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✨ Massive Pipeline Refactor → One Framework for Ego + Exo Datasets, Visualized with Rerun 🚀 After a deep refactoring and cleanup, my entire egocentric/exocentric pipeline is now fully modular. One codebase handles different sensor layouts and generates a unified, multimodal timeseries RRD file that you can open instantly...

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Pablo Vela

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20,836 views • 11 months ago •via X (Twitter)

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Pablo Vela11 months ago

Try it / follow along Code is landing in the pi0-lerobot repo (hand‑kinematic‑fitting branch): < The current viewer can be found here: < Stay tuned—Gradio front‑end + full‑body exo labels up next!

Himanshu Kumar's profile picture
Himanshu Kumar11 months ago

@rerundotio Intriguing, a unified pipeline could streamline varied data analysis in unexpected ways.

Astrid Wilde 🌞's profile picture
Astrid Wilde 🌞11 months ago

@rerundotio are you looking for work? if so call me

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We have HOT3D! I've started using Claude to port more datasets into Rerun and exoego-forge. I'd been meaning to bring in the HOT3D dataset from Meta for a while, but with Claude, it's way easier. My goal is to take any egocentric, exocentric, or both datasets and ingest them into a standardized schema. Getting everything into Rerun means we can easily query and transform data via the in-memory OSS server. This lets us generate SQL-like queries such as: "Find me all frames that only contain left hands in the leftmost camera view." Most people think of Rerun as a viewer, but this is the actual superpower. So far we have: 1. HOT3D 2. Hocap 3. UmeTrack 4. Assembly101 5. EgoDex Planning to add more, and with every addition, it gets easier as we build up agent skills and better code examples. Hoping to make it almost fully automatic for adding new datasets. The next few I'm looking at are Harmony4D and Aria Pilot Gen2 After we have enough samples, I'll work on bringing in all the different algorithms I've worked on to transform the data 🙂
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We have HOT3D! I've started using Claude to port more datasets into Rerun and exoego-forge. I'd been meaning to bring in the HOT3D dataset from Meta for a while, but with Claude, it's way easier. My goal is to take any egocentric, exocentric, or both datasets and ingest them into a standardized schema. Getting everything into Rerun means we can easily query and transform data via the in-memory OSS server. This lets us generate SQL-like queries such as: "Find me all frames that only contain left hands in the leftmost camera view." Most people think of Rerun as a viewer, but this is the actual superpower. So far we have: 1. HOT3D 2. Hocap 3. UmeTrack 4. Assembly101 5. EgoDex Planning to add more, and with every addition, it gets easier as we build up agent skills and better code examples. Hoping to make it almost fully automatic for adding new datasets. The next few I'm looking at are Harmony4D and Aria Pilot Gen2 After we have enough samples, I'll work on bringing in all the different algorithms I've worked on to transform the data 🙂

Pablo Vela

35,662 views • 2 months ago

More progress! I now have two Dockerized Gradio | Rerun apps. The first one takes as input a "raw" rrd file that consists of the synchronized egocentric and exocentric MP4 files. This runs the pipeline and produces an "annotated" rrd file. This has the camera parameters, 3D joints, and projected 2D joints (with 6DOF mano soon). The second app takes this "annotated" rrd file and allows for manual labeling. This is a crucial step in addressing any major failures in the pipeline. Right now, it is only the ego view that can be modified. But I'll eventually extend to all. This results in a final "gt" rrd file. From here, the plan is to improve quality and start building a data loop. Excited to start really scaling this. I'm basically going all in on keeping my data stored as Rerun rrd files. As always, I want to emphasize how crucial it is to LOOK AT YOUR data! The rrd format makes it incredibly easy to do so. Getting the data out to use is a bit of a hassle right now, but for me, it's well worth the tradeoff.
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More progress! I now have two Dockerized Gradio | Rerun apps. The first one takes as input a "raw" rrd file that consists of the synchronized egocentric and exocentric MP4 files. This runs the pipeline and produces an "annotated" rrd file. This has the camera parameters, 3D joints, and projected 2D joints (with 6DOF mano soon). The second app takes this "annotated" rrd file and allows for manual labeling. This is a crucial step in addressing any major failures in the pipeline. Right now, it is only the ego view that can be modified. But I'll eventually extend to all. This results in a final "gt" rrd file. From here, the plan is to improve quality and start building a data loop. Excited to start really scaling this. I'm basically going all in on keeping my data stored as Rerun rrd files. As always, I want to emphasize how crucial it is to LOOK AT YOUR data! The rrd format makes it incredibly easy to do so. Getting the data out to use is a bit of a hassle right now, but for me, it's well worth the tradeoff.

