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Xinjun Li

7 accepted papers

2026

GeoGuide: Hierarchical Geometric Guidance for Open-Vocabulary 3D Semantic Segmentation

CVPR 2026

Open-vocabulary 3D semantic segmentation aims to segment arbitrary categories beyond the training set. Existing methods predominantly rely on distilling knowledge from 2D open-vocabulary models. However, aligning 3D features to the 2D representation space restricts intrinsic 3D geometric learning an

Cited by 0SourceScholar
2026

RayI2P: Learning Rays for Image-to-Point Cloud Registration

ICLR 2026poster

Image-to-point cloud registration aims to estimate the 6-DoF camera pose of a query image relative to a 3D point cloud map. Existing methods fall into two categories: matching-free methods regress pose directly using geometric priors, but lack fine-grained supervision and struggle with precise align…

Cited by 0SourceScholar
2026

Rethinking 2D-3D Registration: A Novel Network for High-Value Zone Selection and Representation Consistency Alignment

CVPR 2026

Both detection-then-match and detection-free methods have been extensively studied for image-to-point cloud registration, yet they still face significant challenges. The detection-then-match approach emphasizes high-quality correspondences but is limited by the availability of repeatable keypoints,

Cited by 0SourceScholar
2025

Bridge 2D-3D: Uncertainty-aware Hierarchical Registration Network with Domain Alignment

AAAI 2025technical

The method for image-to-point cloud registration typically determines the rigid transformation using a coarse-to-fine pipeline. However, directly and uniformly matching image patches with point cloud patches may lead to focusing on incorrect noise patches during matching while ignoring key ones. Mor…

Cited by 1SourcePDFScholar
2025

CA-I2P: Channel-Adaptive Registration Network with Global Optimal Selection

ICCV 2025poster

Detection-free methods typically follow a coarse-to-fine pipeline, extracting image and point cloud features for patch-level matching and refining dense pixel-to-point correspondences. However, differences in feature channel attention between images and point clouds may lead to degraded matching res…

Cited by 0SourcePDFScholar
2025

Implicit Correspondence Learning for Image-to-Point Cloud Registration

CVPR 2025highlight

Image-to-point cloud registration aims to estimate the camera pose of a given image within a 3D scene point cloud. In this area, matching-based methods have achieved leading performance by first detecting the overlapping region, then matching point and pixel features learned by neural networks and f…

Cited by 0SourcePDFScholar
2024

SD2Event:Self-supervised Learning of Dynamic Detectors and Contextual Descriptors for Event Cameras

CVPR 2024poster

Event cameras offer many advantages over traditional frame-based cameras such as high dynamic range and low latency. Therefore event cameras are widely applied in diverse computer vision applications where event-based keypoint detection is a fundamental task. However achieving robust event-based key…

Cited by 6SourcePDFScholar