LiDARNet: A Boundary-Aware Domain Adaptation Model for Point Cloud Semantic Segmentation

2 Mar 2020 Peng Jiang Srikanth Saripalli

We present a boundary-aware domain adaptation model for LiDAR scan full-scene semantic segmentation (LiDARNet). Our model can extract both the domain private features and the domain shared features with a two branch structure... (read more)

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Datasets


Introduced in the Paper:

SemanticUSL

Mentioned in the Paper:

SemanticKITTI A2D2 SemanticPOSS

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Methods used in the Paper


METHOD TYPE
Batch Normalization
Normalization
Residual Connection
Skip Connections
PatchGAN
Discriminators
ReLU
Activation Functions
Tanh Activation
Activation Functions
Residual Block
Skip Connection Blocks
Instance Normalization
Normalization
Convolution
Convolutions
Leaky ReLU
Activation Functions
Sigmoid Activation
Activation Functions
GAN Least Squares Loss
Loss Functions
Cycle Consistency Loss
Loss Functions
CycleGAN
Generative Models