Panoptic NeRF: 3D-to-2D Label Transfer for Panoptic Urban Scene Segmentation (2203.15224v2)

Abstract: Large-scale training data with high-quality annotations is critical for training semantic and instance segmentation models. Unfortunately, pixel-wise annotation is labor-intensive and costly, raising the demand for more efficient labeling strategies. In this work, we present a novel 3D-to-2D label transfer method, Panoptic NeRF, which aims for obtaining per-pixel 2D semantic and instance labels from easy-to-obtain coarse 3D bounding primitives. Our method utilizes NeRF as a differentiable tool to unify coarse 3D annotations and 2D semantic cues transferred from existing datasets. We demonstrate that this combination allows for improved geometry guided by semantic information, enabling rendering of accurate semantic maps across multiple views. Furthermore, this fusion process resolves label ambiguity of the coarse 3D annotations and filters noise in the 2D predictions. By inferring in 3D space and rendering to 2D labels, our 2D semantic and instance labels are multi-view consistent by design. Experimental results show that Panoptic NeRF outperforms existing label transfer methods in terms of accuracy and multi-view consistency on challenging urban scenes of the KITTI-360 dataset.

• The paper presents a novel 3D-to-2D label transfer approach that fuses coarse 3D annotations with 2D semantic predictions via dual semantic fields.
• It integrates semantically-guided geometry optimization, outperforming traditional depth-based methods on metrics such as mIoU on the KITTI-360 dataset.
• The method ensures multi-view consistency, offering scalable benefits for creating densely annotated datasets in autonomous driving applications.

Overview of "Panoptic NeRF: 3D-to-2D Label Transfer for Panoptic Urban Scene Segmentation"

The paper "Panoptic NeRF: 3D-to-2D Label Transfer for Panoptic Urban Scene Segmentation" presents a novel method aimed at enhancing the efficiency of the annotation process for urban scene segmentation by leveraging a mechanism for transferring labels from three-dimensional (3D) space to two-dimensional (2D) images. Utilizing the Neural Radiance Field (NeRF) framework, the authors propose a dual semantic field approach to effectively integrate coarse 3D annotations with 2D semantic cues, resolving ambiguities associated with overlapping object regions in urban environments. This approach facilitates the accurate rendering of multi-view consistent panoptic segmentation labels, a critical task in developing autonomous driving systems.

Technical Contributions

The paper introduces several engineering advancements over existing algorithms:

1. Integration of Coarse 3D Annotations and 2D Semantic Predictions: By inferring semantic information in 3D space, Panoptic NeRF overlays basic 3D bounding primitives with 2D image-derived semantic cues to generate high-fidelity instance labels on 2D views. The combination addresses label ambiguity and noise inherent in both data sources.
2. Dual Semantic Fields: The authors integrate dual semantic fields within the NeRF structure, comprising a fixed semantic field based on 3D bounding primitives and a learned semantic field refined with projected 2D predictions. This duality allows the model to optimize both the underlying geometric structure and semantic labeling, a crucial factor when input views are sparse.
3. Semantically-Guided Geometry Optimization: Introducing semantic supervision into the geometry optimization process significantly outperforms traditional depth-based optimizations, enhancing the accuracy of geometric reconstructions in challenging urban environments.
4. Multi-View Consistency: The method inherently ensures multi-view consistency by conducting 3D space inference, a significant limitation faced by many existing 2D-to-2D label transfer methods.

Quantitative Evaluation

Experimental results demonstrated the effectiveness of Panoptic NeRF compared to established baselines. The method outperformed both Fully Connected Conditional Random Fields (CRF) and other label transfer techniques on the KITTI-360 dataset in terms of semantic segmentation accuracy (mIoU), pixel accuracy, and panoptic quality metrics. Particularly in complex scenes where input views are limited, Panoptic NeRF excelled in generating detailed and consistent object boundaries.

Implications for Future Research

The implications of Panoptic NeRF's methodology extend to practical applications in autonomous vehicle perception systems. The fusion of 3D and 2D labeling strategies in an efficient, automated process could spur the creation of large-scale, densely annotated datasets necessary for training cutting-edge AI models. Future research could explore reducing the computational burden associated with the current per-scene optimization process, potentially leveraging advances in accelerated neural rendering techniques. Additionally, adapting the framework to dynamic scenarios could expand its application to evolving environments.

Conclusion

In summary, "Panoptic NeRF: 3D-to-2D Label Transfer for Panoptic Urban Scene Segmentation" delivers a significant contribution to the field of scene segmentation for autonomous systems, resolving several key challenges in accurate and efficient label transfer. The innovative integration of dual semantic fields within a NeRF-based framework advances both theoretical understanding and practical capabilities in urban scene analysis.