📜 Paper: Multimodality Helps Few-shot 3D Point Cloud Semantic Segmentation (arxiv.org/pdf/2410.22489)
🔗 Code: github.com/ZhaochongAn/...

#ICLR2025 #Multimodality #3DSegmentation @belongielab.org @ellis.eu @ethzurich.bsky.social @ox.ac.uk

3D perception models need more than detection; they require precise, context-rich segmentation to perform reliably.

iMerit delivers high-quality #3Dsegmentation across semantic, instance, and panoptic approaches. Learn more: imerit.net/domains/auto...

#LiDAR #AutonomousVehicles #ComputerVision

#3Dsegmentation breaks when teams label frame by frame. Work on fused, high-density point clouds instead. Annotate once, propagate across frames, and improve boundary accuracy with better context.

Watch: www.youtube.com/watch?v=BD_M...

#LiDAR #ComputerVision #AITraining

The TechBeat: Solving 3D Segmentation’s Biggest Bottleneck (11/23/2025) #Technology #EmergingTechnologies #ArtificialIntelligence #3DSegmentation

PartSAM, a 3‑D part‑segmentation model trained on native geometry, was built with five million shape‑part pairs and can segment surface and interior parts with prompts. Read more: https://getnews.me/partsam-promptable-3d-part-segmentation-trained-on-native-data/ #partsam #3dsegmentation #promptable

GLCND.IO
#3DSegmentation #SemanticSegmentation #PointCloud
https://glcnd.io/advancements-in-3d-segmentation-for-precise-data-analysis/

Open‑YOLO 3D replaces costly SAM/CLIP steps with 2D detection, LG label‑maps, and parallelized visibility, enabling fast and accurate 3D OV segmentation. #3dsegmentation

This section reviews closed‑vocabulary 3D methods, open‑vocabulary 2D recognition, and emerging open‑vocabulary 3D segmentation approaches using SAM/CLIP. #3dsegmentation

Open‑YOLO 3D uses 2D object detection instead of heavy SAM/CLIP for open‑vocabulary 3D segmentation, achieving SOTA results with up to 16× faster inference. #3dsegmentation

arxiv.org/abs/2506.09980
PartPacker: Efficient Part-level 3D Object Generation (Nvidia research).
Given a single input image, this method generates high-quality 3D objects with an arbitrary number of complete and semantically meaningful parts. #3Dsegmentation
huggingface.co/nvidia/PartP... (demo)