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Found 7 papers, 7 papers with code
Swin3D++: Effective Multi-Source Pretraining for 3D Indoor Scene Understanding
Data diversity and abundance are essential for improving the performance and generalization of models in natural language processing and 2D vision.
Swin3D: A Pretrained Transformer Backbone for 3D Indoor Scene Understanding
We pretrained a large {\SST} model on a synthetic Structured3D dataset, which is an order of magnitude larger than the ScanNet dataset.
Ranked #3 on
3D Object Detection
on S3DIS
(using extra training data)
Semi-supervised 3D shape segmentation with multilevel consistency and part substitution
We propose an effective semi-supervised method for learning 3D segmentations from a few labeled 3D shapes and a large amount of unlabeled 3D data.
Interpolation-Aware Padding for 3D Sparse Convolutional Neural Networks
For fine-grained 3D vision tasks where point-wise features are essential, like semantic segmentation and 3D detection, our network achieves higher prediction accuracy than the existing networks using the nearest neighbor interpolation or the normalized trilinear interpolation with the zero-padding or the octree-padding scheme.
Spline Positional Encoding for Learning 3D Implicit Signed Distance Fields
Multilayer perceptrons (MLPs) have been successfully used to represent 3D shapes implicitly and compactly, by mapping 3D coordinates to the corresponding signed distance values or occupancy values.
Unsupervised 3D Learning for Shape Analysis via Multiresolution Instance Discrimination
Although unsupervised feature learning has demonstrated its advantages to reducing the workload of data labeling and network design in many fields, existing unsupervised 3D learning methods still cannot offer a generic network for various shape analysis tasks with competitive performance to supervised methods.
Ranked #2 on
3D Semantic Segmentation
on PartNet
3D Point Cloud Linear Classification
3D Semantic Segmentation
PFCNN: Convolutional Neural Networks on 3D Surfaces Using Parallel Frames
Surface meshes are widely used shape representations and capture finer geometry data than point clouds or volumetric grids, but are challenging to apply CNNs directly due to their non-Euclidean structure.
Ranked #29 on
Semantic Segmentation
on ScanNet
(test mIoU metric)
