3D Semantic Segmentation
168 papers with code • 13 benchmarks • 30 datasets
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.
Benchmarks
These leaderboards are used to track progress in 3D Semantic Segmentation
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Libraries
Use these libraries to find 3D Semantic Segmentation models and implementationsDatasets
nuScenes
SemanticKITTI
S3DIS
KITTI-360
PartNet
SemanticPOSS
AMOS
RELLIS-3D
STPLS3D
nuScenes-C
Subtasks
Latest papers
OpenTrench3D: A Photogrammetric 3D Point Cloud Dataset for Semantic Segmentation of Underground Utilities
We present OpenTrench3D, a novel and comprehensive 3D Semantic Segmentation point cloud dataset, designed to advance research and development in underground utility surveying and mapping.
OA-CNNs: Omni-Adaptive Sparse CNNs for 3D Semantic Segmentation
This exploration led to the creation of Omni-Adaptive 3D CNNs (OA-CNNs), a family of networks that integrates a lightweight module to greatly enhance the adaptivity of sparse CNNs at minimal computational cost.
Visual Foundation Models Boost Cross-Modal Unsupervised Domain Adaptation for 3D Semantic Segmentation
etrontech/vfmseg
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Then, another VFM trained on fine-grained 2D masks is adopted to guide the generation of semantically augmented images and point clouds to enhance the performance of neural networks, which mix the data from source and target domains like view frustums (FrustumMixing).
3D Semantic Segmentation-Driven Representations for 3D Object Detection
In autonomous driving, 3D detection provides more precise information to downstream tasks, including path planning and motion estimation, compared to 2D detection.
MM-Point: Multi-View Information-Enhanced Multi-Modal Self-Supervised 3D Point Cloud Understanding
In perception, multiple sensory information is integrated to map visual information from 2D views onto 3D objects, which is beneficial for understanding in 3D environments.
Scalable 3D Panoptic Segmentation As Superpoint Graph Clustering
drprojects/superpoint_transformer
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We introduce a highly efficient method for panoptic segmentation of large 3D point clouds by redefining this task as a scalable graph clustering problem.
Multi-modality Affinity Inference for Weakly Supervised 3D Semantic Segmentation
The point affinity proposed in this paper is characterized by features from multiple modalities (e. g., point cloud and RGB), and is further refined by normalizing the classifier weights to alleviate the detrimental effects of long-tailed distribution without the need of the prior of category distribution.
PointCT: Point Central Transformer Network for Weakly-supervised Point Cloud Semantic Segmentation
anhthuan1999/PointCT
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Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2023
Although point cloud segmentation has a principal role in 3D understanding, annotating fully large-scale scenes for this task can be costly and time-consuming.
Point Transformer V3: Simpler, Faster, Stronger
facebookresearch/SparseConvNet
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This paper is not motivated to seek innovation within the attention mechanism.
FRNet: Frustum-Range Networks for Scalable LiDAR Segmentation
Firstly, a frustum feature encoder module is used to extract per-point features within the frustum region, which preserves scene consistency and is crucial for point-level predictions.
