Kernekoncepter
Improving self-supervised scene flow estimation through surface awareness and cyclic consistency.
Resumé
The content introduces a novel learning framework to enhance regularization in self-supervised 3D scene flow estimation. It focuses on improving smoothness losses by introducing surface awareness and cyclic consistency. The proposed method outperforms existing models on various datasets, showcasing its effectiveness and generalization capability.
I. Introduction
- Scene flow is crucial for various robotic and computer vision tasks.
- Shift from fully supervised to self-supervised methods due to scarcity of real-world annotations.
- Importance of regularization in self-supervised scene flow estimation.
II. Method
- Proposes novel losses for rigid cluster formation and flow smoothness.
- Introduces surface-aware smoothness and cyclic consistency losses.
- Architecture-independent approach applicable to existing models.
III. Experiments
- Demonstrates performance gains on state-of-the-art architectures and benchmarks.
- Generalization over LiDAR datasets and stereoKITTI.
- Ablation studies show the impact of proposed losses on performance.
IV. Limitations
- Implicitly enforcing translational movement with larger rigid clusters.
- Sparse point clouds may introduce noise with surface-aware smoothness.
V. Conclusion
- Surface awareness and cyclic smoothness improve self-supervised scene flow estimation.
- Demonstrated effectiveness across different datasets and architectures.
- Potential for future applications in instance segmentation.
Statistik
The proposed framework outperforms state-of-the-art models on various datasets.
The method showcases improvements in EPE, AS, AR, and Outlier metrics.
Citater
"Our proposed losses improve the state-of-the-art architectures on both KITTIo and KITTIt splits."
"The experiments demonstrated that the method works in real-world LiDAR settings, with point clouds from a standard stereo-based dataset."