HexPlane Representation for 3D Semantic Scene Understanding
This addresses efficient and accurate 3D scene understanding for applications like robotics and autonomous driving, though it appears incremental as it builds on existing representations.
The paper tackles 3D semantic scene understanding by introducing the HexPlane representation, which projects point clouds into six planes and fuses features to achieve competitive results, such as 77.0 mIoU on ScanNet, surpassing Point Transformer V2 by 1.6 mIoU.
In this paper, we introduce the HexPlane representation for 3D semantic scene understanding. Specifically, we first design the View Projection Module (VPM) to project the 3D point cloud into six planes to maximally retain the original spatial information. Features of six planes are extracted by the 2D encoder and sent to the HexPlane Association Module (HAM) to adaptively fuse the most informative information for each point. The fused point features are further fed to the task head to yield the ultimate predictions. Compared to the popular point and voxel representation, the HexPlane representation is efficient and can utilize highly optimized 2D operations to process sparse and unordered 3D point clouds. It can also leverage off-the-shelf 2D models, network weights, and training recipes to achieve accurate scene understanding in 3D space. On ScanNet and SemanticKITTI benchmarks, our algorithm, dubbed HexNet3D, achieves competitive performance with previous algorithms. In particular, on the ScanNet 3D segmentation task, our method obtains 77.0 mIoU on the validation set, surpassing Point Transformer V2 by 1.6 mIoU. We also observe encouraging results in indoor 3D detection tasks. Note that our method can be seamlessly integrated into existing voxel-based, point-based, and range-based approaches and brings considerable gains without bells and whistles. The codes will be available upon publication.