  In real-life scenarios, humans seek out objects in the 3D world to fulfill
their daily needs or intentions. This inspires us to introduce 3D intention
grounding, a new task in 3D object detection employing RGB-D, based on human
intention, such as "I want something to support my back". Closely related, 3D
visual grounding focuses on understanding human reference. To achieve detection
based on human intention, it relies on humans to observe the scene, reason out
the target that aligns with their intention ("pillow" in this case), and
finally provide a reference to the AI system, such as "A pillow on the couch".
Instead, 3D intention grounding challenges AI agents to automatically observe,
reason and detect the desired target solely based on human intention. To tackle
this challenge, we introduce the new Intent3D dataset, consisting of 44,990
intention texts associated with 209 fine-grained classes from 1,042 scenes of
the ScanNet dataset. We also establish several baselines based on different
language-based 3D object detection models on our benchmark. Finally, we propose
IntentNet, our unique approach, designed to tackle this intention-based
detection problem. It focuses on three key aspects: intention understanding,
reasoning to identify object candidates, and cascaded adaptive learning that
leverages the intrinsic priority logic of different losses for multiple
objective optimization.
