GeoRefer-Bench is a new benchmark for verifiable geospatial referring segmentation that evaluates whether models correctly resolve spatial relations in overhead imagery. Each query is expressed as an executable logical form over a metric scene graph, and predictions are scored with Exact Query Success (EQS), requiring an exact match to the query’s referent set. The dataset contains 700 UAV scenes, 26,217 instances, 142,796 spatial relations, 20,916 executable queries across five reasoning levels, and additional paraphrases, unanswerable queries, counterfactual pairs, and leakage‑controlled splits.
By Shuaishuai Cao, Min Huang, Meng Tang, Xuan Liu, Youjin Wang, Hui Lin
arXiv:2608.20659v1 Announce Type: new
Abstract: Methods that transfer predictions from two-dimensional foundation models into three-dimensional segmentation are commonly grouped by task or representa...
By Wentao Sun, Yiping Chen, John S. Zelek, Jonathan Li
arXiv:2609.36906v1 Announce Type: new
Abstract: Reliable embodied decisions under partial observability require informative observations and sufficient supporting evidence. However, semantic scores a...
By Sean Hardesty Lewis, Zuyi Guo, Benwang Chen, Zirui Liu, Hongyi Lin, Heye Huang
AOV-GS is an active 3D Gaussian mapping system that uses frozen foundation models (SAM and CLIP) to guide exploration toward semantically informative regions. During coarse exploration, it re‑ranks geometrically promising views based on CLIP mask‑embedding novelty from SAM instance segmentation, biasing the robot’s trajectory. The system fits a lightweight scene‑specific language field offline to the collected CLIP embeddings, enabling open‑vocabulary text‑query segmentation on novel views and improving language‑field mIoU by 1.3–1.8 percentage points over geometry‑only planning on Replica and Matterport3D.
By Rodion Shkokov, Tatiana Zemskova, Dmitry Yudin
arXiv:2611.06020v1 Announce Type: new
Abstract: A 3D Gaussian Splatting (3DGS) reconstruction inherits arbitrary units from structure-from-motion, so it cannot be measured or used to place AR content...
By Sooyeun Yang, Jongseong Brad Choi
arXiv:2607. 07737v1 Announce Type: cross Abstract: GNSS-denied unmanned aerial vehicles require occasional absolute position fixes to bound the drift of visual-inertial odometry.
By Natalia Trukhina, Vadim Vashkelis
arXiv:2606.19828v2 Announce Type: replace
Abstract: 3D multimodal large language models (3D MLLMs) describe a 3D object as a whole but cannot address, name, or reason about its parts. Prior part-awar...
By Jintang Xue, Xinyu Wang, Yixing Wu, Jingwen Chen, C. -C. Jay Kuo
RoboPhys-3D is a 3D‑grounded embodied world model benchmark built on RoboTwin 2.0, featuring 50 manipulation tasks, 5,000 episodes, and 25,000 multi‑view ground‑truth videos. It evaluates video world models by processing both generated and ground‑truth videos through the same 3D reconstruction pipeline, allowing the separation of reconstruction‑induced from generation‑induced errors. The benchmark defines 50 metrics across four sub‑dimensions—pixel fidelity, 3D geometry consistency, state understanding, and task completeness—and introduces the Average Full Score and RoboPhyscore for holistic assessment, with RoboPhyscore showing strong correlation with human judgments.
By Tianyi Wang, Jiazhou Chen, Yiming Xu, Xiangyu Li, Tianyi Zeng, Chih-Hsien Chou, Ning Lu, Liang Peng, Junfeng Jiao, Christian Claudel
SpatialMem is a memory-based system that fuses 3D geometry, semantics, and language into a single queryable representation. Starting from casually recorded egocentric RGB video, it reconstructs metrically scaled interior scenes, identifies structural 3D anchors (walls, doors, windows), and links open‑vocabulary object nodes to evidence patches, visual embeddings, and two‑layer descriptions at 3D coordinates, enabling spatial‑relation reasoning, language‑directed retrieval, and QA without special sensors. The system is evaluated at scale on public benchmarks, achieving competitive scores on VSI‑Bench, OpenEQA, and ScanNet, while maintaining a compact index of about 14 KB per scene.
By Xinyi Zheng, Yunze Liu, Chi-Hao Wu, Fan Zhang, Hao Zheng, Wenqi Zhou, Walterio W. Mayol-Cuevas, Junxiao Shen
The paper introduces Linguistic Trajectory Encoding (LTE), a hybrid representation that compresses dynamic object motion histories using natural language descriptions, sparse spatial anchors, and visual anchors. LTE adapts compression to motion complexity by anchoring periods without reliable observations to the last seen location while preserving accuracy with geometric waypoints and linguistic descriptions. Evaluated on the newly constructed Spatial Memory Benchmark (SMB) from EgoLife multi‑day recordings, LTE achieves 45.3 % success in semantic trajectory retrieval and 48.7 % in long‑horizon object retrieval, outperforming prior structured‑memory and VLM baselines, and compresses trajectories 8.7×–26.1× with sub‑second query latency on 24‑hour video.
By Tianyidan Xie, Shenyi Wang, Qiang Tang, Mingjie Wang, Zhicheng Qiu, Xuanfu Li, Zhan Xu, Jian Yang, Lanjun Wang, Zili Yi
The paper introduces Multimodal Filesystem Memory (MFM), a memory architecture for long‑horizon embodied agents that stores episodic, spatial, semantic, procedural, and working memories. MFM uses searchable textual records to link these representations to multimodal episodes, enabling agents to retrieve past experiences via contextual cues and revisit original observations for missing details. Experiments on long‑horizon Minecraft and computer‑use tasks show that MFM improves performance over three base models, achieving higher milestone completion and reward scores compared to baselines.
By Chloe Su, Jiahan Li, Zhenting Qi, Chaoran Cheng, Haoyu Chen, Sejoon Jun, Weirui Ye, Yilun Du
SPARK is a corrective multi‑episode framework for embodied retrieval‑augmented generation that uses a semantic critic, a targeted planner, and selective merging to refine a knowledge base after each episode. In experiments on four AirSim outdoor maps under degraded viewpoints, a single corrective episode improves embodied question‑answering performance on every map while reducing stored objects by 13–41 %. Extending correction to four episodes surpasses the performance of the undegraded reference on all maps, and ablations confirm the importance of each component.
By Dmytro Kurdydyk, Aadi Palnitkar, Sebastian Scherer, Seungchan Kim