Pablo Vela

19,527 views • 7 months ago

Working on adding a new dataset to the lineup. Ported ego-dex over to Rerun With rerun now stabilizing RRD format between versions (0.23 -> 0.24), this is the perfect time to start encoding all of the datasets I've been using to RRD 1. I'm starting with ego-dex and then adding others, such as HOCAP/Assembly 101 2. Looking to see if it also makes sense to port to webdatasets RRD 3. I've started including visualizing confidence — green (high), yellow (medium), red (low). More info on Friday
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Working on adding a new dataset to the lineup. Ported ego-dex over to Rerun With rerun now stabilizing RRD format between versions (0.23 -> 0.24), this is the perfect time to start encoding all of the datasets I've been using to RRD 1. I'm starting with ego-dex and then adding others, such as HOCAP/Assembly 101 2. Looking to see if it also makes sense to port to webdatasets RRD 3. I've started including visualizing confidence — green (high), yellow (medium), red (low). More info on Friday

Pablo Vela

34,253 views • 1 year ago

MVP of Multiview Video → Camera parameters + 3D keypoints. Visualized with Rerun The basic pipeline as of right now looks like this: 1. Capture 🔴 – Using 4 iPhones and an Insta360 Go. iPhone videos are captured via Final Cut Pro Multicam for easy sync and the exocentric view; the Insta360 Go is used for the egocentric view. 2. Sync 🕒 – Custom Gradio app using two Rerun viewers and callbacks for easily aligning frame timestamps so the ego and exo views are aligned. 3. Calibrate 🎯 – Use VGGT from Jianyuan and AI at Meta to get intrinsics/extrinsics for sparse cameras. 4. Estimate 3D 🕺 – Use RTMLib whole‑body keypoint estimator on each frame, then triangulate in 3D. What's missing? 1. No temporal coherence: I’m estimating keypoints one frame at a time and one camera at a time. This leads to a lot of jittering. For now, I plan on adding a One Euro Filter to help with jittering. Long term, I'd want to train a multiview keypoint estimator 2. Kinematic fitting is still missing; this is my next goal. The output will be joint angles, as explored in my previous posts. 3. Missing dense point cloud: VGGT seems to fail for me here. I’m looking to explore using MP‑SFM as a method for generating dense multiview depth maps + normals (plus it has a friendlier license compared to VGGT). 4. Eventually, creation of 4D Gaussian splatting using something akin to DN‑splatter—my long‑term goal is a data engine that provides poses/depths/splats/keypoints/etc.
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MVP of Multiview Video → Camera parameters + 3D keypoints. Visualized with Rerun The basic pipeline as of right now looks like this: 1. Capture 🔴 – Using 4 iPhones and an Insta360 Go. iPhone videos are captured via Final Cut Pro Multicam for easy sync and the exocentric view; the Insta360 Go is used for the egocentric view. 2. Sync 🕒 – Custom Gradio app using two Rerun viewers and callbacks for easily aligning frame timestamps so the ego and exo views are aligned. 3. Calibrate 🎯 – Use VGGT from Jianyuan and AI at Meta to get intrinsics/extrinsics for sparse cameras. 4. Estimate 3D 🕺 – Use RTMLib whole‑body keypoint estimator on each frame, then triangulate in 3D. What's missing? 1. No temporal coherence: I’m estimating keypoints one frame at a time and one camera at a time. This leads to a lot of jittering. For now, I plan on adding a One Euro Filter to help with jittering. Long term, I'd want to train a multiview keypoint estimator 2. Kinematic fitting is still missing; this is my next goal. The output will be joint angles, as explored in my previous posts. 3. Missing dense point cloud: VGGT seems to fail for me here. I’m looking to explore using MP‑SFM as a method for generating dense multiview depth maps + normals (plus it has a friendlier license compared to VGGT). 4. Eventually, creation of 4D Gaussian splatting using something akin to DN‑splatter—my long‑term goal is a data engine that provides poses/depths/splats/keypoints/etc.

