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

We introduce the task of localizing a flexible number of objects in real-world 3D scenes
using natural language descriptions. Existing 3D visual grounding tasks focus on localizing …

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 …

In the realm of computer vision and robotics embodied agents are expected to explore their
environment and carry out human instructions. This necessitates the ability to fully …

We introduce Open3DIS a novel solution designed to tackle the problem of Open-
Vocabulary Instance Segmentation within 3D scenes. Objects within 3D environments …

We propose a lightweight and scalable Regional Point-Language Contrastive learning
framework namely RegionPLC for open-world 3D scene understanding aiming to identify …

For robots to perform a wide variety of tasks, they require a 3D representation of the world
that is semantically rich, yet compact and efficient for task-driven perception and planning …

Open-vocabulary querying in 3D space is challenging but essential for scene understanding
tasks such as object localization and segmentation. Language-embedded scene …

Existing 3D scene understanding methods are heavily focused on 3D semantic and instance
segmentation. However identifying objects and their parts only constitutes an intermediate …

The recent Gaussian Splatting achieves high-quality and real-time novel-view synthesis of
the 3D scenes. However, it is solely concentrated on the appearance and geometry …