  To effectively apply robots in working environments and assist humans, it is
essential to develop and evaluate how visual grounding (VG) can affect machine
performance on occluded objects. However, current VG works are limited in
working environments, such as offices and warehouses, where objects are usually
occluded due to space utilization issues. In our work, we propose a novel
OCID-Ref dataset featuring a referring expression segmentation task with
referring expressions of occluded objects. OCID-Ref consists of 305,694
referring expressions from 2,300 scenes with providing RGB image and point
cloud inputs. To resolve challenging occlusion issues, we argue that it's
crucial to take advantage of both 2D and 3D signals to resolve challenging
occlusion issues. Our experimental results demonstrate the effectiveness of
aggregating 2D and 3D signals but referring to occluded objects still remains
challenging for the modern visual grounding systems. OCID-Ref is publicly
available at https://github.com/lluma/OCID-Ref