Pablo Vela

42,785 views • 1 year ago

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I've been on a SLAM/SFM kick. It's one of the more underexplored and lacking areas when it comes to human teleop/data collections, so I've brought over Deep Patch Visual Odometry/SLAM to Rerun and Gradio. With this example, we now have 1. pycuvslam 2. pycolmap/glomap 3. mast3r-slam 4. dpvo/slam all integrated into rerun. The question becomes, which method should be used in what situations? They all make different trade-offs with different camera requirements and throughput/accuracy. What about when a new method comes out? Now that I have several different methods, I plan to use VSLAM-LAB for evaluation. It uses prefix.dev to isolate all the dependencies of each of these methods and easily compare them against each other. In particular, I'll be converting the data preprocessing, algorithm outputs, and evaluation into rerun recordings (rrd files). This will allow both programmatic querying of anything stored in the files (which method had the highest ATE-to-FPS ratio? Which dataset/sequence caused the most difficulty? etc. etc.), all with easy visual inspection using the rerun server to link them all together. Another really important side effect of this is how it impacts agents. As Karpathy said ``` LLMs are exceptionally good at looping until they meet specific goals, and this is where most of the "feel the AGI" magic is to be found. Don't tell it what to do, give it success criteria, and watch it go. ``` by having accuracy and throughput metrics deeply tied with human inspectable artifacts. One can really accelerate agentic development with an actual understanding of how the method/data performs. I think this is another killer use case that I'll be really leaning into to make ingestion of new datasets/methods trivial with an agent. I'm making it my mission for folks to understand that rerun as a visualization tool only scratches the surface of what its true benefit is. Deep integration between data and visuals, with powerful query capabilities. I'll be focusing on the SLAM use case first and then bringing this into the full egocentric/exocentric data collection domain!

Pablo Vela

40,651 views • 1 month ago

Happy 4th 🇺🇸!! I have a preview for my next release (assembly101 is massive, so I need to push until next week). But in the meantime, check out the link below to try out the 🚧 work in progress using Rerun and Gradio Links below 👇
0:25

Happy 4th 🇺🇸!! I have a preview for my next release (assembly101 is massive, so I need to push until next week). But in the meantime, check out the link below to try out the 🚧 work in progress using Rerun and Gradio Links below 👇

Pablo Vela

34,186 views • 11 months ago

Debug ROS 2 transforms in Rerun 💡 ROS 2’s tf2 gives robotics teams a shared language for tracking coordinate frames over time — transforming data between things like camera_link and base_link. Rerun supports the same idea with named transforms, letting you decouple spatial relationships from your entity hierarchy. This example skips the basics of transforms in Rerun (you can find that in our docs) and instead shows how to debug broken/missing transforms in practice, using the JKK dataset. Check out the how-to 👇
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Debug ROS 2 transforms in Rerun 💡 ROS 2’s tf2 gives robotics teams a shared language for tracking coordinate frames over time — transforming data between things like camera_link and base_link. Rerun supports the same idea with named transforms, letting you decouple spatial relationships from your entity hierarchy. This example skips the basics of transforms in Rerun (you can find that in our docs) and instead shows how to debug broken/missing transforms in practice, using the JKK dataset. Check out the how-to 👇

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Rerun 0.22 is out! 🔎🟡🔜🔵 The release brings long-requested entity filtering for finding data faster in the Viewer, significantly simplified APIs for partial & columnar updates, and many other enhancements. At Rerun we’re building the multimodal data stack for physical AI. Our open-source visualization tools help you model and visualize your data over time.
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Rerun 0.22 is out! 🔎🟡🔜🔵 The release brings long-requested entity filtering for finding data faster in the Viewer, significantly simplified APIs for partial & columnar updates, and many other enhancements. At Rerun we’re building the multimodal data stack for physical AI. Our open-source visualization tools help you model and visualize your data over time.

