LiDAR segmentation is crucial for autonomous driving perception. Recent trends favor point-
or voxel-based methods as they often yield better performance than the traditional range …

We introduce OpenShape, a method for learning multi-modal joint representations of text,
image, and point clouds. We adopt the commonly used multi-modal contrastive learning …

In the field of visual scene understanding, deep neural networks have made impressive
advancements in various core tasks like segmentation, tracking, and detection. However …

Closed-set 3D perception models trained on only a pre-defined set of object categories can
be inadequate for safety critical applications such as autonomous driving where new object …

Efficient data utilization is crucial for advancing 3D scene understanding in autonomous
driving, where reliance on heavily human-annotated LiDAR point clouds challenges fully …

Training a 3D scene understanding model requires complicated human annotations, which
are laborious to collect and result in a model only encoding close-set object semantics. In …

Zero-shot point cloud segmentation aims to make deep models capable of recognizing
novel objects in point cloud that are unseen in the training phase. Recent trends favor the …

Open-vocabulary segmentation of 3D scenes is a fundamental function of human perception
and thus a crucial objective in computer vision research. However, this task is heavily …

The computer vision, graphics, and machine learning research groups have given a
significant amount of focus to 3D object recognition (segmentation, detection, and …

In this work, we introduce OpenIns3D, a new 3D-input-only framework for 3D open-
vocabulary scene understanding. The OpenIns3D framework employs a “Mask-Snap …