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3D Semantic Segmentation
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3D Semantic Segmentation is a computer vision task that involves dividing a 3D point cloud or 3D mesh into semantically meaningful parts or regions. The goal of 3D semantic segmentation is to identify and label different objects and parts within a 3D scene, which can be used for applications such as robotics, autonomous driving, and augmented reality.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Are We Ready for Real-Time LiDAR Semantic Segmentation in Autonomous Driving?
MOSE: Monocular Semantic Reconstruction Using NeRF-Lifted Noisy Priors
Bayesian Self-Training for Semi-Supervised 3D Segmentation
Cross-Modal Self-Supervised Learning with Effective Contrastive Units for LiDAR Point Clouds
Train Till You Drop: Towards Stable and Robust Source-free Unsupervised 3D Domain Adaptation
Image-to-Lidar Relational Distillation for Autonomous Driving Data
Exploring Scene Affinity for Semi-Supervised LiDAR Semantic Segmentation
vFusedSeg3D: 3rd Place Solution for 2024 Waymo Open Dataset Challenge in Semantic Segmentation
SegStitch: Multidimensional Transformer for Robust and Efficient Medical Imaging Segmentation
AMAES: Augmented Masked Autoencoder Pretraining on Public Brain MRI Data for 3D-Native Segmentation
Augmented Efficiency: Reducing Memory Footprint and Accelerating Inference for 3D Semantic Segmentation through Hybrid Vision
Open-Vocabulary 3D Semantic Segmentation with Text-to-Image Diffusion Models
MeshSegmenter: Zero-Shot Mesh Semantic Segmentation via Texture Synthesis
SegPoint: Segment Any Point Cloud via Large Language Model
Serialized Point Mamba: A Serialized Point Cloud Mamba Segmentation Model
RAPiD-Seg: Range-Aware Pointwise Distance Distribution Networks for 3D LiDAR Segmentation
Shape2Scene: 3D Scene Representation Learning Through Pre-training on Shape Data
3D Weakly Supervised Semantic Segmentation with 2D Vision-Language Guidance
Uplifting Range-View-based 3D Semantic Segmentation in Real-Time with Multi-Sensor Fusion
Exploring the Untouched Sweeps for Conflict-Aware 3D Segmentation Pretraining
Pamba: Enhancing Global Interaction in Point Clouds via State Space Model
Enhancing Generalizability of Representation Learning for Data-Efficient 3D Scene Understanding
LiSD: An Efficient Multi-Task Learning Framework for LiDAR Segmentation and Detection
PanoSSC: Exploring Monocular Panoptic 3D Scene Reconstruction for Autonomous Driving
Beyond Bare Queries: Open-Vocabulary Object Grounding with 3D Scene Graph
Merlin: A Vision Language Foundation Model for 3D Computed Tomography
ARCH2S: Dataset, Benchmark and Challenges for Learning Exterior Architectural Structures from Point Clouds
Towards Automating the Retrospective Generation of BIM Models: A Unified Framework for 3D Semantic Reconstruction of the Built Environment
Reasoning3D -- Grounding and Reasoning in 3D: Fine-Grained Zero-Shot Open-Vocabulary 3D Reasoning Part Segmentation via Large Vision-Language Models
SE3D: A Framework For Saliency Method Evaluation In 3D Imaging
TS40K: a 3D Point Cloud Dataset of Rural Terrain and Electrical Transmission System
FRACTAL: An Ultra-Large-Scale Aerial Lidar Dataset for 3D Semantic Segmentation of Diverse Landscapes
ECLAIR: A High-Fidelity Aerial LiDAR Dataset for Semantic Segmentation
OpenTrench3D: A Photogrammetric 3D Point Cloud Dataset for Semantic Segmentation of Underground Utilities
RESSCAL3D: Resolution Scalable 3D Semantic Segmentation of Point Clouds
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.