AdaPoinTr: Diverse Point Cloud Completion With Adaptive Geometry-Aware Transformers.

Area of Science:

• Computer Vision
• Machine Learning
• 3D Data Processing

Background:

• Point cloud completion is crucial for reconstructing 3D objects from incomplete data.
• Existing methods struggle with complex and diverse real-world point cloud scenarios.
• Transformers offer potential for improved structural knowledge and detail preservation.

Purpose of the Study:

• To develop an advanced Transformer-based architecture for point cloud completion.
• To enhance model efficiency and performance in handling complex and noisy 3D data.
• To establish new benchmarks and evaluate methods on challenging, diverse datasets.

Main Methods:

• Proposed PoinTr: a Transformer encoder-decoder architecture for set-to-set point cloud translation.
• Introduced AdaPoinTr: incorporating adaptive query generation and a novel denoising task.
• Developed geometry-aware blocks to explicitly model local geometric relationships.
• Extended the approach to scene-level point cloud completion with a geometry-enhanced framework.

Main Results:

• AdaPoinTr achieved over 20% improvement in completion performance and reduced training time by 15x.
• Established new state-of-the-art results on multiple benchmarks (PCN, ShapeNet-55, KITTI) with superior metrics.
• Demonstrated effectiveness on challenging, diverse point cloud datasets reflecting real-world scenarios.
• Achieved higher throughput and fewer FLOPs compared to previous leading methods.

Conclusions:

• Transformer architectures, like PoinTr and AdaPoinTr, are highly effective for point cloud completion.
• AdaPoinTr offers significant advancements in efficiency and performance for 3D data reconstruction.
• The proposed benchmarks and scene-level completion framework push the boundaries of point cloud research.