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SS3DM: Benchmarking Street-View Surface Reconstruction with a Synthetic 3D Mesh Dataset (2410.21739v2)

Published 29 Oct 2024 in cs.CV

Abstract: Reconstructing accurate 3D surfaces for street-view scenarios is crucial for applications such as digital entertainment and autonomous driving simulation. However, existing street-view datasets, including KITTI, Waymo, and nuScenes, only offer noisy LiDAR points as ground-truth data for geometric evaluation of reconstructed surfaces. These geometric ground-truths often lack the necessary precision to evaluate surface positions and do not provide data for assessing surface normals. To overcome these challenges, we introduce the SS3DM dataset, comprising precise \textbf{S}ynthetic \textbf{S}treet-view \textbf{3D} \textbf{M}esh models exported from the CARLA simulator. These mesh models facilitate accurate position evaluation and include normal vectors for evaluating surface normal. To simulate the input data in realistic driving scenarios for 3D reconstruction, we virtually drive a vehicle equipped with six RGB cameras and five LiDAR sensors in diverse outdoor scenes. Leveraging this dataset, we establish a benchmark for state-of-the-art surface reconstruction methods, providing a comprehensive evaluation of the associated challenges. For more information, visit our homepage at https://ss3dm.top.

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Summary

  • The paper introduces SS3DM, a novel synthetic 3D mesh dataset from CARLA that provides accurate ground truth for street-view surface reconstruction.
  • The paper evaluates methods like R3D3, UrbanNeRF, and StreetSurf using Chamfer Distance metrics to reveal strengths and limitations in reconstruction accuracy.
  • The paper’s findings promote future research on adaptive, scalable techniques for high-precision 3D reconstruction in autonomous driving and digital entertainment.

Analysis of SS3DM: Benchmarking Street-View Surface Reconstruction with a Synthetic 3D Mesh Dataset

The paper presents SS3DM, a novel dataset designed to benchmark street-view surface reconstruction methods using a synthetic 3D mesh dataset generated via the CARLA simulator. This initiative seeks to address crucial issues in existing datasets, such as the lack of precise ground-truth and the inability to comprehensively assess surface normals, primarily due to the limitations of noise-ridden LiDAR data. By providing detailed ground-truth mesh models, SS3DM enhances the potential for accurate geometric evaluations, making it an invaluable resource for advancing algorithms in this domain.

Core Contributions and Dataset Characteristics

Dataset Design

The SS3DM dataset notably shifts the paradigm from noisy LiDAR data to precise synthetic mesh models, providing robust benchmarks for state-of-the-art reconstruction methods. Crafted with the CARLA simulator, the dataset comprises detailed mesh models that facilitate refined assessments of both geometric positions and surface normals, crucial for rendering precise 3D reconstructions.

The dataset captures 28 sequences across diverse outdoor scenes, amassing 81,000 frames with multi-camera RGB inputs and multi-LiDAR point clouds, enhanced by semantic and depth information. These elements aim to bridge the gaps seen in traditional datasets such as KITTI, Waymo, and nuScenes.

Methodology and Evaluation

The paper benchmarks several contemporary surface reconstruction methods including R3D3, UrbanNeRF, and StreetSurf using SS3DM. Evaluated metrics such as Chamfer Distance and Normal Chamfer Distance offer insights into both the geometric accuracy and surface normal reconstruction performance of these methods. Despite the varying performance levels observed across different sequence lengths, the findings underscore the limits of current methodologies, especially regarding the accurate reconstruction of long sequences and sparsely observed regions.

StreetSurf (Full), leveraging NeuralSDF with multi-level hash grids, emerged as superior in reconstructing precise surfaces, highlighting the potential of combining RGB and LiDAR data modalities for enhanced performance. However, persistent challenges remain, particularly with respect to dealing with "floaters" and reconstructing complex, sparse structures accurately.

Implications and Future Directions

The implications of the work are manifold, particularly for advancements in applications such as autonomous driving and digital entertainment, where realistic and precise 3D surface reconstructions are pivotal. The comprehensive data offerings of SS3DM hold promise for significantly enhancing the evaluation robustness of surface reconstruction algorithms, potentially steering future developments in AI and computer vision toward more efficient, scalable models.

Several future directions emerge from this work. Firstly, the development of adaptive, efficient representations for large-scale scenes, potentially through sparse or hierarchical structures, could boost reconstruction efficiency and accuracy. Incorporating a split-and-merge strategy might also alleviate computational load during large-scale reconstructions. Furthermore, advancing multi-stage reconstruction methodologies could offer a balance between capturing surface smoothness and intricate details.

Conclusion

Overall, SS3DM represents a substantial step forward in benchmarking street-view surface reconstruction, offering high-fidelity data that addresses critical limitations of existing datasets. By providing precise ground-truth data, SS3DM sets the stage for more rigorous evaluations and encourages the exploration of innovative reconstruction techniques, ultimately advancing the field towards achieving high-accuracy 3D representations of complex outdoor environments.

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