Local and Global Point Cloud Reconstruction for 3D Hand Pose Estimation
This paper addresses the 3D point cloud reconstruction and 3D pose estimation of the human hand from a single RGB image. To that end, we present a novel pipeline for local and global point cloud reconstruction using a 3D hand template while learning a latent representation for pose estimation. To demonstrate our method, we introduce a new multi-view hand posture dataset to obtain complete 3D point clouds of the hand in the real world. Experiments on our newly proposed dataset and four public benchmarks demonstrate the model's strengths. Our method outperforms competitors in 3D pose estimation while reconstructing realistic-looking complete 3D hand point clouds.
Code (1)
Tasks
3D Hand Pose Estimation3D Point Cloud Reconstruction3D Pose EstimationHand Pose EstimationPoint cloud reconstructionPose EstimationSimilar Papers 제목 키워드 기반
PPSURF: Combining Patches and Point Convolutions for Detailed Surface Reconstruction
3D surface reconstruction from point clouds is a key step in areas such as content creation, archaeology, digital cultural heritage, and engineering. Current approaches either try to optimize a non-data-driven surface re…
Surface ReconstructionL2G Auto-encoder: Understanding Point Clouds by Local-to-Global Reconstruction with Hierarchical Self-Attention
Auto-encoder is an important architecture to understand point clouds in an encoding and decoding procedure of self reconstruction. Current auto-encoder mainly focuses on the learning of global structure by global shape r…
DecoderRetrievalDense 3D Point Cloud Reconstruction Using a Deep Pyramid Network
Reconstructing a high-resolution 3D model of an object is a challenging task in computer vision. Designing scalable and light-weight architectures is crucial while addressing this problem. Existing point-cloud based reco…
3D Point Cloud ReconstructionPoint cloud reconstructionMulti-Angle Point Cloud-VAE: Unsupervised Feature Learning for 3D Point Clouds from Multiple Angles by Joint Self-Reconstruction and Half-to-Half Prediction
Unsupervised feature learning for point clouds has been vital for large-scale point cloud understanding. Recent deep learning based methods depend on learning global geometry from self-reconstruction. However, these meth…
3D Point Cloud Linear ClassificationUnsupervised 3D Point Cloud Linear EvaluationMitigating Prior Shape Bias in Point Clouds via Differentiable Center Learning
Masked autoencoding and generative pretraining have achieved remarkable success in computer vision and natural language processing, and more recently, they have been extended to the point cloud domain. Nevertheless, exis…