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PanoOcc: Unified Occupancy Representation for Camera-based 3D Panoptic Segmentation (2306.10013v1)

Published 16 Jun 2023 in cs.CV and cs.RO

Abstract: Comprehensive modeling of the surrounding 3D world is key to the success of autonomous driving. However, existing perception tasks like object detection, road structure segmentation, depth & elevation estimation, and open-set object localization each only focus on a small facet of the holistic 3D scene understanding task. This divide-and-conquer strategy simplifies the algorithm development procedure at the cost of losing an end-to-end unified solution to the problem. In this work, we address this limitation by studying camera-based 3D panoptic segmentation, aiming to achieve a unified occupancy representation for camera-only 3D scene understanding. To achieve this, we introduce a novel method called PanoOcc, which utilizes voxel queries to aggregate spatiotemporal information from multi-frame and multi-view images in a coarse-to-fine scheme, integrating feature learning and scene representation into a unified occupancy representation. We have conducted extensive ablation studies to verify the effectiveness and efficiency of the proposed method. Our approach achieves new state-of-the-art results for camera-based semantic segmentation and panoptic segmentation on the nuScenes dataset. Furthermore, our method can be easily extended to dense occupancy prediction and has shown promising performance on the Occ3D benchmark. The code will be released at https://github.com/Robertwyq/PanoOcc.

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An Overview of "PanoOcc: Unified Occupancy Representation for Camera-based 3D Panoptic Segmentation"

The paper presents "PanoOcc," a novel methodology in the domain of camera-based 3D panoptic segmentation, aimed at augmenting the understanding of complex 3D environments solely through camera inputs. The work seeks to remediate the fragmented approach of current methods by consolidating different perception tasks into a unified occupancy representation. By effectively processing visual data to predict dense 3D voxel-based panoptic segmentation, PanoOcc proposes a comprehensive and singular framework that combines both the semantic segmentation of surroundings and object detection in the 3D domain.

Methodological Innovations

  1. Unified Occupancy Representation: The cornerstone of PanoOcc is its unique employment of voxel queries to integrate spatiotemporal data across multi-frame and multi-view images. This approach employs a coarse-to-fine scheme to embed feature learning within the voxel space, thus nurturing a holistic understanding of 3D environment occupancy.
  2. Camera-based Panoptic Segmentation: Unlike existing methods which rely on LiDAR, PanoOcc operates exclusively on camera data, proposing a method where inputs from multiple camera perspectives and timeframes are aggregated to generate dense panoptic predictions. This highlights PanoOcc's potential for cost efficiency while maintaining high accuracy through advanced image processing techniques.
  3. Efficiency and Performance: The proposed approach shows significant enhancements over baseline models in both efficiency and performance, as evidenced by state-of-the-art results on the nuScenes and Occ3D benchmarks. The method is shown to outpace existing camera-based methodologies in segmentation and detection metrics, demonstrating PanoOcc's aptitude in processing and interpreting visual data into actionable panoptic insights.

Results and Implications

The empirical results detailed in the paper showcase PanoOcc achieving a 70.7 mIoU on the nuScenes dataset, a marked improvement on previous benchmarks in camera-based semantic segmentation and panoptic segmentation. Additionally, the method displays adaptability, extending to dense occupancy prediction tasks and exhibits promising performance on the Occ3D benchmark dataset.

These achievements underline the potential of PanoOcc in diverse practical applications, especially in autonomous driving, where understanding the dynamic and static components of road scenes in three dimensions is paramount. The methodology encourages a shift towards unified frameworks for holistic 3D scene understanding, pressing the relevance of integrated object segmentation and detection within autonomous systems.

Speculation on Future Research

Given PanoOcc's foundation on camera-based inputs, future research may investigate the incorporation of other sensor modalities to further refine scene understanding, particularly in scenarios with limited visibility or challenging lighting conditions. Additionally, the interaction between voxel-based representations and real-time processing will be an important area for exploration, helping to bridge the gap between high-fidelity 3D reconstructions and latency-efficient scene understanding.

Furthermore, the framework introduced in PanoOcc lays the groundwork for interactions with machine learning models that focus on prediction and planning, ultimately leading to enhanced decision-making pipelines in autonomous systems. Future work might also focus on optimizing the computational requirements of PanoOcc, especially for real-world deployment in edge AI scenarios.

In conclusion, "PanoOcc: Unified Occupancy Representation for Camera-based 3D Panoptic Segmentation" contributes significantly to the field by proposing a method that robustly unifies traditional segmentation and detection tasks, offering a novel and integrated approach to scene understanding in vision-based autonomous systems.

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Authors (5)
  1. Yuqi Wang (62 papers)
  2. Yuntao Chen (37 papers)
  3. Xingyu Liao (18 papers)
  4. Lue Fan (26 papers)
  5. Zhaoxiang Zhang (162 papers)
Citations (51)
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GitHub

  1. GitHub - Robertwyq/PanoOcc: PanoOcc: Unified Occupancy Representation for Camera-based 3D Panoptic Segmentation (171 stars)