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Christoffer Koo Øhrstrøm

@chrisohrstrom.bsky.social
178 followers 468 following 19 posts

PhD student at DTU 🇩🇰 Doing research at the intersection of deep learning, event cameras/neuromorphic vision, multi-modal models, and robotics. chrisohrstrom.github.io

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Reposted by Christoffer Koo Øhrstrøm
Jakub Gregorek @jakubgregorek.bsky.social · 04/09/2026
🚀 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
arxiv.org
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...
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Christoffer Koo Øhrstrøm @chrisohrstrom.bsky.social · 04/02/2026
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 🧵
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Christoffer Koo Øhrstrøm @chrisohrstrom.bsky.social · 03/11/2025
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
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Reposted by Christoffer Koo Øhrstrøm
Lazaros Nalpantidis @lanalpa.bsky.social · 08/01/2025
Can Dynamic Neural Networks boost Computer Vision and Sensor Fusion? We are very happy to share this awesome collection of papers on the topic!
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Reposted by Christoffer Koo Øhrstrøm
Tanishq Mathew Abraham @iscienceluvr.bsky.social · 10/12/2024
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
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