SpatialPoint: Spatial-aware Point Prediction for Embodied Localization

• URL: http://arxiv.org/abs/2603.26690v1
• Date: Mon, 16 Mar 2026 07:03:48 GMT
• Title: SpatialPoint: Spatial-aware Point Prediction for Embodied Localization
• Abstract summary: Embodied intelligence requires a capability to determine where to act in 3D space.<n>We instantiate embodied localization with two complementary target types.<n>We propose SpatialPoint, a vision-language framework with careful design that integrates structured depth into a vision-language model.
• Score: 6.970630364284529
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Embodied intelligence fundamentally requires a capability to determine where to act in 3D space. We formalize this requirement as embodied localization -- the problem of predicting executable 3D points conditioned on visual observations and language instructions. We instantiate embodied localization with two complementary target types: touchable points, surface-grounded 3D points enabling direct physical interaction, and air points, free-space 3D points specifying placement and navigation goals, directional constraints, or geometric relations. Embodied localization is inherently a problem of embodied 3D spatial reasoning -- yet most existing vision-language systems rely predominantly on RGB inputs, necessitating implicit geometric reconstruction that limits cross-scene generalization, despite the widespread adoption of RGB-D sensors in robotics. To address this gap, we propose SpatialPoint, a spatial-aware vision-language framework with careful design that integrates structured depth into a vision-language model (VLM) and generates camera-frame 3D coordinates. We construct a 2.6M-sample RGB-D dataset covering both touchable and air points QA pairs for training and evaluation. Extensive experiments demonstrate that incorporating depth into VLMs significantly improves embodied localization performance. We further validate SpatialPoint through real-robot deployment across three representative tasks: language-guided robotic arm grasping at specified locations, object placement to target destinations, and mobile robot navigation to goal positions.

Related papers

• Assisted Spatial Cognition Through Vision-Language Models [0.0]
  Multimodal AI is transforming assistive technologies by enabling simultaneous processing of visual and textual data.<n>This advancement holds significant promise for over 43 million visually impaired and neuro-divergent individuals worldwide.<n>We introduce a novel end-to-end framework that integrates Large Language Models (LLMs) and Vision-Language Models (VLMs)<n>Our system captures video input via standard mobile phone cameras, and employs SLAM3R to generate dense 3D point clouds in real-time.
  arXiv  Detail & Related papers  (2026-09-11T11:58:59Z)
• GA-VLN: Geometry-Aware BEV Representation for Efficient Vision-Language Navigation [75.85672467847631]
  We introduce the Geometry-Aware BEV (GA-BEV) - a compact, 3D-grounded feature representation.<n>We construct BEV spatial maps from RGB-D inputs by projecting visual features into 3D space.<n>We incorporate features from a pretrained 3D foundation model into the BEV space, injecting structural priors learned from large-scale 3D reconstruction tasks.
  arXiv  Detail & Related papers  (2026-05-21T06:20:17Z)
• Think, Act, Build: An Agentic Framework with Vision Language Models for Zero-Shot 3D Visual Grounding [34.1504914582344]
  3D Visual Grounding aims to localize objects in 3D scenes via natural language descriptions.<n>We propose "Think, Act, Build (TAB)", a generative 2D-to-3D reconstruction paradigm operating directly on raw RGB-D streams.<n>To overcome the multi-view coverage deficit caused by strict VLM semantic tracking, we introduce the Semantic-Anchored Geometric Expansion.
  arXiv  Detail & Related papers  (2026-04-01T06:12:16Z)
• N3D-VLM: Native 3D Grounding Enables Accurate Spatial Reasoning in Vision-Language Models [45.008146973701855]
  N3D-VLM is a novel unified framework that seamlessly integrates native 3D object perception with 3D-aware visual reasoning.<n>Unlike conventional end-to-end models that directly predict answers from RGB/RGB-D inputs, our approach equips the model with native 3D object perception capabilities.
  arXiv  Detail & Related papers  (2025-12-18T14:03:44Z)
• Abstract 3D Perception for Spatial Intelligence in Vision-Language Models [100.13033631690114]
  Vision-language models (VLMs) struggle with 3D-related tasks such as spatial cognition and physical understanding.<n>We introduce SandboxVLM, a framework that leverages abstract bounding boxes to encode geometric structure and physical kinematics for VLM.<n>Our approach consistently improves spatial intelligence, achieving an 8.3% gain on SAT Real compared with baseline methods.
  arXiv  Detail & Related papers  (2025-11-14T04:16:09Z)
• Task-Aware 3D Affordance Segmentation via 2D Guidance and Geometric Refinement [12.260126771415019]
  We introduce Task-Aware 3D Scene-level Affordance segmentation (TASA)<n>TASA is a novel geometry-optimized framework that jointly leverages 2D semantic cues and 3D geometric reasoning in a coarse-to-fine manner.<n>To fully exploit 3D geometric information, a 3D affordance refinement module is proposed to integrate 2D semantic priors with local 3D geometry.
  arXiv  Detail & Related papers  (2025-11-12T13:36:37Z)
• From Spatial to Actions: Grounding Vision-Language-Action Model in Spatial Foundation Priors [54.84863164684646]
  Existing vision-language-action (VLA) models act in 3D real-world but are typically built on 2D encoders.<n>In this work, we introduce FALCON, a novel paradigm that injects rich 3D spatial tokens into the action head.
  arXiv  Detail & Related papers  (2025-10-20T11:26:45Z)
• SURPRISE3D: A Dataset for Spatial Understanding and Reasoning in Complex 3D Scenes [105.8644620467576]
  We introduce Stextscurprise3D, a novel dataset designed to evaluate language-guided spatial reasoning segmentation in complex 3D scenes.<n>Stextscurprise3D consists of more than 200k vision language pairs across 900+ detailed indoor scenes from ScanNet++ v2.<n>The dataset contains 89k+ human-annotated spatial queries deliberately crafted without object name.
  arXiv  Detail & Related papers  (2025-07-10T14:01:24Z)
• Move to Understand a 3D Scene: Bridging Visual Grounding and Exploration for Efficient and Versatile Embodied Navigation [54.04601077224252]
  Embodied scene understanding requires not only comprehending visual-spatial information but also determining where to explore next in the 3D physical world.<n>underlinetextbf3D vision-language learning enables embodied agents to effectively explore and understand their environment.<n>model's versatility enables navigation using diverse input modalities, including categories, language descriptions, and reference images.
  arXiv  Detail & Related papers  (2025-07-05T14:15:52Z)
• Zero-Shot 3D Visual Grounding from Vision-Language Models [10.81711535075112]
  3D Visual Grounding (3DVG) seeks to locate target objects in 3D scenes using natural language descriptions.<n>We present SeeGround, a zero-shot 3DVG framework that leverages 2D Vision-Language Models (VLMs) to bypass the need for 3D-specific training.
  arXiv  Detail & Related papers  (2025-05-28T14:53:53Z)
• Semi-Perspective Decoupled Heatmaps for 3D Robot Pose Estimation from Depth Maps [66.24554680709417]
  Knowing the exact 3D location of workers and robots in a collaborative environment enables several real applications. We propose a non-invasive framework based on depth devices and deep neural networks to estimate the 3D pose of robots from an external camera.
  arXiv  Detail & Related papers  (2022-07-06T08:52:12Z)
• LanguageRefer: Spatial-Language Model for 3D Visual Grounding [72.7618059299306]
  We develop a spatial-language model for a 3D visual grounding problem. We show that our model performs competitively on visio-linguistic datasets proposed by ReferIt3D.
  arXiv  Detail & Related papers  (2021-07-07T18:55:03Z)

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.