3D Instance Segmentation
59 papers with code • 8 benchmarks • 13 datasets
Image: OccuSeg
Benchmarks
These leaderboards are used to track progress in 3D Instance Segmentation
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Libraries
Use these libraries to find 3D Instance Segmentation models and implementations
ScanNet
S3DIS
KITTI-360
PartNet
SceneNN
STPLS3D
ScanNet200
CREMI
FOR-instance
Latest papers
BSNet: Box-Supervised Simulation-assisted Mean Teacher for 3D Instance Segmentation
peoplelu/bsnet
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To generate higher quality pseudo-labels and achieve more precise weakly supervised 3DIS results, we propose the Box-Supervised Simulation-assisted Mean Teacher for 3D Instance Segmentation (BSNet), which devises a novel pseudo-labeler called Simulation-assisted Transformer.
ODIN: A Single Model for 2D and 3D Perception
The gap in performance between methods that consume posed images versus post-processed 3D point clouds has fueled the belief that 2D and 3D perception require distinct model architectures.
Spherical Mask: Coarse-to-Fine 3D Point Cloud Instance Segmentation with Spherical Representation
Coarse-to-fine 3D instance segmentation methods show weak performances compared to recent Grouping-based, Kernel-based and Transformer-based methods.
Open3DIS: Open-Vocabulary 3D Instance Segmentation with 2D Mask Guidance
We introduce Open3DIS, a novel solution designed to tackle the problem of Open-Vocabulary Instance Segmentation within 3D scenes.
MWSIS: Multimodal Weakly Supervised Instance Segmentation with 2D Box Annotations for Autonomous Driving
Instance segmentation is a fundamental research in computer vision, especially in autonomous driving.
PartSLIP++: Enhancing Low-Shot 3D Part Segmentation via Multi-View Instance Segmentation and Maximum Likelihood Estimation
zyc00/partslip2
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Open-world 3D part segmentation is pivotal in diverse applications such as robotics and AR/VR.
OneFormer3D: One Transformer for Unified Point Cloud Segmentation
Semantic, instance, and panoptic segmentation of 3D point clouds have been addressed using task-specific models of distinct design.
3D Indoor Instance Segmentation in an Open-World
aminebdj/3d-owis
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NeurIPS 2023
We argue that such a closed-world assumption is restrictive and explore for the first time 3D indoor instance segmentation in an open-world setting, where the model is allowed to distinguish a set of known classes as well as identify an unknown object as unknown and then later incrementally learning the semantic category of the unknown when the corresponding category labels are available.
Mask-Attention-Free Transformer for 3D Instance Segmentation
dvlab-research/mask-attention-free-transformer
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ICCV 2023
Therefore, we abandon the mask attention design and resort to an auxiliary center regression task instead.
GaPro: Box-Supervised 3D Point Cloud Instance Segmentation Using Gaussian Processes as Pseudo Labelers
vinairesearch/gapro
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ICCV 2023
Furthermore, we demonstrate the robustness of our approach, where we can adapt various state-of-the-art fully supervised methods to the weak supervision task by using our pseudo labels for training.
