11,841 research outputs found
Few-Shot 3D Point Cloud Semantic Segmentation via Stratified Class-Specific Attention Based Transformer Network
3D point cloud semantic segmentation aims to group all points into different
semantic categories, which benefits important applications such as point cloud
scene reconstruction and understanding. Existing supervised point cloud
semantic segmentation methods usually require large-scale annotated point
clouds for training and cannot handle new categories. While a few-shot learning
method was proposed recently to address these two problems, it suffers from
high computational complexity caused by graph construction and inability to
learn fine-grained relationships among points due to the use of pooling
operations. In this paper, we further address these problems by developing a
new multi-layer transformer network for few-shot point cloud semantic
segmentation. In the proposed network, the query point cloud features are
aggregated based on the class-specific support features in different scales.
Without using pooling operations, our method makes full use of all pixel-level
features from the support samples. By better leveraging the support features
for few-shot learning, the proposed method achieves the new state-of-the-art
performance, with 15\% less inference time, over existing few-shot 3D point
cloud segmentation models on the S3DIS dataset and the ScanNet dataset
3D-BEVIS: Bird's-Eye-View Instance Segmentation
Recent deep learning models achieve impressive results on 3D scene analysis
tasks by operating directly on unstructured point clouds. A lot of progress was
made in the field of object classification and semantic segmentation. However,
the task of instance segmentation is less explored. In this work, we present
3D-BEVIS, a deep learning framework for 3D semantic instance segmentation on
point clouds. Following the idea of previous proposal-free instance
segmentation approaches, our model learns a feature embedding and groups the
obtained feature space into semantic instances. Current point-based methods
scale linearly with the number of points by processing local sub-parts of a
scene individually. However, to perform instance segmentation by clustering,
globally consistent features are required. Therefore, we propose to combine
local point geometry with global context information from an intermediate
bird's-eye view representation.Comment: camera-ready version for GCPR '1
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