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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
Looking forward to hear it 🤞 Happy to help if there is more you need.
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Christoffer Koo Øhrstrøm @chrisohrstrom.bsky.social · 04/02/2026
Always happy to compare to good and interesting work :)
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Christoffer Koo Øhrstrøm @chrisohrstrom.bsky.social · 04/02/2026
Our experiments use absolute. It would probably work about the same with normalized coordinates though, but I recon it likely requires fiddling a bit with the initialization range of the projection matrix (W_p) if you prefer normalized.
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Christoffer Koo Øhrstrøm @chrisohrstrom.bsky.social · 04/02/2026
Thanks! I am not too familiar with those tasks, but no I don't think it should be hard to test. And it would be quite interesting to do. Our code is available and the implementation is plug-and-play with standard attention. You only need to give it the nD-position of each token.
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Christoffer Koo Øhrstrøm @chrisohrstrom.bsky.social · 04/02/2026
And here it is. Maybe something along these lines you were thinking of? Designed directly for vision, tested on 2D, 2D-T, 3D, and multi-modal, and it extrapolates very well. Paper: arxiv.org/abs/2602.01418 Website: chrisohrstrom.github.io/parabolic-po... Code: github.com/DTU-PAS/para...
chrisohrstrom.github.io
Parabolic Position Encoding
Where to Attend: A Principled Vision-Centric Position Encoding with Parabolas
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Christoffer Koo Øhrstrøm @chrisohrstrom.bsky.social · 04/02/2026
Where to Attend: A Principled Vision-Centric Position Encoding with Parabolas Paper: arxiv.org/abs/2602.01418 Website: chrisohrstrom.github.io/parabolic-po... Code: github.com/DTU-PAS/para... @rgring.bsky.social @lanalpa.bsky.social
arxiv.org
Where to Attend: A Principled Vision-Centric Position Encoding with Parabolas
We propose Parabolic Position Encoding (PaPE), a parabola-based position encoding for vision modalities in attention-based architectures. Given a set of vision tokens-such as images, point clouds, vid...
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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 · 12/01/2026
Actually working on a principled encoding for 2D, 2D-T, and 3D. Coming soon in a couple of weeks ;)
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Christoffer Koo Øhrstrøm @chrisohrstrom.bsky.social · 08/11/2025
Congratulations. You are now officially Danish.
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Christoffer Koo Øhrstrøm @chrisohrstrom.bsky.social · 03/11/2025
Thanks to my collaborators @rgring.bsky.social @lanalpa.bsky.social. Try it out for yourself: github.com/DTU-PAS/spik...
github.com
GitHub - DTU-PAS/spiking-patches: Spiking Patches: Asynchronous, Sparse, and Efficient Tokens for Event Cameras
Spiking Patches: Asynchronous, Sparse, and Efficient Tokens for Event Cameras - DTU-PAS/spiking-patches
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Christoffer Koo Øhrstrøm @chrisohrstrom.bsky.social · 03/11/2025
We also get a much smaller input sizes with up to a 6.9x reduction over voxels and up to a 8.9x reduction over frames.
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Christoffer Koo Øhrstrøm @chrisohrstrom.bsky.social · 03/11/2025
Results are pretty good. Inference speedups are up to 3.4x over voxels for a point cloud network and up to 10.4x over frames for a Transformer. This comes without sacrificing accuracy. We even outperform voxels and frames in most cases on gesture recognition and object detection.
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Christoffer Koo Øhrstrøm @chrisohrstrom.bsky.social · 03/11/2025
Spiking Patches works by creating a grid of patches and let each patch act as spiking neuron. A patch increases its potential whenever an event arrives within the patch, and a token is created everytime a patch spikes (when the potential exceeds a threshold).
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Christoffer Koo Øhrstrøm @chrisohrstrom.bsky.social · 03/11/2025
We achieve this through tokenization of events. Our tokenizer is called Spiking Patches. Something cool is that tokens are compatible with GNNs, PCNs, and Transformers. This is the first time that anyone applies tokenization to events. We hope to encourage more of this.
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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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Christoffer Koo Øhrstrøm @chrisohrstrom.bsky.social · 13/10/2025
How is external links to be understood? Is it e.g. okay to link to a video (not our own) with examples of a concept that we describe as a preliminary?
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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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Christoffer Koo Øhrstrøm @chrisohrstrom.bsky.social · 18/12/2024
True. Not much of an issue on small codebases. Mostly just feels better with a snappier formatter for those.
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Christoffer Koo Øhrstrøm @chrisohrstrom.bsky.social · 18/12/2024
black is great, but I prefer Ruff because of speed and it is also a really nice linter. docs.astral.sh/ruff/
docs.astral.sh
Ruff
An extremely fast Python linter and code formatter, written in Rust.
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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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Christoffer Koo Øhrstrøm @chrisohrstrom.bsky.social · 28/11/2024
Maybe this is it? arxiv.org/abs/2409.10202 @jakubgregorek.bsky.social
arxiv.org
SteeredMarigold: Steering Diffusion Towards Depth Completion of Largely Incomplete Depth Maps
Even if the depth maps captured by RGB-D sensors deployed in real environments are often characterized by large areas missing valid depth measurements, the vast majority of depth completion methods st...
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