SEGCloud: Semantic Segmentation of 3D Point Clouds (1710.07563v1)

Abstract: 3D semantic scene labeling is fundamental to agents operating in the real world. In particular, labeling raw 3D point sets from sensors provides fine-grained semantics. Recent works leverage the capabilities of Neural Networks (NNs), but are limited to coarse voxel predictions and do not explicitly enforce global consistency. We present SEGCloud, an end-to-end framework to obtain 3D point-level segmentation that combines the advantages of NNs, trilinear interpolation(TI) and fully connected Conditional Random Fields (FC-CRF). Coarse voxel predictions from a 3D Fully Convolutional NN are transferred back to the raw 3D points via trilinear interpolation. Then the FC-CRF enforces global consistency and provides fine-grained semantics on the points. We implement the latter as a differentiable Recurrent NN to allow joint optimization. We evaluate the framework on two indoor and two outdoor 3D datasets (NYU V2, S3DIS, KITTI, Semantic3D.net), and show performance comparable or superior to the state-of-the-art on all datasets.

• The paper proposes an end-to-end framework integrating 3D-FCNN, trilinear interpolation, and FC-CRF to achieve precise point cloud segmentation.
• It demonstrates competitive performance on datasets like NYU V2, S3DIS, KITTI, and Semantic3D.net, with mIOU up to 61.30%.
• The approach scales effectively and establishes a strong foundation for further advancements in 3D scene understanding and autonomous applications.

Semantic Segmentation of 3D Point Clouds Using SEGCloud

The paper "SEGCloud: Semantic Segmentation of 3D Point Clouds" presents a comprehensive end-to-end framework for the semantic segmentation of 3D point clouds, independent of whether the data comes from laser scanners or RGB-D sensors. The proposed framework, SEGCloud, integrates the predictive power of 3D Fully Convolutional Neural Networks (3D-FCNN) with the spatial consistency enforcement of Conditional Random Fields (CRF) using trilinear interpolation (TI) to map coarse voxel predictions back to the fine-grained 3D point level.

Framework Overview

The SEGCloud architecture consists of three primary components:

1. 3D-Fully Convolutional Neural Network (3D-FCNN):
   • The 3D-FCNN processes voxelized input data to produce semantic class probabilities at the voxel level. The proposed network architecture uses residual layers sandwiched between convolutional layers, which efficiently handle 3D input data and downsample the input volume while learning high-level features in a fully convolutional manner, permitting operation on arbitrarily sized input.
2. Trilinear Interpolation (TI):
   • Coarse voxel-level predictions from the 3D-FCNN are translated back to the original 3D points using trilinear interpolation. This step ensures the fine-grained resolution of the original data is maintained while leveraging the high-level features captured at the voxel level.
3. Fully Connected Conditional Random Fields (FC-CRF):
   • The CRF component enforces the spatial consistency of the labels. Implemented as a differentiable Recurrent Neural Network (CRF-RNN), it allows for the end-to-end training of the entire framework. The CRF leverages both the initial predictions and the spatial relationships between points, using a combination of spatial and bilateral terms in the potential functions.

Experimental Evaluation

The framework was tested on four distinct datasets - NYU V2, S3DIS, KITTI, and Semantic3D.net - representing diverse indoor and outdoor scenes. Across these datasets, SEGCloud was shown to achieve performance on par or superior to state-of-the-art methods in 3D semantic segmentation. Key results from these evaluations include:

1. NYU V2:
   • Achieved a mean Intersection over Union (mIOU) of 43.45% and a mean accuracy (mAcc) of 56.43%.
2. Stanford Large-Scale 3D Indoor Spaces Dataset (S3DIS):
   • Reported 48.92% mIOU and 57.35% mAcc, showing clear advantages over previous methods such as PointNet.
3. KITTI:
   • Performance with mIOU of 36.78% and mAcc of 49.46%, consistent with the complexity of outdoor traffic environments.
4. Semantic3D.net:
   • Observed an impressive 61.30% mIOU and 73.08% mAcc, underscoring the robustness of SEGCloud to handle very large-scale datasets.

Contributions and Implications

The contributions of SEGCloud are multi-faceted:

• Integration of 3D-FCNN and CRF: By combining the strengths of 3D-FCNNs with the spatial coherence modeling of CRFs, SEGCloud is able to offer fine-grained 3D semantic segmentation that preserves detailed object boundaries.
• Effective Data Augmentation: The use of on-the-fly augmentation techniques, including geometric transformations and point sub-sampling, ensures the network generalizes well without overfitting.
• Scalability: SEGCloud is designed to scale efficiently with large and diverse datasets, a necessary feature for operational real-world applications in areas such as autonomous driving and augmented reality.

Future Prospects

The proposed method demonstrates significant potential for further advancements in 3D scene understanding. Future developments could explore the use of sparse convolutions to handle the sparsity of 3D data more effectively, potentially leading to additional performance gains. Moreover, there is scope for enhancing the CRF component by refining the bandwidth parameters and potentially incorporating more sophisticated feature correlations.

Conclusion

SEGCloud presents a cohesive and robust framework for the semantic segmentation of 3D point clouds, leveraging a synergy of neural networks, interpolation techniques, and graphical models. Its superior performance on multiple diverse datasets attests to its effectiveness and promise for future advancements in 3D semantic segmentation. This framework, open to further refinement and development, sets a solid foundation for ongoing research in 3D computer vision applications.