arXiv:2607. 11548v1 Announce Type: cross Abstract: Spatial football metrics such as pitch control assume access to the positions of all 22 players, yet the most widely available source of positional data -- the broadcast main camera -- shows only 10-16 of them at any moment.
By Seongjin Choi
The paper introduces Graph-to-Grid (G2G), a method that paints continuous‑coordinate player features onto a grid using bilinear interpolation, enabling end‑to‑end training of per‑player encoders for pass surface prediction. On 53,628 World Cup passes, this painting approach improves selection likelihood by about a quarter of a nat compared to raster‑only inputs, with further gains from learned encoders and message passing. The study also shows that painting benefits existing models like SoccerMap and U‑Net, while other variants such as offset channels or raster‑free decoders do not provide similar improvements.
By Kaan G\"unay, Orhun Gun
Location encoders transform geographic coordinates into high‑dimensional embeddings for machine learning, yet it is unclear how well these embeddings capture interpretable spatial effects. This study benchmarks GeoShapley—a game‑theoretic explainer treating all location features as a single joint player—against eleven TorchSpatial encoders on a synthetic process with known coefficients, across grid, county, and global scales, with and without raw coordinates and under different training regimes. The results show that primary coefficient recovery is consistently high across encoders, while secondary coefficient recovery varies more with scale, especially at the global level, and raw‑coordinate baselines remain competitive throughout.
By Daniel Kiv, Shaowen Wang
OVO‑S‑Bench is a fully human‑annotated benchmark designed to evaluate streaming spatial intelligence in multimodal large language models (MLLMs). It contains 1,680 questions derived from 348 source videos, each with a query timestamp and evidence interval, and tests models on four levels of abstraction: instantaneous egocentric perception, spatiotemporal context tracking, generative spatial reasoning, and allocentric spatial mapping. Across 38 MLLMs, Gemini‑3.1‑Pro scored 59.2 versus 92.2 for human experts, with allocentric spatial mapping identified as the main challenge, and the benchmark reveals that chain‑of‑thought reasoning can worsen spatial errors when not grounded in the stream.
By Yifei Li, Pengyiang Liu, Yuhang Zang, Zhongyue Shi, Qi Fu, Hongye Hao, Jiwen Lu
arXiv:2610.19520v1 Announce Type: new
Abstract: Cameras and the foundation models built on them recognize what a 3D scene contains, but not what it is made of: surfaces of the same colour can be diff...
By Chia-Ming Lee, Yu-Jou Hsiao, Ming-Ching Chang, Xin Li, Yu-Lun Liu, Chih-Chung Hsu
arXiv:2610.32894v1 Announce Type: new
Abstract: Location encoders map geographic coordinates to high-dimensional descriptors through contrastive alignment with paired Earth Observation imagery. We pr...
By Panagiotis Koromilas, Eleanna Vali, Diogenis Antonopoulos, Eleni Saka, Dimitris Christofis, Nikolaos Ioannis Bountos, Konstantin Klemmer, Ioannis Papoutsis, Yannis Panagakis, Mihalis A. Nicolaou