Scene Segmentation
120 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.
Benchmarks
These leaderboards are used to track progress in Scene Segmentation
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Libraries
Use these libraries to find Scene Segmentation models and implementations
ScanNet
NYUv2
SUN RGB-D
UAVid
StreetHazards
Mila Simulated Floods
Berkeley DeepDrive Video
Most implemented papers
Mesh Convolution with Continuous Filters for 3D Surface Parsing
In this paper, we propose a series of modular operations for effective geometric feature learning from 3D triangle meshes.
FIFO: Learning Fog-invariant Features for Foggy Scene Segmentation
Robust visual recognition under adverse weather conditions is of great importance in real-world applications.
Rethinking Surgical Instrument Segmentation: A Background Image Can Be All You Need
lofrienger/single_surgicalscene_for_segmentation
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Our empirical analysis suggests that without the high cost of data collection and annotation, we can achieve decent surgical instrument segmentation performance.
Improving Nighttime Driving-Scene Segmentation via Dual Image-adaptive Learnable Filters
wenyyu/IA-Seg
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With DIAL-Filters, we design both unsupervised and supervised frameworks for nighttime driving-scene segmentation, which can be trained in an end-to-end manner.
Neural Implicit Vision-Language Feature Fields
In this work, we present a zero-shot volumetric open-vocabulary semantic scene segmentation method.
Parsing Natural Scenes and Natural Language with Recursive Neural Networks
Recursive structure is commonly found in the inputs of different modalities such as natural scene images or natural language sentences. Discovering this recursive structure helps us to not only identify the units that an image or sentence contains but also how they interact to form a whole.
Learning Rich Features from RGB-D Images for Object Detection and Segmentation
In this paper we study the problem of object detection for RGB-D images using semantically rich image and depth features.
A Deep Siamese Network for Scene Detection in Broadcast Videos
Nikhil9786/Scene-Segmentation-Using-MovieScenes-Dataset
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We present a model that automatically divides broadcast videos into coherent scenes by learning a distance measure between shots.
Dirty Pixels: Towards End-to-End Image Processing and Perception
princeton-computational-imaging/DirtyPixels
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As such, conventional imaging involves processing the RAW sensor measurements in a sequential pipeline of steps, such as demosaicking, denoising, deblurring, tone-mapping and compression.
A Design Methodology for Efficient Implementation of Deconvolutional Neural Networks on an FPGA
In support of such applications, various FPGA accelerator architectures have been proposed for convolutional neural networks (CNNs) that enable high performance for classification tasks at lower power than CPU and GPU processors.
