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

Current 3D scene segmentation methods are heavily dependent on manually annotated 3D
training datasets. Such manual annotations are labor-intensive, and often lack fine-grained …

Large visual-language models (VLMs), like CLIP, enable open-set image segmentation to
segment arbitrary concepts from an image in a zero-shot manner. This goes beyond the …

While grasp detection is an important part of any robotic manipulation pipeline, reliable and
accurate grasp detection in $ SE (3) $ remains a research challenge. Many robotics …

We introduce the task of localizing an input image within a multi-modal reference map
represented by a collection of 3D scene graphs. These scene graphs comprise multiple …

In this work, we address the task of point cloud denoising using a novel framework adapting
Diffusion Schrödinger bridges to unstructured data like point sets. Unlike previous works that …

In recent years, modern techniques in deep learning and large-scale datasets have led to
impressive progress in 3D instance segmentation, grasp pose estimation, and robotics. This …

Recent advances in predicting 6D grasp poses from a single depth image have led to
promising performance in robotic grasping. However, previous grasping models face …

The advent of Vision Language Models (VLMs) transformed image understanding from
closed-set classifications to dynamic image-language interactions, enabling open …