The article titled "Jigsaw Jeeves: Building a Puzzle Assistant using Computer Vision" provides a conceptual overview and a step‑by‑step walkthrough of a solution approach implemented in Python. It explains how computer vision techniques can be applied to assist users in solving jigsaw puzzles, detailing the design and implementation choices made in the project. The post serves as a practical guide for developers interested in combining vision algorithms with puzzle‑solving applications.
arXiv:2609.14473v1 Announce Type: new Abstract: Personal AI assistants hold the potential to evolve from digital interfaces into embodied companions capable of guiding users through complex physical...
arXiv:2607. 27670v2 Announce Type: replace-cross Abstract: Jigsaw puzzle solving requires jointly reasoning about visual content and geometric constraints, yet existing benchmarks use rectangular cuts that create ambiguous ground truth in texture-repeated regions.
arXiv:2604.22875v3 Announce Type: replace-cross Abstract: When answering questions about images, humans naturally point, label, and draw to explain their reasoning. In contrast, modern vision-languag...
The article titled "Computer Vision: SIFT algorithm (Scale Invariant Feature Transform)" discusses the SIFT algorithm, a method for matching objects across different viewpoints. It highlights the elegance of this approach in handling variations in scale and orientation. The post was originally published on Towards Data Science.
arXiv:2503. 19990v4 Announce Type: replace Abstract: Many real-world applications of spatial intelligence, such as robotic control, autonomous driving, and automated assembly, require spatial reasoning across multiple sequential steps.
arXiv:2608. 07955v1 Announce Type: new Abstract: Large vision-language models have achieved strong performance in multimodal reasoning, but they remain unreliable on fine-grained spatial tasks that demand both precise spatial perception and fine-grained geometric computation beyond end-to-end generation.
arXiv:2608.22174v1 Announce Type: new Abstract: Unified multimodal models (UMMs) can perform both understanding and generation, raising a central question: can visual generation improve understanding...
Posted by Mark Matthews, Senior Software Engineer, and Dmitry Lagun, Research Scientist, Google Research A person's prior experience and understanding of the world generally enables them to easily infer what an object looks like in whole, even if only looking at a few 2D pictures of it. Yet the capacity for a computer to reconstruct the shape of an object in 3D given only a few images has remained a difficult algorithmic problem for years.
arXiv:2607. 25537v1 Announce Type: cross Abstract: In the age of foundation models, a model is only as good as its prompt.
Simon Willison demonstrates how to use Blender with coding agents on macOS by installing the full Mac application from blender.org and issuing prompts to ChatGPT Codex. He shows a step-by-step example where the agent renders a scene of a pelican riding a bicycle, then adds background flair and improves the image. The resulting image is generated via Blender's Python API.
Posted by Srinivas Sunkara and Gilles Baechler, Software Engineers, Google Research Screen user interfaces (UIs) and infographics, such as charts, diagrams and tables, play important roles in human communication and human-machine interaction as they facilitate rich and interactive user experiences. UIs and infographics share similar design principles and visual language (e.
arXiv:2404. 01299v3 Announce Type: replace-cross Abstract: Causal video question answering (QA) has garnered increasing interest, yet existing datasets often lack depth in causal reasoning.