Jianmei Wang — accepted AI-conference papers

2026

LiteSense: Lifting Lightweight ToF with RGB for High-Resolution Metric Depth Estimation

Yusheng Li, Lizhi Lou, Yan Tang, Zekai Miao, Shaoming Zhang, Jianmei Wang

CVPR 2026

Metric depth estimation aims to recover depth maps with absolute scale, high resolution, and cross-scene consistency from visual observations. Existing approaches either rely on large-scale models or costly sensors to preserve metric accuracy and generalization, both ill-suited to resource-constrain

Cited by 0 Source ↗Scholar ↗

2023

Dynamic Object Classification of Low-Resolution Point Clouds: An LSTM-Based Ensemble Learning Approach

Shaoming Zhang, Tangjun Yao, Jianmei Wang, Tiantian Feng, Zhong Wang

RA-L 2023

In unmanned vehicle perception, dynamic object classification is applied to classify objects accurately and timely, providing decision-making for obstacle avoidance and planning. Low-resolution LiDAR is one of the most important sensors for this task. Unfortunately, the existing approaches perform u

Cited by 2 Source ↗Scholar ↗