PanopticNeRF-360: Panoramic 3D-to-2D Label Transfer in Urban Scenes (2309.10815v2)

Abstract: Training perception systems for self-driving cars requires substantial 2D annotations that are labor-intensive to manual label. While existing datasets provide rich annotations on pre-recorded sequences, they fall short in labeling rarely encountered viewpoints, potentially hampering the generalization ability for perception models. In this paper, we present PanopticNeRF-360, a novel approach that combines coarse 3D annotations with noisy 2D semantic cues to generate high-quality panoptic labels and images from any viewpoint. Our key insight lies in exploiting the complementarity of 3D and 2D priors to mutually enhance geometry and semantics. Specifically, we propose to leverage coarse 3D bounding primitives and noisy 2D semantic and instance predictions to guide geometry optimization, by encouraging predicted labels to match panoptic pseudo ground truth. Simultaneously, the improved geometry assists in filtering 3D&2D annotation noise by fusing semantics in 3D space via a learned semantic field. To further enhance appearance, we combine MLP and hash grids to yield hybrid scene features, striking a balance between high-frequency appearance and contiguous semantics. Our experiments demonstrate PanopticNeRF-360's state-of-the-art performance over label transfer methods on the challenging urban scenes of the KITTI-360 dataset. Moreover, PanopticNeRF-360 enables omnidirectional rendering of high-fidelity, multi-view and spatiotemporally consistent appearance, semantic and instance labels. We make our code and data available at https://github.com/fuxiao0719/PanopticNeRF

PanopticNeRF-360: Advancements in Panoramic 3D-to-2D Label Transfer for Urban Scenes

The paper "PanopticNeRF-360: Panoramic 3D-to-2D Label Transfer in Urban Scenes" presents a novel approach to the challenge of label transfer in complex, real-world urban environments. The authors propose a method that leverages coarse 3D bounding box annotations alongside noisy 2D semantic cues to produce high-fidelity and spatiotemporally consistent labels across omnidirectional views, with a primary focus on the KITTI-360 dataset.

Key Contributions and Methodology

The authors' essential contribution is the development of PanopticNeRF-360, which advances from previous methodologies through several key features:

1. Panoptic Label-Guided Geometry Optimization: By enhancing the conventional semantic label-guided geometry optimization to include panoptic labels with pseudo instance annotations, the method improves the density field, which subsequently enhances the semantic field.
2. Trade-off Between Appearance and Semantics: The authors document a significant trade-off between high-frequency appearance modeling and semantic coherence. To mitigate this, they employ a combination of multi-layer perceptrons (MLP) and multi-resolution hash grids, achieving a noted enhancement in appearance metrics (an improvement of 4dB) while reducing training times by approximately 2.5x.
3. Omnidirectional Panoramic Label Transfer: Extending label transfer from perspective views to panoramic views with the introduction of side-facing fisheye cameras is another significant advancement. The work includes the establishment of a new benchmark with manually annotated panoptic labels on fisheye images, allowing for robust comparative studies.

Experimental Results

The experimental layout of the paper is extensive, with the following highlights:

• A benchmark comparison on fisheye views and forward-facing views demonstrated superior performance in terms of mIoU (0.8) for semantics and PQ (2.3) for instances.
• A thorough ablation paper validated the efficacy of the enhanced network modules, while comparisons with existing methodologies, such as Jacobian NeRF (CVPR 2023) and SSA (Segment Anything variant, 2023), underscored the superior performance of PanopticNeRF-360.

Implications and Future Work

The introduction of PanopticNeRF-360 has notable theoretical and practical implications. From a theoretical standpoint, it contributes to the body of knowledge on the balancing of high-frequency image details with semantic coherence in neural scene representations. Practically, this research holds potential for improving the automated understanding of urban environments, which could influence advancements in autonomous driving systems and smart urban planning.

Future work could explore further optimized training algorithms to expedite performance gains and reduce computation demands. Additionally, expanding the method to handle diverse weather conditions and varying lighting in real-world scenarios would be valuable to enhance its robustness.

In conclusion, PanopticNeRF-360 signifies a meaningful step in label transfer methodologies, addressing both the geometric and semantic complexities of urban scenes with promising results that provide a basis for continued exploration and application in AI-driven urban analyses.