Transfer Learning from Synthetic to Real LiDAR Point Cloud for Semantic Segmentation (2107.05399v2)

Abstract: Knowledge transfer from synthetic to real data has been widely studied to mitigate data annotation constraints in various computer vision tasks such as semantic segmentation. However, the study focused on 2D images and its counterpart in 3D point clouds segmentation lags far behind due to the lack of large-scale synthetic datasets and effective transfer methods. We address this issue by collecting SynLiDAR, a large-scale synthetic LiDAR dataset that contains point-wise annotated point clouds with accurate geometric shapes and comprehensive semantic classes. SynLiDAR was collected from multiple virtual environments with rich scenes and layouts which consists of over 19 billion points of 32 semantic classes. In addition, we design PCT, a novel point cloud translator that effectively mitigates the gap between synthetic and real point clouds. Specifically, we decompose the synthetic-to-real gap into an appearance component and a sparsity component and handle them separately which improves the point cloud translation greatly. We conducted extensive experiments over three transfer learning setups including data augmentation, semi-supervised domain adaptation and unsupervised domain adaptation. Extensive experiments show that SynLiDAR provides a high-quality data source for studying 3D transfer and the proposed PCT achieves superior point cloud translation consistently across the three setups. SynLiDAR project page: \url{https://github.com/xiaoaoran/SynLiDAR}

Summary of "SynLiDAR: Learning From Synthetic LiDAR Sequential Point Cloud for Semantic Segmentation"

The paper "SynLiDAR: Learning From Synthetic LiDAR Sequential Point Cloud for Semantic Segmentation" addresses significant challenges in the application of LiDAR point clouds for semantic segmentation, a critical task in 3D scene perception. The authors seek to overcome the limitations of data annotation in large-scale datasets by introducing SynLiDAR, a comprehensive synthetic LiDAR point cloud dataset, and proposing a novel translation mechanism, PCT (Point Cloud Translator), to bridge the domain gap between synthetic and real LiDAR data.

SynLiDAR is presented as a large-scale dataset designed to fill the existing void in synthetic LiDAR data suitable for transfer learning in 3D scene understanding. It consists of over 19 billion annotated points encompassing 32 semantic classes, derived from meticulously constructed virtual environments that faithfully mimic real-world geometries and layouts. The availability of such extensive and detailed synthetic data stands to significantly aid research efforts in semantic segmentation, especially given the challenges associated with collecting and annotating large real-world LiDAR datasets.

The core innovation of the paper lies in the PCT, which performs efficient domain adaptation by decomposing the synthetic-to-real point cloud translation into an appearance component and a sparsity component, addressing them separately. The Appearance Translation Module (ATM) and the Sparsity Translation Module (STM) work in tandem within the PCT to adjust synthetic point clouds' appearance and sparsity to more closely align with real data, a methodology that has shown promise in multiple semantic segmentation scenarios.

The authors conducted rigorous experiments under different transfer learning configurations: data augmentation (DA), semi-supervised domain adaptation (SSDA), and unsupervised domain adaptation (UDA). The results demonstrated that SynLiDAR significantly enhances semantic segmentation models when combined with real datasets, achieving improved mean IoU scores in both SemanticKITTI and SemanticPOSS datasets. The use of PCT further elevated performance, underscoring its efficacy in mitigating domain gaps effectively.

In practical terms, the implications of this research are far-reaching, offering a viable pathway to alleviate the heavy resource demands of annotating real-world LiDAR data. Theoretically, the paper advances the understanding of domain adaptation in semantic segmentation, particularly for 3D point clouds, an area historically overshadowed by work in 2D image domains.

Future work could explore the extension of SynLiDAR with additional virtual environments or new classes to broaden its applicability. Moreover, further development on the PCT could include more sophisticated models that account for additional domain gap variables or hyperparameters that could further perfect the alignment between synthetic and real data distributions.

In sum, the introduction of SynLiDAR along with the proposed PCT represents a valuable contribution to AI research focused on understanding and practical deployment of semantic segmentation models in dynamic, three-dimensional environments. The advances described in this paper hold potential utility not only within academic inquiry but also across various industrial applications, particularly in urban planning and autonomous navigation where 3D perception is crucial.