3D Part Segmentation
44 papers with code • 2 benchmarks • 2 datasets
Segmenting 3D object parts
( Image credit: MeshCNN: A Network with an Edge )
By exploiting metric space distances, our network is able to learn local features with increasing contextual scales.
Besides, our experiments converting CIFAR-10 into a point cloud showed that networks built on PointConv can match the performance of convolutional networks in 2D images of a similar structure.
For example, on the challenging S3DIS dataset for large-scale semantic scene segmentation, the Point Transformer attains an mIoU of 70. 4% on Area 5, outperforming the strongest prior model by 3. 3 absolute percentage points and crossing the 70% mIoU threshold for the first time.
Specifically, the convolutional weight for local point set is forced to learn a high-level relation expression from predefined geometric priors, between a sampled point from this point set and the others.