Rethinking Open-Vocabulary Segmentation of Radiance Fields in 3D Space

• URL: http://arxiv.org/abs/2408.07416v3
• Date: Fri, 21 Feb 2025 06:43:11 GMT
• Title: Rethinking Open-Vocabulary Segmentation of Radiance Fields in 3D Space
• Authors: Hyunjee Lee, Youngsik Yun, Jeongmin Bae, Seoha Kim, Youngjung Uh,
• Abstract summary: We redefine the problem to segment the 3D volume and propose the following methods for better 3D understanding.<n>We directly supervise the 3D points to train the language embedding field, unlike previous methods that anchor supervision at 2D pixels.<n>We transfer the learned language field to 3DGS, achieving the first real-time rendering speed without sacrificing training time or accuracy.
• Score: 10.49905491984899
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Understanding the 3D semantics of a scene is a fundamental problem for various scenarios such as embodied agents. While NeRFs and 3DGS excel at novel-view synthesis, previous methods for understanding their semantics have been limited to incomplete 3D understanding: their segmentation results are rendered as 2D masks that do not represent the entire 3D space. To address this limitation, we redefine the problem to segment the 3D volume and propose the following methods for better 3D understanding. We directly supervise the 3D points to train the language embedding field, unlike previous methods that anchor supervision at 2D pixels. We transfer the learned language field to 3DGS, achieving the first real-time rendering speed without sacrificing training time or accuracy. Lastly, we introduce a 3D querying and evaluation protocol for assessing the reconstructed geometry and semantics together. Code, checkpoints, and annotations are available at the project page.

