Search Results for author: Linqing Zhao

Found 6 papers, 4 papers with code

Memory-based Adapters for Online 3D Scene Perception

no code implementations11 Mar 2024 Xiuwei Xu, Chong Xia, Ziwei Wang, Linqing Zhao, Yueqi Duan, Jie zhou, Jiwen Lu

To this end, we propose an adapter-based plug-and-play module for the backbone of 3D scene perception model, which constructs memory to cache and aggregate the extracted RGB-D features to empower offline models with temporal learning ability.

Anyview: Generalizable Indoor 3D Object Detection with Variable Frames

no code implementations9 Oct 2023 Zhenyu Wu, Xiuwei Xu, Ziwei Wang, Chong Xia, Linqing Zhao, Jiwen Lu, Haibin Yan

Existing methods only consider fixed frames of input data for a single detector, such as monocular RGB-D images or point clouds reconstructed from dense multi-view RGB-D images.

3D Object Detection Object +2

Dense Hybrid Proposal Modulation for Lane Detection

1 code implementation28 Apr 2023 Yuejian Wu, Linqing Zhao, Jiwen Lu, Haibin Yan

In addition to the shape and location constraints, we design a quality-aware classification loss to adaptively supervise each positive proposal so that the discriminative power can be further boosted.

Lane Detection

SurroundOcc: Multi-Camera 3D Occupancy Prediction for Autonomous Driving

2 code implementations ICCV 2023 Yi Wei, Linqing Zhao, Wenzhao Zheng, Zheng Zhu, Jie zhou, Jiwen Lu

Towards a more comprehensive perception of a 3D scene, in this paper, we propose a SurroundOcc method to predict the 3D occupancy with multi-camera images.

3D Object Detection Autonomous Driving +2

SurroundDepth: Entangling Surrounding Views for Self-Supervised Multi-Camera Depth Estimation

1 code implementation7 Apr 2022 Yi Wei, Linqing Zhao, Wenzhao Zheng, Zheng Zhu, Yongming Rao, Guan Huang, Jiwen Lu, Jie zhou

In this paper, we propose a SurroundDepth method to incorporate the information from multiple surrounding views to predict depth maps across cameras.

Autonomous Driving Monocular Depth Estimation

Similarity-Aware Fusion Network for 3D Semantic Segmentation

1 code implementation4 Jul 2021 Linqing Zhao, Jiwen Lu, Jie zhou

To address this, we employ a late fusion strategy where we first learn the geometric and contextual similarities between the input and back-projected (from 2D pixels) point clouds and utilize them to guide the fusion of two modalities to further exploit complementary information.

3D Semantic Segmentation

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