Scene Segmentation

104 papers with code • 5 benchmarks • 7 datasets

Scene segmentation is the task of splitting a scene into its various object components.

Image adapted from Temporally coherent 4D reconstruction of complex dynamic scenes.


Use these libraries to find Scene Segmentation models and implementations

Most implemented papers

SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

PaddlePaddle/PaddleSeg 2 Nov 2015

We show that SegNet provides good performance with competitive inference time and more efficient inference memory-wise as compared to other architectures.

Fully Convolutional Networks for Semantic Segmentation

pytorch/vision CVPR 2015

Convolutional networks are powerful visual models that yield hierarchies of features.

Point Transformer

Pointcept/Pointcept ICCV 2021

For example, on the challenging S3DIS dataset for large-scale semantic scene segmentation, the Point Transformer attains an mIoU of 70. 4% on Area 5, outperforming the strongest prior model by 3. 3 absolute percentage points and crossing the 70% mIoU threshold for the first time.

Dual Attention Network for Scene Segmentation

junfu1115/DANet CVPR 2019

Specifically, we append two types of attention modules on top of traditional dilated FCN, which model the semantic interdependencies in spatial and channel dimensions respectively.

Panoptic Segmentation

cocodataset/panopticapi CVPR 2019

We propose and study a task we name panoptic segmentation (PS).

KPConv: Flexible and Deformable Convolution for Point Clouds

HuguesTHOMAS/KPConv ICCV 2019

Furthermore, these locations are continuous in space and can be learned by the network.

Seamless Scene Segmentation

mapillary/seamseg CVPR 2019

In this work we introduce a novel, CNN-based architecture that can be trained end-to-end to deliver seamless scene segmentation results.

A Local-to-Global Approach to Multi-modal Movie Scene Segmentation

AnyiRao/SceneSeg CVPR 2020

Scene, as the crucial unit of storytelling in movies, contains complex activities of actors and their interactions in a physical environment.

Point-Voxel CNN for Efficient 3D Deep Learning

mit-han-lab/pvcnn NeurIPS 2019

The computation cost and memory footprints of the voxel-based models grow cubically with the input resolution, making it memory-prohibitive to scale up the resolution.