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
Latest papers with no code
Gaga: Group Any Gaussians via 3D-aware Memory Bank
We introduce Gaga, a framework that reconstructs and segments open-world 3D scenes by leveraging inconsistent 2D masks predicted by zero-shot segmentation models.
OpenNeRF: Open Set 3D Neural Scene Segmentation with Pixel-Wise Features and Rendered Novel Views
Our OpenNeRF further leverages NeRF's ability to render novel views and extract open-set VLM features from areas that are not well observed in the initial posed images.
BEVCar: Camera-Radar Fusion for BEV Map and Object Segmentation
Semantic scene segmentation from a bird's-eye-view (BEV) perspective plays a crucial role in facilitating planning and decision-making for mobile robots.
PointSeg: A Training-Free Paradigm for 3D Scene Segmentation via Foundation Models
On top of that, PointSeg can incorporate with various segmentation models and even surpasses the supervised methods.
DA-BEV: Unsupervised Domain Adaptation for Bird's Eye View Perception
Camera-only Bird's Eye View (BEV) has demonstrated great potential in environment perception in a 3D space.
CoSSegGaussians: Compact and Swift Scene Segmenting 3D Gaussians with Dual Feature Fusion
We propose Compact and Swift Segmenting 3D Gaussians(CoSSegGaussians), a method for compact 3D-consistent scene segmentation at fast rendering speed with only RGB images input.
Segment3D: Learning Fine-Grained Class-Agnostic 3D Segmentation without Manual Labels
Therefore, we explore the use of image segmentation foundation models to automatically generate training labels for 3D segmentation.
Cataract-1K: Cataract Surgery Dataset for Scene Segmentation, Phase Recognition, and Irregularity Detection
Besides, we initiate the research on domain adaptation for instrument segmentation in cataract surgery by evaluating cross-domain instrument segmentation performance in cataract surgery videos.
FALCON: Fairness Learning via Contrastive Attention Approach to Continual Semantic Scene Understanding in Open World
In particular, we first introduce a new Fairness Contrastive Clustering loss to address the problems of catastrophic forgetting and fairness.
Transferring to Real-World Layouts: A Depth-aware Framework for Scene Adaptation
Based on such observation, we propose a depth-aware framework to explicitly leverage depth estimation to mix the categories and facilitate the two complementary tasks, i. e., segmentation and depth learning in an end-to-end manner.
