Surface reconstruction even when occlusion by using density with volume rendering with NeuS NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstruction arXiv paper abstract https://arxiv.org/abs/2106.10689 arXiv PDF paper https://arxiv.org/pdf/2106.10689.pdf GitHub https://github.com/Totoro97/NeuS … present a novel neural surface reconstruction method, called NeuS, for reconstructing objects and scenes with…

Survey of surface reconstruction from point clouds with Sulzer A Survey and Benchmark of Automatic Surface Reconstruction from Point Clouds arXiv paper abstract https://arxiv.org/abs/2301.13656 arXiv PDF paper https://arxiv.org/pdf/2301.13656.pdf GitHub https://github.com/raphaelsulzer/dsr-benchmark … survey and benchmark traditional and novel learning-based algorithms that address the problem of surface reconstruction from point clouds.

Monocular 3D reconstruction using Chamfer distance twice with CD2 CD2: Fine-grained 3D Mesh Reconstruction With Twice Chamfer Distance arXiv paper abstract https://arxiv.org/abs/2206.00447 arXiv PDF paper https://arxiv.org/pdf/2206.00447.pdf GitHub https://github.com/ThibaultGROUEIX/ChamferDistancePytorch Monocular 3D reconstruction is to reconstruct the shape of object and its other information from a single RGB image.

Survey of visual anomaly detection in industrial manufacturing using deep learning Deep Visual Anomaly Detection in Industrial Manufacturing: A Survey arXiv paper abstract https://arxiv.org/abs/2301.11514 arXiv PDF paper https://arxiv.org/pdf/2301.11514.pdf Project page https://github.com/M-3LAB/awesome-industrial-anomaly-detection The recent rapid development of deep learning has laid a milestone in visual anomaly detection (VAD).

Detect and segment unknown objects with unsupervised learning using graph partitioning with CutLER Cut and Learn for Unsupervised Object Detection and Instance Segmentation arXiv paper abstract https://arxiv.org/abs/2301.11320 arXiv PDF paper https://arxiv.org/pdf/2301.11320.pdf GitHub https://github.com/facebookresearch/cutler … propose Cut-and-LEaRn (CutLER), a simple approach for training unsupervised object detection and segmentation models.

Get object pose that is consistent with the input and is unique with C-3PO Certifiable 3D Object Pose Estimation: Foundations, Learning Models, and Self-Training arXiv paper abstract https://arxiv.org/abs/2206.11215 arXiv PDF paper https://arxiv.org/pdf/2206.11215.pdf GitHub https://github.com/mit-spark/c-3po … consider a certifiable object pose estimation problem … given a partial point cloud of an…

Point cloud analysis with unsupervised learning using simple contrastive learning with Jiang Unsupervised Contrastive Learning with Simple Transformation for 3D Point Cloud Data arXiv paper abstract https://arxiv.org/abs/2110.06632 arXiv PDF paper https://arxiv.org/pdf/2110.06632.pdf Though a number of point cloud learning methods have been proposed to handle unordered points, most of them are…

Detect unknown objects and attributes better by jointly training on both tasks with OvarNet OvarNet: Towards Open-vocabulary Object Attribute Recognition arXiv paper abstract https://arxiv.org/abs/2301.09506 arXiv PDF paper https://arxiv.org/pdf/2301.09506.pdf … consider … detecting objects and inferring their visual attributes in an image, even for those with no manual annotations provided at…

Unsupervised monocular depth estimation by optimizing with knowledge from Flow-Net with FG-Depth FG-Depth: Flow-Guided Unsupervised Monocular Depth Estimation arXiv paper abstract https://arxiv.org/abs/2301.08414 arXiv PDF paper https://arxiv.org/pdf/2301.08414.pdf The great potential of unsupervised monocular depth estimation has been demonstrated by many works due to low annotation cost and impressive accuracy comparable to…