Leveraging 2D-VLM for Label-Free 3D Segmentation in Large-Scale Outdoor Scene Understanding

• URL: http://arxiv.org/abs/2601.02029v1
• Date: Mon, 05 Jan 2026 11:42:49 GMT
• Title: Leveraging 2D-VLM for Label-Free 3D Segmentation in Large-Scale Outdoor Scene Understanding
• Authors: Toshihiko Nishimura, Hirofumi Abe, Kazuhiko Murasaki, Taiga Yoshida, Ryuichi Tanida,
• Abstract summary: The proposed approach projects 3D point clouds onto 2D images using virtual cameras and performs semantic segmentation via a foundation 2D model guided by natural language prompts.<n>Our method outperforms existing training-free approaches and achieves segmentation accuracy comparable to supervised methods.
• Score: 0.9351726364879229
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents a novel 3D semantic segmentation method for large-scale point cloud data that does not require annotated 3D training data or paired RGB images. The proposed approach projects 3D point clouds onto 2D images using virtual cameras and performs semantic segmentation via a foundation 2D model guided by natural language prompts. 3D segmentation is achieved by aggregating predictions from multiple viewpoints through weighted voting. Our method outperforms existing training-free approaches and achieves segmentation accuracy comparable to supervised methods. Moreover, it supports open-vocabulary recognition, enabling users to detect objects using arbitrary text queries, thus overcoming the limitations of traditional supervised approaches.

Related papers

• Multi-View Projection for Unsupervised Domain Adaptation in 3D Semantic Segmentation [0.9345376836714131]
  We propose a multi-view projection framework for unsupervised domain adaptation (UDA)<n>Our method aligns LiDAR scans into coherent 3D scenes and renders them from multiple virtual camera poses to generate 2D datasets (PC2D)<n>An ensemble of 2D segmentation models is trained on these modalities, and during inference, hundreds of views per scene are processed, with logits back-projected to 3D.<n>We show that our framework enables segmentation of rare classes, leveraging only 2D annotations for those classes while relying on 3D annotations for others in the source domain.
  arXiv  Detail & Related papers  (2025-05-21T14:08:42Z)
• 3D Can Be Explored In 2D: Pseudo-Label Generation for LiDAR Point Clouds Using Sensor-Intensity-Based 2D Semantic Segmentation [3.192308005611312]
  We introduce a new 3D semantic segmentation pipeline that leverages aligned scenes and state-of-the-art 2D segmentation methods.<n>Our approach generates 2D views from LiDAR scans colored by sensor intensity and applies 2D semantic segmentation to these views.<n>The segmented 2D outputs are then back-projected onto the 3D points, with a simple voting-based estimator.
  arXiv  Detail & Related papers  (2025-05-06T08:31:32Z)
• Zero-Shot Dual-Path Integration Framework for Open-Vocabulary 3D Instance Segmentation [19.2297264550686]
  Open-vocabulary 3D instance segmentation transcends traditional closed-vocabulary methods. We introduce Zero-Shot Dual-Path Integration Framework that equally values the contributions of both 3D and 2D modalities. Our framework, utilizing pre-trained models in a zero-shot manner, is model-agnostic and demonstrates superior performance on both seen and unseen data.
  arXiv  Detail & Related papers  (2024-08-16T07:52:00Z)
• Open-Vocabulary 3D Semantic Segmentation with Text-to-Image Diffusion Models [57.37244894146089]
  We propose Diff2Scene, which leverages frozen representations from text-image generative models, along with salient-aware and geometric-aware masks, for open-vocabulary 3D semantic segmentation and visual grounding tasks. We show that it outperforms competitive baselines and achieves significant improvements over state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-07-18T16:20:56Z)
• 3D Weakly Supervised Semantic Segmentation with 2D Vision-Language Guidance [68.8825501902835]
  3DSS-VLG is a weakly supervised approach for 3D Semantic with 2D Vision-Language Guidance. To the best of our knowledge, this is the first work to investigate 3D weakly supervised semantic segmentation by using the textual semantic information of text category labels.
  arXiv  Detail & Related papers  (2024-07-13T09:39:11Z)
• DatasetNeRF: Efficient 3D-aware Data Factory with Generative Radiance Fields [68.94868475824575]
  This paper introduces a novel approach capable of generating infinite, high-quality 3D-consistent 2D annotations alongside 3D point cloud segmentations. We leverage the strong semantic prior within a 3D generative model to train a semantic decoder. Once trained, the decoder efficiently generalizes across the latent space, enabling the generation of infinite data.
  arXiv  Detail & Related papers  (2023-11-18T21:58:28Z)
• Leveraging Large-Scale Pretrained Vision Foundation Models for Label-Efficient 3D Point Cloud Segmentation [67.07112533415116]
  We present a novel framework that adapts various foundational models for the 3D point cloud segmentation task. Our approach involves making initial predictions of 2D semantic masks using different large vision models. To generate robust 3D semantic pseudo labels, we introduce a semantic label fusion strategy that effectively combines all the results via voting.
  arXiv  Detail & Related papers  (2023-11-03T15:41:15Z)
• PartSLIP: Low-Shot Part Segmentation for 3D Point Clouds via Pretrained Image-Language Models [56.324516906160234]
  Generalizable 3D part segmentation is important but challenging in vision and robotics. This paper explores an alternative way for low-shot part segmentation of 3D point clouds by leveraging a pretrained image-language model, GLIP. We transfer the rich knowledge from 2D to 3D through GLIP-based part detection on point cloud rendering and a novel 2D-to-3D label lifting algorithm.
  arXiv  Detail & Related papers  (2022-12-03T06:59:01Z)
• CMR3D: Contextualized Multi-Stage Refinement for 3D Object Detection [57.44434974289945]
  We propose Contextualized Multi-Stage Refinement for 3D Object Detection (CMR3D) framework. Our framework takes a 3D scene as input and strives to explicitly integrate useful contextual information of the scene. In addition to 3D object detection, we investigate the effectiveness of our framework for the problem of 3D object counting.
  arXiv  Detail & Related papers  (2022-09-13T05:26:09Z)
• 3D Guided Weakly Supervised Semantic Segmentation [27.269847900950943]
  We propose a weakly supervised 2D semantic segmentation model by incorporating sparse bounding box labels with available 3D information. We manually labeled a subset of the 2D-3D Semantics(2D-3D-S) dataset with bounding boxes, and introduce our 2D-3D inference module to generate accurate pixel-wise segment proposal masks.
  arXiv  Detail & Related papers  (2020-12-01T03:34:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.