We present a novel task for cross-dataset visual grounding in 3D scenes (Cross3DVG), which overcomes limitations of existing 3D visual grounding models, specifically their restricted 3D resources and consequent tendencies of overfitting a specific 3D dataset. We created RIORefer, a large-scale 3D visual grounding dataset, to facilitate Cross3DVG. It includes more than 63k diverse descriptions of 3D objects within 1,380 indoor RGB-D scans from 3RScan, with human annotations. After training the Cross3DVG model using the source 3D visual grounding dataset, we evaluate it without target labels using the target dataset with, e.g., different sensors, 3D reconstruction methods, and language annotators. Comprehensive experiments are conducted using established visual grounding models and with CLIP-based multi-view 2D and 3D integration designed to bridge gaps among 3D datasets. For Cross3DVG tasks, (i) cross-dataset 3D visual grounding exhibits significantly worse performance than learning and evaluation with a single dataset because of the 3D data and language variants across datasets. Moreover, (ii) better object detector and localization modules and fusing 3D data and multi-view CLIP-based image features can alleviate this lower performance. Our Cross3DVG task can provide a benchmark for developing robust 3D visual grounding models to handle diverse 3D scenes while leveraging deep language understanding.
RGBT-GroundBench: Visual Grounding Beyond RGB in Complex Real-World Scenarios
Institute of Artificial Intelligence, State Key Laboratory of Virtual Reality Technology and Systems, Beihang University（北京航空航天大学人工智能研究院、虚拟现实技术与系统国家重点实验室）；Pengcheng Laboratory（鹏城实验室）；Shanghai Jiao Tong University（上海交通大学）
SRIBD（深圳理工大学（筹））；CUHK-Shenzhen（香港中文大学（深圳））；FNii（未来网络智研院）；Deepwise AI Lab（深兰人工智能实验室）；The University of Hong Kong（香港大学）
Grounding referring expressions in RGBD image has been an emerging field. We present a novel task of 3D visual grounding in single-view RGBD image where the referred objects are often only partially scanned due to occlusion. In contrast to previous works that directly generate object proposals for grounding in the 3D scenes, we propose a bottom-up approach to gradually aggregate context-aware information, effectively addressing the challenge posed by the partial geometry. Our approach first fuses the language and the visual features at the bottom level to generate a heatmap that coarsely local…
RGBX-R1: Visual Modality Chain-of-Thought Guided Reinforcement Learning for Multimodal Grounding
School of Artificial Intelligence, Tianjin University（天津大学人工智能学院）；Low-Altitude Intelligence Lab, Xiong’an National Innovation Center（雄安国家创新中心低空智能实验室）；Xiong’an Guochuang Lantian Technology Co., Ltd.（雄安国创莲田科技有限公司）；College of Electronic Science and Technology, National University of Defense Technology（国防科技大学电子科学学院）
University of Electronic Science and Technology of China（电子科技大学）
Aiming to link natural language descriptions to specific regions in a 3D scene represented as 3D point clouds, 3D visual grounding is a very fundamental task for human-robot interaction. The recognition errors can significantly impact the overall accuracy and then degrade the operation of AI systems. Despite their effectiveness, existing methods suffer from the difficulty of low recognition accuracy in cases of multiple adjacent objects with similar appearances.To address this issue, this work intuitively introduces the human-robot interaction as a cue to facilitate the development of 3D visua…
Technical University of Munich（慕尼黑工业大学）；Simon Fraser University（西蒙弗雷泽大学）
Recent studies on dense captioning and visual grounding in 3D have achieved impressive results. Despite developments in both areas, the limited amount of available 3D vision-language data causes overfitting issues for 3D visual grounding and 3D dense captioning methods. Also, how to discriminatively describe objects in complex 3D environments is not fully studied yet. To address these challenges, we present D3Net, an end-to-end neural speaker-listener architecture that can detect, describe and discriminate. Our D3Net unifies dense captioning and visual grounding in 3D in a self-critical manner…
Generalist Scanner Meets Specialist Locator: A Synergistic Coarse-to-Fine Framework for Robust GUI Grounding
University of California, San Diego（加州大学圣地亚哥分校）；ByteDance（字节跳动）；University of California, Merced（加州大学默塞德分校）；University of Southern California（南加州大学）；University at Buffalo（布法罗大学）；The University of Queensland（昆士兰大学）
Service robots should be able to interact naturally with non-expert human users, not only to help them in various tasks but also to receive guidance in order to resolve ambiguities that might be present in the instruction. We consider the task of visual grounding, where the agent segments an object from a crowded scene given a natural language description. Modern holistic approaches to visual grounding usually ignore language structure and struggle to cover generic domains, therefore relying heavily on large datasets. Additionally, their transfer performance in RGB-D datasets suffers due to hi…
Solving Zero-Shot 3D Visual Grounding as Constraint Satisfaction Problems
Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, University of Groningen（格罗宁根大学伯努利学院）；Computer Vision for Human-Computer Interaction Lab (cv:hci), Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院人机交互计算机视觉实验室）
Move to Understand a 3D Scene: Bridging Visual Grounding and Exploration for Efficient and Versatile Embodied Navigation
Tsinghua University（清华大学）；Beijing Institute of Technology（北京理工大学）；Beihang University（北航）；State Key Laboratory of General Artificial Intelligence, BIGAI, China（国家一般人工智能重点实验室, BIGAI, 中国）
University of Rochester（罗切斯特大学）；The Chinese University of Hong Kong（香港中文大学）
