Recent advancements in vision foundation models (VFMs) have opened up new
possibilities for versatile and efficient visual perception. In this work, we introduce Seal, a …

We study the problem of 3D semantic segmentation from raw point clouds. Unlike existing
methods which primarily rely on a large amount of human annotations for training neural …

Weak supervision has proven to be an effective strategy for reducing the burden of
annotating semantic segmentation tasks in 3D space. However, unconstrained or heuristic …

As artificial intelligence (AI) has already seen numerous successful applications, the
upcoming challenge lies in how to realize artificial general intelligence (AGI). Self-learning …

In the past decade, deep neural networks have achieved significant progress in point cloud
learning. However, collecting large-scale precisely-annotated point clouds is extremely …

Manual annotation of every point in a point cloud is a costly and labor-intensive process.
While weakly supervised point cloud semantic segmentation (WSPCSS) with sparse …

Active learning, a label-efficient paradigm, empowers models to interactively query an oracle
for labeling new data. In the realm of LiDAR semantic segmentation, the challenges stem …

Whilst the availability of 3D LiDAR point cloud data has significantly grown in recent years,
annotation remains expensive and time-consuming, leading to a demand for semi …

Deep learning applications on LiDAR data suffer from a strong domain gap when applied to
different sensors or tasks. In order for these methods to obtain similar accuracy on different …

This article tackles the problem of requiring a large amount of data annotation in the LiDAR
point cloud semantic segmentation (PCSS) task by proposing OPOCA, a weakly supervised …