Recent advances in 3D scene-language understanding have leveraged Large Language Models (LLMs) for 3D reasoning by transferring their general reasoning ability to 3D multi-modal contexts. However, existing methods typically adopt standard decoders from language modeling, which rely on a causal attention mask. This design introduces two fundamental conflicts in 3D scene understanding: sequential bias among order-agnostic 3D objects and restricted object-instruction attention, hindering task-specific reasoning. To overcome these limitations, we propose 3D Spatial Language Instruction Mask (3D-SLIM), an effective masking strategy that replaces the causal mask with an adaptive attention mask tailored to the spatial structure of 3D scenes. Our 3D-SLIM introduces two key components: a Geometry-adaptive Mask that constrains attention based on spatial density rather than token order, and an Instruction-aware Mask that enables object tokens to directly access instruction context. This design allows the model to process objects based on their spatial relationships while being guided by the user's task. 3D-SLIM is simple, requires no architectural modifications, and adds no extra parameters, yet it yields substantial performance improvements across diverse 3D scene-language tasks. Extensive experiments across multiple benchmarks and LLM baselines validate its effectiveness and underscore the critical role of decoder design in 3D multi-modal reasoning.
Turning Generators into Retrievers: Unlocking MLLMs for Natural Language-Guided Geo-Localization
Vermont Artificial Intelligence Lab, Department of Computer Science, University of Vermont（佛蒙特大学计算机科学系佛蒙特人工智能实验室）；Intelligent Machines Lab, Department of Artificial Intelligence, Information Technology University（信息技术大学人工智能系智能机器实验室）；Institute of Artificial Intelligence, University of Central Florida（中佛罗里达大学人工智能研究所）
Text2Earth: Unlocking Text-driven Remote Sensing Image Generation with a Global-Scale Dataset and a Foundation Model
Beihang University（北京航空航天大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Shen Yuan Honors College of Beihang University（北京航空航天大学沈元荣誉学院）；National University of Singapore（新加坡国立大学）
Unlocking Compositional Generalization in Continual Few-Shot Learning
Faculty of Information Technology, University of Science, Vietnam National University（信息科技学院，科学大学，越南国家大学）；Department of Computer Science, University of Warwick（计算机科学系，沃里克大学）
MM-3DScene: 3D Scene Understanding by Customizing Masked Modeling with Informative-Preserved Reconstruction and Self-Distilled Consistency
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
Many Multi-View-Stereo algorithms extract a 3D mesh model of a scene, after fusing depth maps into a volumetric representation of the space. Due to the limited scalability of such representations, the estimated model does not capture fine details of the scene. Therefore a mesh refinement algorithm is usually applied; it improves the mesh resolution and accuracy by minimizing the photometric error induced by the 3D model into pairs of cameras. The choice of these pairs significantly affects the quality of the refinement and usually relies on sparse 3D points belonging to the surface. Instead, i…
Unlocking Generalization Power in LiDAR Point Cloud Registration
School of Computer Science, Northwestern Polytechnical University（西北工业大学计算机学院）；Ningbo Institute, Northwestern Polytechnical University（西北工业大学宁波研究院）；Swansea University（斯旺西大学）；HuaZhong University of Science and Technology（华中科技大学）
GeoHand: Unlocking Prior Geometry Knowledge for Monocular 3D Hand Reconstruction
School of Artificial Intelligence, Xidian University（西安电子科技大学人工智能学院）；Zhongguancun Academy（中关村学院）；School of Automation, Beijing Institute of Technology（北京理工大学自动化学院）
Scene text recognition (STR) enables computers to recognize and read the text in various real-world scenes. Recent STR models benefit from taking linguistic information in addition to visual cues into consideration. We propose a novel Masked Vision-Language Transformers (MVLT) to capture both the explicit and the implicit linguistic information. Our encoder is a Vision Transformer, and our decoder is a multi-modal Transformer. MVLT is trained in two stages: in the first stage, we design a STR-tailored pretraining method based on a masking strategy; in the second stage, we fine-tune our model a…
