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Found 11 papers, 6 papers with code
Instance-aware 3D Semantic Segmentation powered by Shape Generators and Classifiers
In the experiments, our method significantly outperform existing approaches in 3D semantic segmentation on several public benchmarks, such as Waymo Open Dataset, SemanticKITTI and ScanNetV2.
GenCorres: Consistent Shape Matching via Coupled Implicit-Explicit Shape Generative Models
GenCorres addresses this issue by learning an implicit generator from the input shapes, which provides intermediate shapes between two arbitrary shapes.
LiDAR-Based 3D Object Detection via Hybrid 2D Semantic Scene Generation
Bird's-Eye View (BEV) features are popular intermediate scene representations shared by the 3D backbone and the detector head in LiDAR-based object detectors.
ARAPReg: An As-Rigid-As Possible Regularization Loss for Learning Deformable Shape Generators
Our approach builds on an approximation of the as-rigid-as possible (or ARAP) deformation energy.
Dense Correspondences between Human Bodies via Learning Transformation Synchronization on Graphs
We introduce an approach for establishing dense correspondences between partial scans of human models and a complete template model.
Joint Learning of Neural Networks via Iterative Reweighted Least Squares
We formulate this problem as joint learning of multiple copies of the same network architecture and enforce the network weights to be shared across these networks.
Learning Transformation Synchronization
Our approach alternates between transformation synchronization using weighted relative transformations and predicting new weights of the input relative transformations using a neural network.
Joint Map and Symmetry Synchronization
Most existing techniques in map computation (e. g., in the form of feature or dense correspondences) assume that the underlying map between an object pair is unique.
Translation Synchronization via Truncated Least Squares
In this paper, we introduce a robust algorithm, \textsl{TranSync}, for the 1D translation synchronization problem, in which the aim is to recover the global coordinates of a set of nodes from noisy measurements of relative coordinates along an observation graph.
Dual Decomposed Learning with Factorwise Oracle for Structural SVM of Large Output Domain
In this work, we show that, by decomposing training of Structural Support Vector Machine (SVM) into a series of multiclass SVM problems connected through messages, one can replace expensive structured oracle with Factorwise Maximization Oracle (FMO) that allows efficient implementation of complexity sublinear to the factor domain.
PD-Sparse : A Primal and Dual Sparse Approach to Extreme Multiclass and Multilabel Classification
In this work, we show that a margin-maximizing loss with l1 penalty, in case of Extreme Classification, yields extremely sparse solution both in primal and in dual without sacrificing the expressive power of predictor.
