Point Transformer

Latest papers

8 papers1 paper in the last four weeks, with none the four weeks before. 0.0% of all new papers.

Lang3DSeg: Annotation-Free Open-Vocabulary 3D Segmentation with Point Transformers
Oct 1, 2026Cigdem Kokenoz, Amir Salarpour, Alkim Domeke +5Open-Vocabulary 3D Segmentation Transformer
Department of Automotive Engineering, Clemson University, Greenville, SC, USA. · School of Computing, Clemson University, Clemson, SC, USA.
Bi-PT: Bidirectional Cross-Attention Point Transformers for Four-Chamber Heart Reconstruction from Sparse Cardiac MRI Data
Jul 8, 2026Chenchuhui Hu, Shaoming Pan, Leon Axel +1Transformer 3D Medical Imaging
Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, USA · School of Medicine, New York University, New York, NY, USA
MedPCFM: Improving Medical Point Cloud Completion by Integrating Point Transformers and Flow Matching
Jun 23, 2026Kamil Kwarciak, Marek WodzinskiFlow Matching Point Cloud Completion
Department of Measurement and Electronics, AGH University of Krakow, Krakow, Poland · Sano Centre for Computational Medicine, Krakow, Poland
HEPTv2: End-to-End Efficient Point Transformer for Charged Particle Reconstruction
Jun 18, 2026Siqi Miao, Shitij Govil, Jack P. Rodgers +5Efficient Transformer Inference Transformer
School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA. · Department of Physics and Astronomy, Purdue University, West Lafayette, IN, USA. · Department of Physics, University of California San Diego, La Jolla, CA, USA. +1
SegmentAnyTreeV2: Scaling Transformer-Based Tree Instance Segmentation Across Sensors, Platforms, and Forests
Jun 6, 2026Maciej Wielgosz, Stefano Puliti, Rasmus AstrupTransformer Instance Segmentation
Norwegian Institute of Bioeconomy Research (NIBIO), Høgskoleveien 7, 1433 Ås, Norway
OPTNet: Ordering Point Transformer Network for Post-disaster 3D Semantic Segmentation
May 16, 2026Nhut Le, Ehsan Karimi, Maryam RahnemoonfarDisaster Damage Assessment Transformer
Computer Science and Engineering, Lehigh University, Bethlehem PA 18015, US · Civil and Environmental Engineering, Lehigh University, Bethlehem PA 18015, US
ClickSeg3D: Few-Click Interactive Segmentation via Semantic Embeddings
May 9, 2026Xueyang Kang, Zijian Yu, Kourosh Khoshelham +13D Point Cloud Segmentation 3D Instance Segmentation
School of Electrical and Electronic Engineering, Nanyang Technological University · University of Science and Technology of China · Faculty of Architecture and the Built Environment, Delft University of Technology
Riverine Land Cover Mapping through Semantic Segmentation of Multispectral Point Clouds
Mar 23, 2026Sopitta Thurachen, Josef Taher, Matti Lehtomäki +6Point Transformer Land Cover Classification
Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute FGI, Vuorimiehentie 5, Espoo, 02150, Finland · Faculty of Technology, University of Turku, Turku, 20014, Finland · Department of Geography and Geology, University of Turku, Turku, 20014, Finland +1

Oct 1, 2026J/K move · Enter open · S save

Lang3DSeg: Annotation-Free Open-Vocabulary 3D Segmentation with Point Transformers

Cigdem Kokenoz, Amir Salarpour, Alkim Domeke, Christopher Salas+4

Department of Automotive Engineering, Clemson University, Greenville, SC, USA. · School of Computing, Clemson University, Clemson, SC, USA.

Open-Vocabulary 3D Segmentation Transformer 3D Semantic Segmentation Point Transformer

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Accurate 3D semantic perception is critical for safe autonomous navigation. However, supervised LiDAR segmentation remains tied to closed taxonomies and to the cost of point-wise manual annotation. Open-vocabulary methods avoid that cost by projecting the output of 2D vision-language models onto LiDAR and distilling it into a 3D network. These methods rely almost exclusively on voxel-based sparse convolutions, and point transformers have so far been limited to indoor environments, where 3D data is dense and bounded. We present Lang3DSeg, which establishes a point transformer as the backbone for annotation-free open-vocabulary segmentation of outdoor 3D LiDAR, and is trained from scratch without geometric pre-training. This training paradigm necessitates addressing the inherent noise in 2D-to-3D label projections; specifically, naive projection often suffers from depth ambiguity, where points behind an object are erroneously assigned its semantic label. We therefore composite masks using an explicit class-priority rule and truncate each projected instance at the first gap in its depth distribution, correcting the projection error directly rather than averaging it over registered sequences. Lang3DSeg achieves 52.8% mIoU on nuScenes validation and 41.4% on SemanticKITTI, the highest among published annotation-free methods on both benchmarks. Every 3D semantic segmentation is on a single LiDAR sweep, and inference operates in real-time without running vision-language models.