277 Open source projects that are alternatives of or similar to attMPTI

Paper list of deep learning on point clouds.

Learning low-shot object classification with explicit shape bias learned from point clouds

Papers and Datasets about Point Cloud.

Associatively Segmenting Instances and Semantics in Point Clouds, CVPR 2019

DBNet: A Large-Scale Dataset for Driving Behavior Learning, CVPR 2018

Implementation of the Point Transformer layer, in Pytorch

finetune bert for small dataset text classification in a few-shot learning manner using ProtoNet

Research platform for 3D object detection in PyTorch.

A novel method to tune language models. Codes and datasets for paper ``GPT understands, too''.

Python binding of 3D visualization library Pangolin

Neural Point-Based Graphics

Code for A GRAPH-CNN FOR 3D POINT CLOUD CLASSIFICATION (ICASSP 2018)

Code for ACL'19 "Few-Shot Representation Learning for Out-Of-Vocabulary Words"

orb-slam2 with semantic label

Few-shot Keyword Spotting in Any Language and Multilingual Spoken Word Corpus

3D Bounding Box Annotation Tool (3D-BAT) Point cloud and Image Labeling

[ECCV 2020] Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution

Differentiable Surface Splatting

Learning To Compare For Text , Few shot learning in text classification

Paperlist of awesome 3D detection methods

Rank 1st in the leaderboard of SemanticKITTI semantic segmentation (both single-scan and multi-scan) (Nov. 2020) (CVPR2021 Oral)

An integrated SfM (Structure from Motion) and MVS (Multi-View Stereo) solution.

Awesome Few-shot learning

[CVPR'19] JSIS3D: Joint Semantic-Instance Segmentation of 3D Point Clouds

The public CGAL repository, see the README below

code/webpage for the DeepMapping project

C++ library and programs for reading and writing ASPRS LAS format with LiDAR data

LibFewShot: A Comprehensive Library for Few-shot Learning.

The PyTorch Implementation of F-ConvNet for 3D Object Detection

[CVPR 2021] Exploiting Edge-Oriented Reasoning for 3D Point-based Scene Graph Analysis (official pytorch implementation)

Morphing and Sampling Network for Dense Point Cloud Completion (AAAI2020)

📄 Spatial Contrastive Learning for Few-Shot Classification (ECML/PKDD 2021).

3D Graph Neural Networks for RGBD Semantic Segmentation

MinkLoc++: Lidar and Monocular Image Fusion for Place Recognition

L-CAS 3D Point Cloud Annotation Tool

FlowNet3D: Learning Scene Flow in 3D Point Clouds (CVPR 2019)

A hackable lidar viewer

Linked Dynamic Graph CNN: Learning through Point Cloud by Linking Hierarchical Features

The open source mesh processing system

Code for PCN: Point Completion Network in 3DV'18 (Oral)

A high-performance neural network library for point cloud processing.

No description or website provided.

Point cloud semantic segmentation via Deep 3D Convolutional Neural Network

Robotics with GPU computing

PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space

PyTorch implementation of 'Squeeze and Excite' Guided Few Shot Segmentation of Volumetric Scans

PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling （CVPR 2020）

PointNetVLAD: Deep Point Cloud Based Retrieval for Large-Scale Place Recognition, CVPR 2018

A PyTorch implementation of Dynamic Graph CNN for Learning on Point Clouds (DGCNN)

Julia bindings for the displaz lidar viewer

This is a package for extrinsic calibration between a 3D LiDAR and a camera, described in paper: Improvements to Target-Based 3D LiDAR to Camera Calibration. This package is used for Cassie Blue's 3D LiDAR semantic mapping and automation.

Visualising LIDAR data from KITTI dataset.

Grid-GCN for Fast and Scalable Point Cloud Learning

The implementation of 3D-UNet using PyTorch

Light-weight camera LiDAR calibration package for ROS using OpenCV and PCL (PnP + LM optimization)

Python bindings for the Point Cloud Library (PCL)

Style-based Point Generator with Adversarial Rendering for Point Cloud Completion (CVPR 2021)

Experiments on unsupervised point cloud reconstruction.

awesome few shot / meta learning papers