We tackle the problem of 3D point cloud localization based on a few natural linguistic
descriptions and introduce a novel neural network Text2Loc that fully interprets the semantic …

Place recognition based on point clouds (LiDAR) is an important component for autonomous
robots or self-driving vehicles. Current SOTA performance is achieved on accumulated …

In the era of advancing autonomous driving and increasing reliance on geospatial
information, high-precision mapping not only demands accuracy but also flexible …

Point cloud upsampling is to densify a sparse point set acquired from 3D sensors, providing
a denser representation for the underlying surface. Existing methods divide the input points …

“Where was this place?”-figuring out the location of a point cloud scene is a challenge that
has attracted researchers in recent years, under the name of point cloud place recognition …

Unpaired point cloud completion involves filling in missing parts of a point cloud without
requiring partial-complete correspondence. Meanwhile, since point cloud completion is an ill …

Due to the limited resolution of 3D sensors and the inevitable mutual occlusion between
objects, 3D scans of real scenes are commonly incomplete. Previous scene completion …

The descriptive power and comparative metric of features are the key factors in multi-modal
place recognition tasks. However, learning a multi-modal descriptor directly for comparison …

Previous neural radiance fields often struggle to preserve high-frequency textures in urban
and aerial large-scale scenes due to insufficient model capacity on the scene surface. This …

Point cloud plays a significant role in recent learning-based vision tasks, which contain
additional information about the physical space compared to 2D images. However, such a …