Learning Dynamic Scene-Conditioned 3D Object Detectors.

Area of Science:

• Computer Vision
• Artificial Intelligence
• Robotics

Background:

• Existing 3D object detectors struggle with ambiguity in complex scenes due to a lack of scene-level context.
• Object-level analysis alone is insufficient for accurately distinguishing similar objects based solely on point data.

Purpose of the Study:

• To develop a dynamic 3D object detector (HyperDet3D) that leverages scene-level knowledge for improved accuracy.
• To enhance the detector by incorporating scene-specific priors to adapt to varying environments.
• To propose HyperFormer3D, addressing noise and redundancy in scene input for more robust parameter generation.

Main Methods:

• Designed scene-conditioned hypernetworks to learn both scene-agnostic embeddings and scene-specific knowledge.
• Introduced task-specific scene priors: Semantic Occurrence and Objectness Localization.
• Developed a transformer-based hypernetwork in HyperFormer3D to translate scene priors into detector parameters, mitigating noise.

Main Results:

• HyperDet3D effectively reduces ambiguity in 3D object representation using hierarchical scene context.
• HyperFormer3D demonstrates improved performance by utilizing focused scene priors, avoiding raw scene data issues.
• Both methods show significant effectiveness on ScanNet, SUN RGB-D, and MatterPort3D datasets.

Conclusions:

• Scene-level priors are crucial for enhancing the performance and robustness of 3D object detectors.
• HyperFormer3D offers a more refined approach by incorporating task-specific scene understanding, leading to superior detection capabilities.
• The proposed methods represent a significant advancement in dynamic 3D object detection within complex environments.