SOTAVerified

Point cloud reconstruction

This task aims to solve inherent problems in raw point clouds: sparsity, noise, and irregularity.

Papers

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Showing 11–20 of 101 papers

TitleStatusHype
Energy-Based Sliced Wasserstein DistanceCode1
Query6DoF: Learning Sparse Queries as Implicit Shape Prior for Category-Level 6DoF Pose EstimationCode1
Sparse2Dense: Learning to Densify 3D Features for 3D Object DetectionCode1
Cross-modal Learning for Image-Guided Point Cloud Shape CompletionCode1
Masked Surfel Prediction for Self-Supervised Point Cloud LearningCode1
Masked Autoencoders in 3D Point Cloud Representation LearningCode1
Autoregressive 3D Shape Generation via Canonical MappingCode1
Local and Global Point Cloud Reconstruction for 3D Hand Pose EstimationCode1
PointMixer: MLP-Mixer for Point Cloud UnderstandingCode1
Patch-Based Deep Autoencoder for Point Cloud Geometry CompressionCode1
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