🚀 Our paper is at #TAROS 2026 in Manchester, 7–9 Sep! 🤖   We present ShotcreteDepth 🏗️ — a bi-modal #dataset for robotic #depth #perception in dusty, low-light #shotcrete environments. 📄 arxiv.org/abs/2606.23152 🔗 github.com/dtu-pas/shot... @lanalpa.bsky.social @aicentre.dk

ShotcreteDepth: A Bi-modal Dataset for Robust Robotic Depth Perception in Shotcrete Construction Environments

We introduce ShotcreteDepth, a bi-modal dataset from the construction domain that captures both an active shotcreting process and general construction environments. The dataset comprises stereo RGB im...

arxiv.org

What if position encodings were designed for vision from scratch? We introduce PaPE—Parabolic Position Encoding. Outperforms RoPE on 7/8 datasets and extrapolates to higher resolutions without fine-tuning or position interpolation. Paper, code, and website in thread 🧵

What if we could represent events (event cameras) in a way that preserves both asynchrony and spatial sparsity? Exited to share our latest work where we answer this question positively. Spiking Patches: Asynchronous, Sparse, and Efficient Tokens for Event Cameras Paper: arxiv.org/abs/2510.26614

Can Dynamic Neural Networks boost Computer Vision and Sensor Fusion? We are very happy to share this awesome collection of papers on the topic!

Fabio Montello@zioictus.bsky.social · 2y ago

First little project of the year: an awesome collection of papers on Dynamic Neural Networks for Computer Vision and Sensor Fusion! Each paper comes with a brief summary and code link. 👉 github.com/DTU-PAS/awes...

Inventors of flow matching have released a comprehensive guide going over the math & code of flow matching! Also covers variants like non-Euclidean & discrete flow matching. A PyTorch library is also released with this guide! This looks like a very good read! 🔥 arxiv: arxiv.org/abs/2412.06264