In this paper, we propose a broad comparison between Fully Convolutional Networks (FCNs) and Mask Region-based Convolutional Neural Networks (Mask-RCNNs) applied in the Salient Object Detection (SOD) context. Studies in the SOD literature usually explore architectures based in FCNs to detect salient regions and objects in visual scenes. However, besides the promising results achieved, FCNs showed issues in some challenging scenarios. Fairly recently studies in the SOD literature proposed the use of a Mask-RCNN approach to overcome such issues. However, there is no extensive comparison between …
University of Westminster（威斯敏斯特大学）；NVIDIA Corporation（英伟达公司）
Spatial image quality metrics designed for camera systems generally employ the Modulation Transfer Function (MTF), the Noise Power Spectrum (NPS), and a visual contrast detection model. Prior art indicates that scene-dependent characteristics of non-linear, content-aware image processing are unaccounted for by MTFs and NPSs measured using traditional methods. We present two novel metrics: the log Noise Equivalent Quanta (log NEQ) and Visual log NEQ. They both employ scene-and-process-dependent MTF (SPD-MTF) and NPS (SPD-NPS) measures, which account for signal-transfer and noise scene-dependenc…
Robust Bimanual Vision-Language-Action Models via Embarrassingly Simple Modality Masking
Shanghai Innovation Institute（上海创新研究院）；Wuhan University（武汉大学）；Zhejiang University（浙江大学）；Shanghai Jiao Tong University（上海交通大学）；University of Science and Technology of China（中国科学技术大学）；City University of Hong Kong（香港城市大学）；Southeast University（东南大学）
Hierarchical and Holistic Open-Vocabulary Functional 3D Scene Graphs for Indoor Spaces
Tsinghua University（清华大学）；ETH Zürich（苏黎世联邦理工学院）；MPI for Informatics（信息研究所）；Dalian University of Technology（大连理工大学）；Microsoft（微软）；Stanford University（斯坦福大学）；University of Lugano（卢加诺大学）
Single view depth estimation models can be trained from video footage using a self-supervised end-to-end approach with view synthesis as the supervisory signal. This is achieved with a framework that predicts depth and camera motion, with a loss based on reconstructing a target video frame from temporally adjacent frames. In this context, occlusion relates to parts of a scene that can be observed in the target frame but not in a frame used for image reconstruction. Since the image reconstruction is based on sampling from the adjacent frame, and occluded areas by definition cannot be sampled, r…
University of Bonn（波恩大学）；Delft University of Technology（代尔夫特理工大学）
Novel view synthesis is required in many robotic applications, such as VR teleoperation and scene reconstruction. Existing methods are often too slow for these contexts, cannot handle dynamic scenes, and are limited by their explicit depth estimation stage, where incorrect depth predictions can lead to large projection errors. Our proposed method runs in real time on live streaming data and avoids explicit depth estimation by efficiently warping input images into the target frame for a range of assumed depth planes. The resulting plane sweep volume (PSV) is directly fed into our network, which…
Rectifying Mask via Entropy for Distractor-Free 3DGS in Ambiguous Scenarios
Samsung Electronics（三星电子）；Georgia Institute of Technology（佐治亚理工学院）；GenGenAI；Yonsei University（延世大学）；Seoul National University（首尔国立大学）；Kennesaw State University（肯纳邦州立大学）；University of Texas at Arlington（德克萨斯大学阿灵顿分校）
Photorealistic Inpainting for Perturbation-based Explanations in Ecological Monitoring
Duke University（杜克大学）；University of Agder（阿格德大学）；University of Cambridge（剑桥大学）；U.S. National Park Service Department of Interior（美国国家公园管理局）；Alaska Spatial Science（阿拉斯加空间科学）
3D Scene Graph Prediction: Generating Hierarchical Models from Partially Observed Environments
Laboratory for Information & Decision Systems (LIDS), Massachusetts Institute of Technology（信息与决策系统实验室（LIDS），麻省理工学院）；Department of Electrical and Computer Engineering, Oakland University（奥克兰大学电气与计算机工程系）
Osaka University（大阪大学）；Osaka Institute of Technology（大阪工业大学）
Despite remarkable progress in image translation, the complex scene with multiple discrepant objects remains a challenging problem. The translated images have low fidelity and tiny objects in fewer details causing unsatisfactory performance in object recognition. Without thorough object perception (i.e., bounding boxes, categories, and masks) of images as prior knowledge, the style transformation of each object will be difficult to track in translation. We propose panoptic-aware generative adversarial networks (PanopticGAN) for image-to-image translation together with a compact panoptic segmen…
