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20192026
most citedDLA-Net: Learning Dual Local Attention Features for Semantic Segmentation of Large-Scale Building Facade Point Clouds

7 citations · 15 across the 5 of their papers we have counts for

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6 papers · 1 filter

cs.CV2026

ZIL: Zero-shot Image-to-LiDAR Registration

Zijun Li, Xiaotian Sun, Xuelun Shen +6

Image-to-LiDAR registration estimates the camera pose of an image with respect to a LiDAR point cloud. It has diverse applications in autonomous driving, robot navigation etc. Howe…

cs.CV2024

ConDo: Continual Domain Expansion for Absolute Pose Regression

Zijun Li, Zhipeng Cai, Bochun Yang +5

Visual localization is a fundamental machine learning problem. Absolute Pose Regression (APR) trains a scene-dependent model to efficiently map an input image to the camera pose in…

cs.CV2024★ 2 cited

GIM: Learning Generalizable Image Matcher From Internet Videos

Xuelun Shen, Zhipeng Cai, Wei Yin +5

Image matching is a fundamental computer vision problem. While learning-based methods achieve state-of-the-art performance on existing benchmarks, they generalize poorly to in-the-…

cs.CV2021★ 7 cited

DLA-Net: Learning Dual Local Attention Features for Semantic Segmentation of Large-Scale Building Facade Point Clouds

Yanfei Su, Weiquan Liu, Zhimin Yuan +4

Semantic segmentation of building facade is significant in various applications, such as urban building reconstruction and damage assessment. As there is a lack of 3D point clouds…

cs.CV2021

A Detector-oblivious Multi-arm Network for Keypoint Matching

Xuelun Shen, Qian Hu, Xin Li +1

This paper presents a matching network to establish point correspondence between images. We propose a Multi-Arm Network (MAN) to learn region overlap and depth, which can greatly i…

cs.CV2019★ 6 cited

RF-Net: An End-to-End Image Matching Network based on Receptive Field

Xuelun Shen, Cheng Wang, Xin Li +5

This paper proposes a new end-to-end trainable matching network based on receptive field, RF-Net, to compute sparse correspondence between images. Building end-to-end trainable mat…