Deep_Object_Pose
Hierarchical-Localization
Deep_Object_Pose | Hierarchical-Localization | |
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3 | 5 | |
1,028 | 3,237 | |
0.5% | 1.9% | |
7.8 | 5.5 | |
3 months ago | 29 days ago | |
Python | Python | |
GNU General Public License v3.0 or later | Apache License 2.0 |
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Deep_Object_Pose
- FLaNK Stack 29 Jan 2024
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6D object pose estimation by known 3d model
I've been doing some research in this area and there are a few deep learning solutions to this problem. For example, NVIDIA's Deep Object Pose Estimation will estimate the 6DOF pose of a known object. But you'll have to train the network if you want to detect a new object. PoseCNN, which someone else mentioned, does a similar thing. CenterPose is more interesting, as it can estimate then pose of an object from a known category; e.g. sneakers, or laptops, rather that one specific object (as DOPE and PoseCNN do).
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Machine Learning Workshop tonight 8-9pm hosted by Underwater Robotics!
For our last event of ArchE Week, the Ohio State Underwater Robotics Team (Website, Instagram) is hosting a workshop tonight on machine learning! The workshop is an interactive walkthrough of using machine learning solutions to make predictions. Some example problems we could be trying to solve are predicting a grade, predicting the weather, and the classic recognize a digit problem. Our team personally uses machine learning to do real-time object detection with YOLO and NVidia DOPE, so we may touch on that as well!
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.
What are some alternatives?
reor - Private & local AI personal knowledge management app for high entropy people.
colmap - COLMAP - Structure-from-Motion and Multi-View Stereo
PoseCNN-PyTorch - PyTorch implementation of the PoseCNN framework
LoFTR - Code for "LoFTR: Detector-Free Local Feature Matching with Transformers", CVPR 2021, T-PAMI 2022
CenterPose - Single-Stage Keypoint-based Category-level Object Pose Estimation from an RGB Image (ICRA 2022)
nerfmm - (Arxiv 2021) NeRF--: Neural Radiance Fields Without Known Camera Parameters
llm-classifier - Classify data instantly using an LLM
hdl_localization - Real-time 3D localization using a (velodyne) 3D LIDAR
2021_ML_Workshop - 2021 ML Workshop
aquamam - An autoregressive, quaternion manifold model for rapidly estimating complex SO(3) distributions.
java-snapshot-testing - Facebook style snapshot testing for JAVA Tests
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.