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20242026
most citedSmall, Versatile and Mighty: A Range-View Perception Framework

1 citations · 2 across the 5 of their papers we have counts for

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cs.CV2026

OPUS-V2: Bridging the Gap between Sparse Points and Dense Voxels

Jiabao Wang, Qiang Meng, Liujiang Yan +3

The point-based occupancy prediction paradigm has achieved an attractive trade-off between accuracy and efficiency by modeling 3D space sparsely. However, its predictions inherentl…

cs.CV2026

GEM: Generating LiDAR World Model via Deformable Mamba

Yang Wu, Zhaojiang Liu, Qiang Meng +5

World models, which simulate environmental dynamics and generate sensor observations, are gaining increasing attention in autonomous driving. However, progress in LiDAR-based world…

cs.CV2025

COME: Adding Scene-Centric Forecasting Control to Occupancy World Model

Yining Shi, Kun Jiang, Qiang Meng +6

World models are critical for autonomous driving to simulate environmental dynamics and generate synthetic data. Existing methods struggle to disentangle ego-vehicle motion (perspe…

cs.CV20241 cited

OPUS: Occupancy Prediction Using a Sparse Set

Jiabao Wang, Zhaojiang Liu, Qiang Meng +6

Occupancy prediction, aiming at predicting the occupancy status within voxelized 3D environment, is quickly gaining momentum within the autonomous driving community. Mainstream occ…

cs.CV2024

Towards Stable 3D Object Detection

Jiabao Wang, Qiang Meng, Guochao Liu +4

In autonomous driving, the temporal stability of 3D object detection greatly impacts the driving safety. However, the detection stability cannot be accessed by existing metrics suc…

cs.CV20241 cited

Small, Versatile and Mighty: A Range-View Perception Framework

Qiang Meng, Xiao Wang, JiaBao Wang +2

Despite its compactness and information integrity, the range view representation of LiDAR data rarely occurs as the first choice for 3D perception tasks. In this work, we further p…