Hierarchical-Localization VS Deep_Object_Pose

Compare Hierarchical-Localization vs Deep_Object_Pose and see what are their differences.

Deep_Object_Pose

Deep Object Pose Estimation (DOPE) – ROS inference (CoRL 2018) (by NVlabs)
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Hierarchical-Localization Deep_Object_Pose
5 3
2,754 951
3.6% 2.2%
7.0 5.2
18 days ago 8 days ago
Python Python
Apache License 2.0 GNU General Public License v3.0 or later
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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Hierarchical-Localization

Posts with mentions or reviews of Hierarchical-Localization. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-03-26.
  • What algorithms should I look at if I'm interested in SLAM-like navigation, but with 3-D map foreknowledge?
    3 projects | /r/computervision | 26 Mar 2023
    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.
  • 6D object pose estimation by known 3d model
    5 projects | /r/computervision | 7 Oct 2022
    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.
  • Using Unified Camera Model parameters in COLMAP: Hierarchical Localization
    2 projects | /r/computervision | 24 Jun 2021
    I am looking for some advice on a problem I am running into using this localization algorithm.

Deep_Object_Pose

Posts with mentions or reviews of Deep_Object_Pose. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2024-01-29.
  • FLaNK Stack 29 Jan 2024
    46 projects | dev.to | 29 Jan 2024
  • 6D object pose estimation by known 3d model
    5 projects | /r/computervision | 7 Oct 2022
    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).
  • Machine Learning Workshop tonight 8-9pm hosted by Underwater Robotics!
    2 projects | /r/OSU | 7 Apr 2021
    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!

What are some alternatives?

When comparing Hierarchical-Localization and Deep_Object_Pose you can also consider the following projects:

colmap - COLMAP - Structure-from-Motion and Multi-View Stereo

LoFTR - Code for "LoFTR: Detector-Free Local Feature Matching with Transformers", CVPR 2021, T-PAMI 2022

nerfmm - (Arxiv 2021) NeRF--: Neural Radiance Fields Without Known Camera Parameters

PoseCNN-PyTorch - PyTorch implementation of the PoseCNN framework

reor - Self-organizing AI note-taking app that runs models locally.

hdl_localization - Real-time 3D localization using a (velodyne) 3D LIDAR

CenterPose - Single-Stage Keypoint-based Category-level Object Pose Estimation from an RGB Image (ICRA 2022)

DOLG-pytorch - Unofficial PyTorch Implementation of "DOLG: Single-Stage Image Retrieval with Deep Orthogonal Fusion of Local and Global Features"

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.

iNeRF-public

aquamam - An autoregressive, quaternion manifold model for rapidly estimating complex SO(3) distributions.

lightweight-human-pose-estimation.pytorch - Fast and accurate human pose estimation in PyTorch. Contains implementation of "Real-time 2D Multi-Person Pose Estimation on CPU: Lightweight OpenPose" paper.