Related papers

• MOSE: Monocular Semantic Reconstruction Using NeRF-Lifted Noisy Priors [11.118490283303407]
  We propose a neural field semantic reconstruction approach to lift inferred image-level noisy priors to 3D. Our method produces accurate semantics and geometry in both 3D and 2D space.
  arXiv  Detail & Related papers  (2024-09-21T05:12:13Z)
• General Geometry-aware Weakly Supervised 3D Object Detection [62.26729317523975]
  A unified framework is developed for learning 3D object detectors from RGB images and associated 2D boxes. Experiments on KITTI and SUN-RGBD datasets demonstrate that our method yields surprisingly high-quality 3D bounding boxes with only 2D annotation.
  arXiv  Detail & Related papers  (2024-07-18T17:52:08Z)
• Reasoning3D -- Grounding and Reasoning in 3D: Fine-Grained Zero-Shot Open-Vocabulary 3D Reasoning Part Segmentation via Large Vision-Language Models [20.277479473218513]
  We introduce a new task: Zero-Shot 3D Reasoning for parts searching and localization for objects. We design a simple baseline method, Reasoning3D, with the capability to understand and execute complex commands. We show that Reasoning3D can effectively localize and highlight parts of 3D objects based on implicit textual queries.
  arXiv  Detail & Related papers  (2024-05-29T17:56:07Z)
• Know Your Neighbors: Improving Single-View Reconstruction via Spatial Vision-Language Reasoning [119.99066522299309]
  KYN is a novel method for single-view scene reconstruction that reasons about semantic and spatial context to predict each point's density. We show that KYN improves 3D shape recovery compared to predicting density for each 3D point in isolation. We achieve state-of-the-art results in scene and object reconstruction on KITTI-360, and show improved zero-shot generalization compared to prior work.
  arXiv  Detail & Related papers  (2024-04-04T17:59:59Z)
• POP-3D: Open-Vocabulary 3D Occupancy Prediction from Images [32.33170182669095]
  We describe an approach to predict open-vocabulary 3D semantic voxel occupancy map from input 2D images. The architecture consists of a 2D-3D encoder together with occupancy prediction and 3D-language heads. The output is a dense voxel map of 3D grounded language embeddings enabling a range of open-vocabulary tasks.
  arXiv  Detail & Related papers  (2024-01-17T18:51:53Z)
• Weakly-Supervised 3D Visual Grounding based on Visual Linguistic Alignment [26.858034573776198]
  We propose a weakly supervised approach for 3D visual grounding based on Visual Linguistic Alignment. Our 3D-VLA exploits the superior ability of current large-scale vision-language models on aligning the semantics between texts and 2D images. During the inference stage, the learned text-3D correspondence will help us ground the text queries to the 3D target objects even without 2D images.
  arXiv  Detail & Related papers  (2023-12-15T09:08:14Z)
• Four Ways to Improve Verbo-visual Fusion for Dense 3D Visual Grounding [56.00186960144545]
  3D visual grounding is the task of localizing the object in a 3D scene which is referred by a description in natural language. We propose a dense 3D grounding network, featuring four novel stand-alone modules that aim to improve grounding performance.
  arXiv  Detail & Related papers  (2023-09-08T19:27:01Z)
• CLIP$^2$: Contrastive Language-Image-Point Pretraining from Real-World Point Cloud Data [80.42480679542697]
  We propose Contrastive Language-Image-Point Cloud Pretraining (CLIP$2$) to learn the transferable 3D point cloud representation in realistic scenarios. Specifically, we exploit naturally-existed correspondences in 2D and 3D scenarios, and build well-aligned and instance-based text-image-point proxies from those complex scenarios.
  arXiv  Detail & Related papers  (2023-03-22T09:32:45Z)
• SSR-2D: Semantic 3D Scene Reconstruction from 2D Images [54.46126685716471]
  In this work, we explore a central 3D scene modeling task, namely, semantic scene reconstruction without using any 3D annotations. The key idea of our approach is to design a trainable model that employs both incomplete 3D reconstructions and their corresponding source RGB-D images. Our method achieves the state-of-the-art performance of semantic scene completion on two large-scale benchmark datasets MatterPort3D and ScanNet.
  arXiv  Detail & Related papers  (2023-02-07T17:47:52Z)
• SceneDreamer: Unbounded 3D Scene Generation from 2D Image Collections [49.802462165826554]
  We present SceneDreamer, an unconditional generative model for unbounded 3D scenes. Our framework is learned from in-the-wild 2D image collections only, without any 3D annotations.
  arXiv  Detail & Related papers  (2023-02-02T18:59:16Z)
• Learning 3D Scene Priors with 2D Supervision [37.79852635415233]
  We propose a new method to learn 3D scene priors of layout and shape without requiring any 3D ground truth. Our method represents a 3D scene as a latent vector, from which we can progressively decode to a sequence of objects characterized by their class categories. Experiments on 3D-FRONT and ScanNet show that our method outperforms state of the art in single-view reconstruction.
  arXiv  Detail & Related papers  (2022-11-25T15:03:32Z)
• 3D Concept Grounding on Neural Fields [99.33215488324238]
  Existing visual reasoning approaches typically utilize supervised methods to extract 2D segmentation masks on which concepts are grounded. Humans are capable of grounding concepts on the underlying 3D representation of images. We propose to leverage the continuous, differentiable nature of neural fields to segment and learn concepts.
  arXiv  Detail & Related papers  (2022-07-13T17:59:33Z)
• NeSF: Neural Semantic Fields for Generalizable Semantic Segmentation of 3D Scenes [25.26518805603798]
  NeSF is a method for producing 3D semantic fields from posed RGB images alone. Our method is the first to offer truly dense 3D scene segmentations requiring only 2D supervision for training.
  arXiv  Detail & Related papers  (2021-11-25T21:44:54Z)
• Parameter-Efficient Person Re-identification in the 3D Space [51.092669618679615]
  We project 2D images to a 3D space and introduce a novel parameter-efficient Omni-scale Graph Network (OG-Net) to learn the pedestrian representation directly from 3D point clouds. OG-Net effectively exploits the local information provided by sparse 3D points and takes advantage of the structure and appearance information in a coherent manner. We are among the first attempts to conduct person re-identification in the 3D space.
  arXiv  Detail & Related papers  (2020-06-08T13:20:33Z)
• Semantic Correspondence via 2D-3D-2D Cycle [58.023058561837686]
  We propose a new method on predicting semantic correspondences by leveraging it to 3D domain. We show that our method gives comparative and even superior results on standard semantic benchmarks.
  arXiv  Detail & Related papers  (2020-04-20T05:27:45Z)

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.