Brain-inspired algorithms have become a new trend in next-generation artificial intelligence.
Through research on brain science, the intelligence of remote sensing algorithms can be …

Modern 3D semantic instance segmentation approaches predominantly rely on specialized
voting mechanisms followed by carefully designed geometric clustering techniques. Building …

With the fast development of 3D data acquisition techniques, topographic point clouds have
become easier to acquire and have promoted many geospatial applications. Ground filtering …

Existing 3D instance segmentation methods are predominated by the bottom-up design--
manually fine-tuned algorithm to group points into clusters followed by a refinement network …

Urban modeling from LiDAR point clouds is an important topic in computer vision, computer
graphics, photogrammetry and remote sensing. 3D city models have found a wide range of …

Current 3D open-vocabulary scene understanding methods mostly utilize well-aligned 2D
images as the bridge to learn 3D features with language. However, applying these …

We present the UrbanBIS benchmark for large-scale 3D urban understanding, supporting
practical urban-level semantic and building-level instance segmentation. UrbanBIS …

Advances in remote sensing image processing techniques have further increased the
demand for annotated datasets. However, preparing annotated multi-temporal 2D/3D …

Most 3D instance segmentation methods exploit a bottom-up strategy, typically including
resource-exhaustive post-processing. For point grouping, bottom-up methods rely on prior …

With the rapid advancement of 3D sensors, there is an increasing demand for 3D scene
understanding and an increasing number of 3D deep learning algorithms have been …