiDAR sensor as queries to lever…

cs.CV2022★ 9 cited

RangeUDF: Semantic Surface Reconstruction from 3D Point Clouds

Bing Wang, Zhengdi Yu, Bo Yang +5

We present RangeUDF, a new implicit representation based framework to recover the geometry and semantics of continuous 3D scene surfaces from point clouds. Unlike occupancy fields…

cs.CV2022★ 2 cited

No Pain, Big Gain: Classify Dynamic Point Cloud Sequences with Static Models by Fitting Feature-level Space-time Surfaces

Jia-Xing Zhong, Kaichen Zhou, Qingyong Hu +3

Scene flow is a powerful tool for capturing the motion field of 3D point clouds. However, it is difficult to directly apply flow-based models to dynamic point cloud classification…

cs.CV2019★ 2 cited

Learning Selective Sensor Fusion for States Estimation

Changhao Chen, Stefano Rosa, Chris Xiaoxuan Lu +3

Autonomous vehicles and mobile robotic systems are typically equipped with multiple sensors to provide redundancy. By integrating the observations from different sensors, these mob…