Open-source alternatives to Dilation

DeconvNet : Learning Deconvolution Network for Semantic Segmentation

Pytorch Implementation -- All about Structure: Adapting Structural Information across Domains for Boosting Semantic Segmentation, CVPR 2019

TPAMI 2023 ESI Highly Cited Paper SePiCo: Semantic-Guided Pixel Contrast for Domain Adaptive Semantic Segmentation https://arxiv.org/abs/2204.08808

Keras implementation of Deeplab v3+ with pretrained weights

Refign: Align and Refine for Adaptation of Semantic Segmentation to Adverse Conditions WACV 2023

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

Mask RCNN in TensorFlow

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

Learning to Reconstruct 3D Non-Cuboid Room Layout from a Single RGB Image

Yolact is a computer vision framework and real-time instance segmentation model. It utilizes a fully convolutional neural network to detect objects and generate pixel-level masks for images and video feeds. The system employs prototypical mask generation to create global mask prototypes that are linearly combined for instance-specific results. It incorporates deformable convolutional layers and deformable region-of-interest pooling to adapt spatial sampling to the irregular shapes of objects. The framework covers the full model development lifecycle, including training on custom datasets, ac

Implementation of various Deep Image Segmentation models in keras.

Created by Charles R. Qi , Wei Liu , Chenxia Wu , Hao Su and Leonidas J. Guibas from Stanford University and Nuro Inc.