Open-source alternatives to Refign

PointNet is a deep learning architecture designed to process and classify raw 3D point clouds directly without voxelization. It provides a system for 3D object classification, semantic segmentation frameworks for partitioning clouds into categories, and tools for visualizing 3D shapes. The project utilizes a transform network to align point clouds into a canonical coordinate space and employs symmetric-function-based aggregation to condense point-wise features into global vectors regardless of point order. It also features a multi-scale grouping architecture to extract hierarchical geometric

PointNet++ is a deep learning framework designed for processing and classifying 3D point cloud data. It utilizes a hierarchical feature learning architecture to extract geometric patterns from sampled 3D point sets. The framework implements a variety of 3D analysis tools, including a point cloud classifier for categorizing objects based on spatial coordinates and surface normals, a semantic scene segmenter for labeling surfaces in large-scale environments, and a tool for 3D object part segmentation. The system covers a broad range of capabilities including geometric feature extraction, 3D da

Official Implementation of PiPa: Pixel- and Patch-wise Self-supervised Learning for Domain Adaptative Semantic Segmentation

Hey guys! Welcome to this repository of our paper 'Phase Consistent Ecological Domain Adaptation', which is accepted by CVPR 2020. First things first, here's a link to our paper. Make sure to read it and you will like it. Currently we are organizing the code and will make it public available…

Official implementation for our ECCV 2022 paper "DecoupleNet: Decoupled Network for Domain Adaptive Semantic Segmentation"

PointConv: Deep Convolutional Networks on 3D Point Clouds. CVPR 2019 Wenxuan Wu, Zhongang Qi, Li Fuxin.

Implementation for PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentation (CVPR 2020)

Unsupervised Intra-domain Adaptation for Semantic Segmentation through Self-Supervision (CVPR 2020 Oral)

CVPR 2021 & IJCV 2024 Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers

Properties of dilated convolution are discussed in our ICLR 2016 conference paper. This repository contains the network definitions and the trained models. You can use this code together with vanilla Caffe to segment images using the pre-trained models. If you want to train the models yourself,…

TITAN-Net introduces a fresh, effective approach for bridging the gap between sensor modalities with different data formats! 🌉 By harnessing the power of scene semantics, TITAN-Net can, for the very first time, synthesize a panoramic color image directly from a 3D LiDAR point cloud.

Style-Hallucinated Dual Consistency Learning for Domain Generalized Semantic Segmentation. ECCV 2022

by Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, Jiaya Jia, details are in project page.

DeconvNet : Learning Deconvolution Network for Semantic Segmentation

The code and trained models of: Affinity Space Adaptation for Semantic Segmentation Across Domains.

Jun Fu, Jing Liu, Haijie Tian, Yong Li, Yongjun Bao, Zhiwei Fang,and Hanqing Lu

Shape Projective Fully Convolutional Network

Pytorch Implementation for NeurIPS (oral) paper: Pixel Level Cycle Association: A New Perspective for Domain Adaptive Semantic Segmentation

Seed, Expand, Constrain: Three Principles for Weakly-Supervised Image Segmentation

IJCAI2020 & IJCV2021 :city_sunrise: Unsupervised Scene Adaptation with Memory Regularization in vivo

CVPR22 Official Implementation of DAFormer: Improving Network Architectures and Training Strategies for Domain-Adaptive Semantic Segmentation

ECCV22 Official Implementation of HRDA: Context-Aware High-Resolution Domain-Adaptive Semantic Segmentation

CVPR23 Official Implementation of MIC: Masked Image Consistency for Context-Enhanced Domain Adaptation

Code for Constructing Self-motivated Pyramid Curriculums for Cross-Domain Semantic Segmentation: A Non-Adversarial Approach