BIP3D
| Property | Value |
|---|---|
| Authors | Xuewu Lin, Tianwei Lin, Lichao Huang, Hongyu Xie, Zhizhong Su |
| Paper | arXiv:2411.14869 |
| Organization | HorizonRobotics |
What is BIP3D?
BIP3D is an innovative model designed to bridge the gap between 2D image understanding and 3D perception for embodied intelligence. Built upon GroundingDINO, it introduces significant improvements in multi-view 3D detection and grounding tasks, achieving state-of-the-art performance across various benchmarks.
Implementation Details
The model introduces two major improvements over existing architectures:
- Enhanced 3D Deformable Attention: Replaces traditional deformable aggregation with a more sophisticated 3D deformable attention mechanism (DAT), utilizing trilinear interpolation for improved feature sampling
- Mixed Data Training Strategy: Combines detection and grounding data during the finetuning process for optimal performance
- RGB-D Support: Capable of processing both RGB and RGB-D inputs, with RGB-D showing superior performance
Core Capabilities
- Multi-view 3D Detection with up to 23.24% accuracy on RGB-D inputs
- 3D Grounding with performance reaching 70.53% on the test dataset
- Effective across various scenarios including ScanNet, 3RScan, and MP3D datasets
- Superior performance in both view-dependent and view-independent tasks
Frequently Asked Questions
Q: What makes this model unique?
BIP3D stands out for its novel fusion operation using 3D deformable attention and mixed data training approach, achieving significant improvements over baseline models in 3D perception tasks. It demonstrates exceptional performance particularly with RGB-D inputs, showing up to 66.58% accuracy in validation tests.
Q: What are the recommended use cases?
The model is particularly well-suited for embodied AI applications requiring 3D perception, including indoor navigation, object detection, and scene understanding. It performs exceptionally well in multi-view scenarios and can handle both simple and complex grounding tasks across various environments.