3D visual grounding aims at grounding a natural language description about a 3D scene, usually represented in the form of 3D point clouds, to the targeted object region. Point clouds are sparse, noisy, and contain limited semantic information compared with 2D images. These inherent limitations make the 3D visual grounding problem more challenging. In this study, we propose 2D Semantics Assisted Training (SAT) that utilizes 2D image semantics in the training stage to ease point-cloud-language joint representation learning and assist 3D visual grounding. The main idea is to learn auxiliary align…
Multiple Consistent 2D-3D Mappings for Robust Zero-Shot 3D Visual Grounding
Hangzhou Dianzi University（杭州电子科技大学）；Zhejiang University（浙江大学）；Alibaba Group（阿里巴巴集团）；Institute for Clarity in Documentation（文档清晰研究所）；Inria Paris-Rocquencourt（Inria巴黎-鲁克蒙特研究所）；Rajiv Gandhi University（拉贾·甘地大学）；Tsinghua University（清华大学）；Palmer Research Laboratories（帕勒实验室）
School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University（西北工业大学光电与智能研究院）；Technical University of Munich (TUM)（慕尼黑工业大学）
Natural Human-Robot Interaction (HRI) is one of the key components for service robots to be able to work in human-centric environments. In such dynamic environments, the robot needs to understand the intention of the user to accomplish a task successfully. Towards addressing this point, we propose a software architecture that segments a target object from a crowded scene, indicated verbally by a human user. At the core of our system, we employ a multi-modal deep neural network for visual grounding. Unlike most grounding methods that tackle the challenge using pre-trained object detectors via a…
The Chinese University of Hong Kong (Shenzhen)（香港中文大学（深圳））；Shenzhen Research Institute of Big Data（深圳大数据研究院）；Southern University of Science and Technology（南方科技大学）
Compared with the visual grounding on 2D images, the natural-language-guided 3D object localization on point clouds is more challenging. In this paper, we propose a new model, named InstanceRefer, to achieve a superior 3D visual grounding through the grounding-by-matching strategy. In practice, our model first predicts the target category from the language descriptions using a simple language classification model. Then, based on the category, our model sifts out a small number of instance candidates (usually less than 20) from the panoptic segmentation of point clouds. Thus, the non-trivial 3D…
To effectively apply robots in working environments and assist humans, it is essential to develop and evaluate how visual grounding (VG) can affect machine performance on occluded objects. However, current VG works are limited in working environments, such as offices and warehouses, where objects are usually occluded due to space utilization issues. In our work, we propose a novel OCID-Ref dataset featuring a referring expression segmentation task with referring expressions of occluded objects. OCID-Ref consists of 305,694 referring expressions from 2,300 scenes with providing RGB image and po…
VLM-Grounder: A VLM Agent for Zero-Shot 3D Visual Grounding
The Chinese University of Hong Kong（香港中文大学）；Shanghai AI Laboratory（上海人工智能实验室）；Zhejiang University（浙江大学）；Centre for Perceptual and Interactive Intelligence（感知与交互智能中心）
SeCG: Semantic-Enhanced 3D Visual Grounding via Cross-modal Graph Attention
School of Automation Science and Engineering, South China University of Technology（华南理工大学自动化科学与工程学院）；School of Software Engineering, South China University of Technology（华南理工大学软件学院）
3DAxisPrompt: Promoting the 3D Grounding and Reasoning in GPT-4o
Shanghai AI Lab（上海人工智能实验室）；Dalian University of Technology（大连理工大学）；School of Geodesy and Geomatics, Wuhan University（武汉大学测绘学院）；The Chinese University of Hong Kong（香港中文大学）；Tsinghua University（清华大学）
提出 3DAxisPrompt，通过 3D 坐标轴与 SAM 掩码为 GPT-4o 提供几何先验，并在四个数据集上验证其 3D 定位与推理能力。
Technical University of Munich（慕尼黑工业大学）；Google（谷歌）
We present SPSG, a novel approach to generate high-quality, colored 3D models of scenes from RGB-D scan observations by learning to infer unobserved scene geometry and color in a self-supervised fashion. Our self-supervised approach learns to jointly inpaint geometry and color by correlating an incomplete RGB-D scan with a more complete version of that scan. Notably, rather than relying on 3D reconstruction losses to inform our 3D geometry and color reconstruction, we propose adversarial and perceptual losses operating on 2D renderings in order to achieve high-resolution, high-quality colored …
RGB-D scanning of indoor environments is important for many applications, including real estate, interior design, and virtual reality. However, it is still challenging to register RGB-D images from a hand-held camera over a long video sequence into a globally consistent 3D model. Current methods often can lose tracking or drift and thus fail to reconstruct salient structures in large environments (e.g., parallel walls in different rooms). To address this problem, we propose a "fine-to-coarse" global registration algorithm that leverages robust registrations at finer scales to seed detection an…
Technical University of Munich（慕尼黑工业大学）；Simon Fraser University（西蒙弗雷泽大学）
We present Scan2CAD, a novel data-driven method that learns to align clean 3D CAD models from a shape database to the noisy and incomplete geometry of a commodity RGB-D scan. For a 3D reconstruction of an indoor scene, our method takes as input a set of CAD models, and predicts a 9DoF pose that aligns each model to the underlying scan geometry. To tackle this problem, we create a new scan-to-CAD alignment dataset based on 1506 ScanNet scans with 97607 annotated keypoint pairs between 14225 CAD models from ShapeNet and their counterpart objects in the scans. Our method selects a set of represen…
