Hierarchical-Localization
lightweight-human-pose-estimation.pytorch
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Hierarchical-Localization | lightweight-human-pose-estimation.pytorch | |
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5 | 2 | |
2,816 | 2,022 | |
4.1% | - | |
7.0 | 0.0 | |
8 days ago | about 1 month ago | |
Python | Python | |
Apache License 2.0 | Apache License 2.0 |
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Hierarchical-Localization
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What algorithms should I look at if I'm interested in SLAM-like navigation, but with 3-D map foreknowledge?
You can try hierarchical localization, it's pretty memory efficient since it only brings up relevant point clouds instead of the entire mapped pointed when you're computing poses.
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6D object pose estimation by known 3d model
Sounds like this is a 3D to 2D correspondence estimation problem. So is it correct that you are trying find the pose of the object based on seen 2D images? First you need to define a canonical reference frame for the object. This object reference frame is essentially glued to the object and you want to estimate the object to camera frame transformation matrix which will give you the pose of the object relative to how you are viewing it from a given frame. To achieve this, most literature use some form of 3D to 2D feature correspondence search from which a transformation matrix is obtained using projective geometry. Features like SIFT features can be used to find correspondences between features seen in the 2D image and features in the 3D object. This is also an active area of research in computer vision and the state of the art uses learned deep features. You can check out https://github.com/cvg/Hierarchical-Localization which is the State-of-the-Art in camera 6DOF pose estimation from known 3D models of the world. For your scenario, you just need to define the object coordinate system and you can obtain the pose if you know the object to camera transformations. You should also first look into the classical approaches which use some variants of PNP + RANSAC algorithm to find 2D to 3D correspondences. Since you also know the relative poses of the cameras, you can also do refinement like bundle adjustment to better predict your 2D to 3D correspondences. Let me know if you find any good tutorials or resources online.
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3D object recognition for AR in Unity
Different traditional methods can also be helped by specific ML tasks such as: (a) initial 2D bounding box detection to limit region of 3D pose estimation (b) edge detection (like HED: https://arxiv.org/abs/1504.06375) (c) training on a photogrametry model for more robust retrieval and matching in changing scale and light (like HLoC: https://github.com/cvg/Hierarchical-Localization)
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Automatic Image Registration for big data
I have been very successful with these kind of image pairs using SuperPoint+SuperGlue. The authors‘ work is awesome and code is available here: hloc
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Using Unified Camera Model parameters in COLMAP: Hierarchical Localization
I am looking for some advice on a problem I am running into using this localization algorithm.
lightweight-human-pose-estimation.pytorch
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Help finding an appropriate model for human pose estimation
Lightweight OpenPose: Runs in realtime >20fps confirmed, training code is provided
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How do I properly dissect a Github repo of a ML model?
Using https://github.com/Daniil-Osokin/lightweight-human-pose-estimation.pytorch as an example (or another repo, that was just one I found), could someone please give me a step by step process of how they read a repo for a research paper?
What are some alternatives?
colmap - COLMAP - Structure-from-Motion and Multi-View Stereo
BlazePose-tensorflow - A third-party Tensorflow Implementation for paper "BlazePose: On-device Real-time Body Pose tracking".
LoFTR - Code for "LoFTR: Detector-Free Local Feature Matching with Transformers", CVPR 2021, T-PAMI 2022
openpose - OpenPose: Real-time multi-person keypoint detection library for body, face, hands, and foot estimation
nerfmm - (Arxiv 2021) NeRF--: Neural Radiance Fields Without Known Camera Parameters
AlphaPose - Real-Time and Accurate Full-Body Multi-Person Pose Estimation&Tracking System
Deep_Object_Pose - Deep Object Pose Estimation (DOPE) – ROS inference (CoRL 2018)
openpifpaf - Official implementation of "OpenPifPaf: Composite Fields for Semantic Keypoint Detection and Spatio-Temporal Association" in PyTorch.
hdl_localization - Real-time 3D localization using a (velodyne) 3D LIDAR
DeepLabCut - Official implementation of DeepLabCut: Markerless pose estimation of user-defined features with deep learning for all animals incl. humans
mcl_3dl - A ROS node to perform a probabilistic 3-D/6-DOF localization system for mobile robots with 3-D LIDAR(s). It implements pointcloud based Monte Carlo localization that uses a reference pointcloud as a map.
kapao - KAPAO is an efficient single-stage human pose estimation model that detects keypoints and poses as objects and fuses the detections to predict human poses.