Demo-Pose: Depth-Monocular Modality Fusion For Object Pose Estimation

1. 1Fuse RGB and depth modalities to leverage semantic cues and geometric information
2. 2Implement a cross-modal fusion approach to combine the strengths of both modalities
3. 3Train a model to estimate 9-DoF pose (6D pose + 3D size) without relying on CAD models during inference
4. 4Evaluate the performance of the model on category-level object pose estimation tasks

Computer vision engineers and researchers on a team can benefit from this approach to improve object pose estimation in applications like robotics and AR/VR. This can be particularly useful for teams working on scene understanding and 3D vision tasks

💡 Fusing depth and monocular modalities can improve object pose estimation by leveraging both semantic and geometric information

💡 Fusing depth and monocular modalities for object pose estimation without CAD models! #AI #ComputerVision