PlaceIt3D: Language-Guided Object Placement in Real 3D Scenes

• URL: http://arxiv.org/abs/2505.05288v1
• Date: Thu, 08 May 2025 14:29:11 GMT
• Title: PlaceIt3D: Language-Guided Object Placement in Real 3D Scenes
• Authors: Ahmed Abdelreheem, Filippo Aleotti, Jamie Watson, Zawar Qureshi, Abdelrahman Eldesokey, Peter Wonka, Gabriel Brostow, Sara Vicente, Guillermo Garcia-Hernando,
• Abstract summary: We introduce the novel task of Language-Guided Object Placement in Real 3D Scenes.<n>Compared with other language-guided localization tasks in 3D scenes such as grounding, this task has specific challenges.<n>It is ambiguous because it has multiple valid solutions, and it requires reasoning about 3D geometric relationships and free space.
• Score: 37.89787678513378
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce the novel task of Language-Guided Object Placement in Real 3D Scenes. Our model is given a 3D scene's point cloud, a 3D asset, and a textual prompt broadly describing where the 3D asset should be placed. The task here is to find a valid placement for the 3D asset that respects the prompt. Compared with other language-guided localization tasks in 3D scenes such as grounding, this task has specific challenges: it is ambiguous because it has multiple valid solutions, and it requires reasoning about 3D geometric relationships and free space. We inaugurate this task by proposing a new benchmark and evaluation protocol. We also introduce a new dataset for training 3D LLMs on this task, as well as the first method to serve as a non-trivial baseline. We believe that this challenging task and our new benchmark could become part of the suite of benchmarks used to evaluate and compare generalist 3D LLM models.

Related papers

• Does Your 3D Encoder Really Work? When Pretrain-SFT from 2D VLMs Meets 3D VLMs [72.11701578308804]
  This paper categorizes recent 3D Vision-Language Models into 3D object-centric, 2D image-based, and 3D scene-centric approaches.<n>Despite the architectural similarity of 3D scene-centric VLMs to their 2D counterparts, they have exhibited comparatively lower performance compared with the latest 3D object-centric and 2D image-based approaches.<n>Our investigation suggests that while these models possess cross-modal alignment capabilities, they tend to over-rely on linguistic cues and overfit to frequent answer distributions.
  arXiv  Detail & Related papers  (2025-06-05T17:56:12Z)
• Rethinking Open-Vocabulary Segmentation of Radiance Fields in 3D Space [10.49905491984899]
  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.
  arXiv  Detail & Related papers  (2024-08-14T09:50:02Z)
• SceneGPT: A Language Model for 3D Scene Understanding [0.9054540533394926]
  We present SceneGPT, an LLM based scene understanding system which can perform 3D spatial reasoning without training or explicit 3D supervision. Key components of our framework are - 1) a 3D scene graph, that serves as scene representation, encoding the objects in the scene and their spatial relationships 2) a pre-trained LLM that can be adapted with in context learning for 3D spatial reasoning.
  arXiv  Detail & Related papers  (2024-08-13T14:26:30Z)
• Grounded 3D-LLM with Referent Tokens [58.890058568493096]
  We propose Grounded 3D-LLM to consolidate various 3D vision tasks within a unified generative framework. The model uses scene referent tokens as special noun phrases to reference 3D scenes. Per-task instruction-following templates are employed to ensure natural and diversity in translating 3D vision tasks into language formats.
  arXiv  Detail & Related papers  (2024-05-16T18:03:41Z)
• 3DMIT: 3D Multi-modal Instruction Tuning for Scene Understanding [12.823274886850697]
  We introduce a novel and efficient prompt tuning paradigm, 3DMIT. This paradigm eliminates the alignment stage between 3D scenes and language and extends the instruction prompt with the 3D modality information. We evaluate the effectiveness of our method across diverse tasks in the 3D scene domain.
  arXiv  Detail & Related papers  (2024-01-06T12:20:18Z)
• Chat-Scene: Bridging 3D Scene and Large Language Models with Object Identifiers [65.51132104404051]
  We introduce the use of object identifiers and object-centric representations to interact with scenes at the object level. Our model significantly outperforms existing methods on benchmarks including ScanRefer, Multi3DRefer, Scan2Cap, ScanQA, and SQA3D.
  arXiv  Detail & Related papers  (2023-12-13T14:27:45Z)
• 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)
• 3D-LLM: Injecting the 3D World into Large Language Models [60.43823088804661]
  Large language models (LLMs) and Vision-Language Models (VLMs) have been proven to excel at multiple tasks, such as commonsense reasoning. We propose to inject the 3D world into large language models and introduce a new family of 3D-LLMs. Specifically, 3D-LLMs can take 3D point clouds and their features as input and perform a diverse set of 3D-related tasks.
  arXiv  Detail & Related papers  (2023-07-24T17:59:02Z)
• Multi-CLIP: Contrastive Vision-Language Pre-training for Question Answering tasks in 3D Scenes [68.61199623705096]
  Training models to apply common-sense linguistic knowledge and visual concepts from 2D images to 3D scene understanding is a promising direction that researchers have only recently started to explore. We propose a novel 3D pre-training Vision-Language method, namely Multi-CLIP, that enables a model to learn language-grounded and transferable 3D scene point cloud representations.
  arXiv  Detail & Related papers  (2023-06-04T11:08:53Z)

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.