VideoReloc presents a method for long‑term indoor video relocalization that relies on a compact semantic scene graph rather than visual appearance. By adaptively selecting clip lengths based on odometry and object‑motion criteria, the system gathers spatial evidence, verifies poses through object triplets, and refines orientation using box faces and gravity cues. This approach achieves high localization accuracy with a tiny 100 kB map, outperforming traditional appearance‑based methods on RIO10 and ReplicaCAD datasets.
By Qianru Li, Xuyang Chen, Xuqin Wang, Zhenghao Zhang, Hongyi Luo, Tao Wu, Daniel Cremers, Lu Liu, Yanfeng Zhang
arXiv:2610.34033v1 Announce Type: new
Abstract: Camera motion recognition requires separating viewpoint changes caused by camera translation, rotation, or zoom from object motion and scene dynamics....
By Xiangkun Shi, Hanhui Li, Shiyue Zhang, Chang Liu, Junwen Pan, Zhenbang Sun, Xiaodan Liang
arXiv:2605. 16713v2 Announce Type: replace-cross Abstract: Modern Vision-Language Models (VLMs) achieve strong semantic recognition, yet remain brittle on elementary spatial relations such as left of, on, behind, and between.
By Renjie Gu, Kaichen Zhou, Yan Luo, Mengyu Wang
GeoFlip is a new benchmark for video generation models that tests their ability to produce physically correct outcomes when the initial geometry of a scene is altered. The benchmark presents 482 pairs of scenes with controlled changes in extent, placement, and mechanism configuration, and evaluates models on event correctness, structural integrity, and similarity to reference videos. Across 16 evaluated models, the best event correctness averaged only 27.98%, highlighting significant gaps in geometry‑sensitive physical reasoning.
By Yuqi Li, Junming Liu, Bowen Hu, Ruize Niu, Huiran Duan, Yingli Tian, Hao Wu
arXiv:2610.42002v2 Announce Type: replace
Abstract: Video generation models synthesize realistic motion, but can they respond correctly to changes in initial geometry? We introduce GeoFlip, a video g...
By Yuqi Li, Junming Liu, Bowen Hu, Ruize Niu, Huiran Duan, Ming-Hsuan Yang, Yingli Tian, Hao Wu
OmniAct3D is a framework that adapts Vision Foundation Model (VFM) detectors trained on perspective images to equirectangular projection (ERP) panoramic images for 3D detection. It introduces the ERP-Ray Geometry Adapter (ERGA-Ray) to model spherical rays, the Visual-Action Reasoning Chain (VARC) to ground hypotheses in panoramic evidence, and the Appearance-Guided Heading Expert (AGHE) to recover high‑resolution heading cues. The method achieves a 2.96 NDS improvement on Spheriverse and a 24.87 mAP gain on PanoMMOcc, while preserving most of the baseline’s performance across configurations.
By Runtong Wu, Fei Teng, Di Wen, Guoqiang Zhao, Kunyu Peng, Kailun Yang
MVLoMa is a sparse star‑graph matcher that extends the LoMa framework from image pairs to multi‑view settings. It uses a frozen multi‑view geometry model to jointly describe all images in a star, injecting cross‑view geometric context into local descriptors and matching the centre image to all neighbours in a single forward pass. On several datasets, MVLoMa achieves the best macro‑average relative‑pose AUC and is more than twice as fast as sequential pairwise evaluation, while supporting multiple frozen geometry encoders and demonstrating the benefits of geometry‑aware descriptors, visibility filtering, and joint inference.
By Xinyang Lu, Shengzhe Wei, Sheng Tang, Weiqi Zhang, Juntong Fang, Zesong Li
MVLoMa is a sparse star‑graph matcher that extends the LoMa local‑feature matching framework from image pairs to multi‑view settings. It uses a frozen multi‑view geometry model to jointly describe all images in a star, injecting cross‑view geometric context into local descriptors and performing centre‑to‑neighbour matching in a single forward pass with per‑view self‑attention and bidirectional centre‑neighbour cross‑attention. On 25‑view star graphs from HyperSim, ScanNet++, Mip‑NeRF 360, and Tanks and Temples, MVLoMa achieves the best macro‑average relative‑pose AUC at all thresholds and is 2.20 times faster than sequential pairwise evaluation, while supporting multiple frozen geometry encoders and demonstrating the benefits of geometry‑aware descriptors, visibility filtering, geometric negative supervision, and joint multi‑view inference.
DK‑VLM introduces Dual‑Kernel Attention, where a visual token’s attention is computed from both its input position and its geometric position derived from 3D points and camera poses. A semantic kernel preserves pretrained attention while a geometric kernel modulates attention via point displacements and relative view transformations, with a learned gate fusing the two. The model also employs Reference‑Frame Composition supervision to update its reference frame across successive judgments, achieving top scores on six spatial benchmarks and strong performance on VSI‑Bench and ReVSI‑32.
By Han Su, Meng Tian, Tianyu Huang, Mingyu Gao, Lennox Wei, Dongxu Li, Jianhua Han, Hang Xu, Xiaohe Wu, Wangmeng Zuo
TALON introduces Token‑Aligned Lightweight Adapters for 6‑DoF spacecraft pose estimation, inserting spatiotemporal 3D adapters before self‑attention layers of a frozen Vision Transformer. A patch‑token alignment loss grounds adapted features to keypoint structure, enabling the frozen backbone to reason over temporally enriched tokens with minimal added parameters. On the SPADES and SwissCube datasets, TALON cuts pose error by 50% and improves ADD‑0.1d accuracy by 21.8%, respectively, while also achieving significant gains in zero‑shot sim‑to‑real cross‑domain evaluation.
By Abid Ali, Nidhal Eddine Chenni, Arunkumar Rathinam, Djamila Aouada
arXiv:2605.12938v2 Announce Type: replace-cross
Abstract: Video world models should predict future appearance in a way that remains consistent with 3D scene structure, camera motion, and lens geometr...
By Seonghyun Jin, Youngmin Kim, Sunwoo Park, Jong Chul Ye
The paper introduces Parabolic Positional Encoding (PaPE), a parabola‑based method for encoding positions of vision tokens in attention‑based models. PaPE is designed to respect vision‑specific properties such as translation and rotation invariance, distance decay, directionality, and context awareness. Experiments on ImageNet‑1K and eight other datasets across four modalities show that PaPE extrapolates well—improving performance by up to 10.5 points over the next best encoding—and generally matches or surpasses baseline encodings.
By Christoffer Koo {\O}hrstr{\o}m, Rafael I. Cabral Muchacho, Yifei Dong, Filippos Moumtzidellis, Ronja G\"uldenring, Florian T. Pokorny, Lazaros Nalpantidis