We present CIRCLE, a framework for large-scale scene completion and geometric refinement based on local implicit signed distance functions.
Humans can naturally and effectively find salient regions in complex scenes.
Meshes with arbitrary connectivity can be remeshed to hold Loop subdivision sequence connectivity via self-parameterization, making SubdivNet a general approach.
In the first week of May, 2021, researchers from four different institutions: Google, Tsinghua University, Oxford University and Facebook, shared their latest work [16, 7, 12, 17] on arXiv. org almost at the same time, each proposing new learning architectures, consisting mainly of linear layers, claiming them to be comparable, or even superior to convolutional-based models.
Only query coordinates with high uncertainties are forwarded to the next level to a bigger neural network with a more powerful representational capability.
Attention mechanisms, especially self-attention, have played an increasingly important role in deep feature representation for visual tasks.
Ranked #10 on Semantic Segmentation on Cityscapes val
It is inherently permutation invariant for processing a sequence of points, making it well-suited for point cloud learning.
Ranked #11 on 3D Part Segmentation on ShapeNet-Part (Instance Average IoU metric)
Experimental results show that Alt-ConvLSTM efficiently models the material kinetic features and greatly outperforms vanilla ConvLSTM with only the single state update.
We present ClusterVO, a stereo Visual Odometry which simultaneously clusters and estimates the motion of both ego and surrounding rigid clusters/objects.
Taking into account the category-independent property of each target, we design a single stage salient instance segmentation framework, with a novel segmentation branch.