Recent developments in foundation models, like Large Language Models (LLMs) and Vision-
Language Models (VLMs), trained on extensive data, facilitate flexible application across …

Building general-purpose robots that operate seamlessly in any environment, with any
object, and utilizing various skills to complete diverse tasks has been a long-standing goal in …

3D segmentation is a fundamental and challenging problem in computer vision with
applications in autonomous driving and robotics. It has received significant attention from the …

Modern tools for class-agnostic image segmentation (eg, SegmentAnything) and open-set
semantic understanding (eg, CLIP) provide unprecedented opportunities for robot …

As large language models (LLMs) evolve, their integration with 3D spatial data (3D-LLMs)
has seen rapid progress, offering unprecedented capabilities for understanding and …

Open-vocabulary 3D instance segmentation is cutting-edge for its ability to segment 3D
instances without predefined categories. However progress in 3D lags behind its 2D …

Abstract 3D segmentation is a fundamental and challenging problem in computer vision with
applications in autonomous driving and robotics. It has received significant attention from the …

Neural Fields have emerged as a transformative approach for 3D scene representation in
computer vision and robotics, enabling accurate inference of geometry, 3D semantics, and …

Referring perception, which aims at grounding visual objects with multimodal referring
guidance, is essential for bridging the gap between humans, who provide instructions, and …

The advancement of foundation models, such as large language models (LLMs), vision-
language models (VLMs), diffusion models, and robotics foundation models (RFMs), has …