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Found 4 papers, 1 papers with code
ShapeConv: Shape-aware Convolutional Layer for Indoor RGB-D Semantic Segmentation
The reason is that the learnt weights for balancing the importance between the shape and base components in ShapeConv become constants in the inference phase, and thus can be fused into the following convolution, resulting in a network that is identical to one with vanilla convolutional layers.
Ranked #3 on
Semantic Segmentation
on Stanford2D3D - RGBD
Hausdorff Point Convolution with Geometric Priors
We show that HPC constitutes a powerful point feature learning with a rather compact set of only four types of geometric priors as kernels.
Specular-to-Diffuse Translation for Multi-View Reconstruction
To alleviate this restriction, we introduce S2Dnet, a generative adversarial network for transferring multiple views of objects with specular reflection into diffuse ones, so that multi-view reconstruction methods can be applied more effectively.
DiDA: Disentangled Synthesis for Domain Adaptation
The key idea is that by learning to separately extract both the common and the domain-specific features, one can synthesize more target domain data with supervision, thereby boosting the domain adaptation performance.
