3D object pose from video scan without CAD model or training with OnePose OnePose: One-Shot Object Pose Estimation without CAD Models arXiv paper abstract https://arxiv.org/abs/2205.12257 arXiv PDF paper https://arxiv.org/pdf/2205.12257.pdf Project page https://zju3dv.github.io/onepose Twitter video https://twitter.com/XiaoweiZhou5/status/1529415986142916608 … propose a new method named OnePose for object pose estimation.

Explain image classifier by showing features in source and target domains with XSDA-Net Explainable Supervised Domain Adaptation arXiv paper abstract https://arxiv.org/abs/2205.09943 arXiv PDF paper https://arxiv.org/pdf/2205.09943v1.pdf Domain adaptation techniques have contributed to the success of deep learning. Leveraging knowledge from an auxiliary source domain for learning in labeled data-scarce target domain…

Classify small images accurately using little memory and CPU with ImageSig ImageSig: A signature transform for ultra-lightweight image recognition arXiv paper abstract https://arxiv.org/abs/2205.06929v1 arXiv PDF paper https://arxiv.org/pdf/2205.06929v1.pdf This paper introduces a new lightweight method for image recognition. ImageSig is based on computing signatures and does not require a convolutional structure…

Better object segmentation of unknown objects using region awareness with RAML Region-Aware Metric Learning for Open World Semantic Segmentation via Meta-Channel Aggregation arXiv paper abstract https://arxiv.org/abs/2205.08083v1 arXiv PDF paper https://arxiv.org/pdf/2205.08083v1.pdf As one of the most challenging and practical segmentation tasks, open-world semantic segmentation requires the model to segment the anomaly…

3D object reconstruction using a few views despite noisy camera poses with FvOR FvOR: Robust Joint Shape and Pose Optimization for Few-view Object Reconstruction arXiv paper abstract https://arxiv.org/abs/2205.07763 arXiv PDF paper https://arxiv.org/pdf/2205.07763.pdf GitHub https://github.com/zhenpeiyang/FvOR Reconstructing an accurate 3D object model from a few image observations remains a challenging problem in…

Survey of learning from only a few examples with computer vision applications A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunities arXiv paper abstract https://arxiv.org/abs/2205.06743v1 arXiv PDF paper https://arxiv.org/pdf/2205.06743v1.pdf Few-shot learning (FSL) … an effective learning method and shows great potential … learning valid information rapidly from just…

Survey of filling in missing depth data using deep learning Deep Depth Completion: A Survey arXiv paper abstract https://arxiv.org/abs/2205.05335v1 arXiv PDF paper https://arxiv.org/pdf/2205.05335v1.pdf Depth completion aims at predicting dense pixel-wise depth from a sparse map captured from a depth sensor. It plays an essential role in various applications such as…

Improve 3D object reconstruction using unsupervised learning on image collection with TARS Topologically-Aware Deformation Fields for Single-View 3D Reconstruction arXiv paper abstract https://arxiv.org/abs/2205.06267 arXiv PDF paper https://shivamduggal4.github.io/tars-3D/static/paper/main.pdf Supplementary PDF paper https://shivamduggal4.github.io/tars-3D/static/paper/supplementary.pdf Project page https://shivamduggal4.github.io/tars-3D YouTube https://www.youtube.com/watch?v=hSWaN_6Ir1c