[Paper Reproduction] MimicPlay: Learning Robot Skills from Human Demonstrations
A long-horizon imitation learning method from Fei-Fei Li's team at Stanford University that learns by watching human actions.
Vision
21 posts
Quickly Implementing Three-Dimensional Reconstruction with NeRF (instant-ngp)
Instant-NGP, short for Instant Neural Graphics Primitives, uses multiresolution hash encoding to solve the efficiency problem encountered when parameterizing NeRF with a fully connected neural network. It greatly accelerates network training, reducing three-dimensional reconstruction time from several hours to several seconds.
【Motion Imitation】Robot Motion Planning Methods Learned from Human Demonstrations
A survey of research on methods for robot motion generation and planning learned from videos of humans grasping objects.
【Grasping by Imitation】Learning Robotic Grasping from Human Demonstrations
A survey of research on robotic grasping learned from videos of humans grasping objects.
[Grasp Pose Estimation Algorithm] RGB Matters Paper Notes and Reproduction
Reproducing the RGB Matters algorithm proposed by Shanghai Jiao Tong University.
Survey of Grasp Pose Estimation Algorithms
A survey of grasp pose estimation papers from top conferences and journals over the past three years
[Grasping Algorithm] Contact GraspNet
Reproducing Contact GraspNet, a grasp estimation algorithm proposed by NVIDIA
[6D Pose Estimation] Gen6D Algorithm
Algorithm reproduction
GDRNPP: A 6D Pose Estimation Algorithm
[Paper Notes] 6D Pose Estimation Papers from ECCV2020–2022
Notes from searching for and reading ECCV papers related to 6D pose estimation from the past three years
[Paper Notes] Papers on 6D Pose Estimation at CVPR2020-2022
A review of and notes on CVPR papers related to 6D pose estimation from the past three years
[Paper Notes] Papers Citing DenseFusion | No Code
I searched for papers citing DenseFusion, but so many of them had no open-source code that after reading two, I switched to focusing only on papers from top-tier conferences.
Creating a Custom LINEMOD Dataset (ObjectDatasetTools)
I am currently studying 6D pose estimation algorithms. The LINEMOD dataset is the most commonly used dataset in this field, but testing only on an existing dataset is not enough. Building a custom dataset is necessary for practical applications.
[Paper Notes] FFB6D
FFB6D fuses RGB and point cloud information during the feature extraction stage, then performs instance segmentation and keypoint detection.
[Paper Notes] DenseFusion
This paper proposes a heterogeneous architecture that processes two data sources (RGB and depth maps) separately and uses a novel dense fusion network to extract dense pixel-level features for pose estimation.
[6D Pose Estimation Algorithm] PVNet
Attempted to test PVNet but gave up partway through.
[6D Pose Estimation Algorithm] PoseCNN
Addresses the increased difficulty of 6D pose estimation caused by occlusion and clutter between objects by combining a backbone network with ICP point matching.
[6D Pose Estimation Algorithm] Introduction to LineMod and Code Testing
The LineMod algorithm was proposed by Hinterstoisser et al. in 2011. Using template matching, it addresses real-time detection and 6D pose estimation and localization for low-texture three-dimensional objects in cluttered scenes.
Creating a Three-Dimensional Mesh Model of an Object with COLMAP and OpenMVS
COLMAP is a general-purpose Structure-from-Motion (SfM) and Multi-View Stereo (MVS) tool with graphical and command-line interfaces. OpenMVS is one of the three-dimensional reconstruction frameworks that produces better reconstruction results, and it provides automated scripts.
A Survey of Three-Dimensional Reconstruction Methods
Three-dimensional reconstruction uses a camera to photograph real-world objects and scenes, then processes them with computer vision techniques to obtain a three-dimensional model of the object. The main techniques involved include multi-view stereo, depth map estimation, point cloud processing, mesh reconstruction and optimization, texture mapping, Markov random fields, image segmentation, and more.
Classic Instance Segmentation Model: Mask R-CNN
Mask R-CNN is a major 2017 work by Kaiming He. It performs instance segmentation alongside object detection and achieves excellent results.