Rerun

14,994 views • 1 year ago

Colmap 4.0 was very recently released, so it inspired me to do some work to better understand it and its new capabilities with Rerun. I want to really understand how Colmap, and in particular, pycolmap, works outside of just calling it via the CLI. So my goal is to use the low-level pycolmap API to log every part of the pipeline. The explicit goal is to have an alternative to the SQLite database that I can utilize. Instead of SQLite, I want to try logging everything directly to rerun and use RRD. This means I can have deep inspectability and still save the features/matches/2D view geometry, but be able to view it directly in rerun. I think this is one of the superpowers that rerun provides; data and visualizations are deeply integrated. As I'm often working with sequential data (videos), I'm going to specifically focus on four things: 1. Monocular Video Simple: Calls high-level APIs such as pycolmap.extract_features, pycolmap.match_sequential, pycolmap.incremental_mapping. These are basically identical to the CLI options and provide a good baseline. 2. Monocular Video Streamed: Take the above high-level APIs and break them down to their iterator version, logging each component in a streamed manner. This way, I can stream the intermediate features to rerun while the extraction/matching/mapping is happening. 3. Rig with unknown calibration: <- WHAT THE VIDEO SHOWS This is probably the most interesting version and the first one I've been working on. It allows one to set a rig between known sensors, such as in VR/AR devices, leading to much better reconstructions with multiple cameras. This is the case where we don't know the calibration a priori, so we have to run a reconstruction twice: once as a normal Colmap reconstruction with no rig constraints, use this to generate the constraints, and then do it again with the newly found rig. 4. Rig with known calibration: This is the RoboCap example, where we have a pre-calibrated set of sensors, so we don't need to run the two reconstructions and also gain better matching between cameras, both spatially and temporally. Again, this leads to a much better reconstruction! Along with all this, GLOMAP has become a first-class global mapper, making it super easy to use directly within pycolmap! I'm excited to do more with this and compare it to things like pycuvslam, vipe, and other alternatives.
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Colmap 4.0 was very recently released, so it inspired me to do some work to better understand it and its new capabilities with Rerun. I want to really understand how Colmap, and in particular, pycolmap, works outside of just calling it via the CLI. So my goal is to use the low-level pycolmap API to log every part of the pipeline. The explicit goal is to have an alternative to the SQLite database that I can utilize. Instead of SQLite, I want to try logging everything directly to rerun and use RRD. This means I can have deep inspectability and still save the features/matches/2D view geometry, but be able to view it directly in rerun. I think this is one of the superpowers that rerun provides; data and visualizations are deeply integrated. As I'm often working with sequential data (videos), I'm going to specifically focus on four things: 1. Monocular Video Simple: Calls high-level APIs such as pycolmap.extract_features, pycolmap.match_sequential, pycolmap.incremental_mapping. These are basically identical to the CLI options and provide a good baseline. 2. Monocular Video Streamed: Take the above high-level APIs and break them down to their iterator version, logging each component in a streamed manner. This way, I can stream the intermediate features to rerun while the extraction/matching/mapping is happening. 3. Rig with unknown calibration: <- WHAT THE VIDEO SHOWS This is probably the most interesting version and the first one I've been working on. It allows one to set a rig between known sensors, such as in VR/AR devices, leading to much better reconstructions with multiple cameras. This is the case where we don't know the calibration a priori, so we have to run a reconstruction twice: once as a normal Colmap reconstruction with no rig constraints, use this to generate the constraints, and then do it again with the newly found rig. 4. Rig with known calibration: This is the RoboCap example, where we have a pre-calibrated set of sensors, so we don't need to run the two reconstructions and also gain better matching between cameras, both spatially and temporally. Again, this leads to a much better reconstruction! Along with all this, GLOMAP has become a first-class global mapper, making it super easy to use directly within pycolmap! I'm excited to do more with this and compare it to things like pycuvslam, vipe, and other alternatives.

Pablo Vela

30,070 views • 2 months ago

🚀 Rerun 0.23 is out! The highlights: 🔸Backwards-compatible .rrd – open today’s recordings with tomorrow’s viewer. 🔸 Viewer callbacks – wire the viewer into your notebooks or web apps and react to selections and time-scrubs in real-time, e.g., to build annotation tools. 👇
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🚀 Rerun 0.23 is out! The highlights: 🔸Backwards-compatible .rrd – open today’s recordings with tomorrow’s viewer. 🔸 Viewer callbacks – wire the viewer into your notebooks or web apps and react to selections and time-scrubs in real-time, e.g., to build annotation tools. 👇

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55,438 views • 1 year ago

A Tradition Unlike Any Other . . . It's Friday! And a rerun for the first time in years!
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A Tradition Unlike Any Other . . . It's Friday! And a rerun for the first time in years!

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267,818 views • 6 months ago

We have SLAM on the Robocap! 🎉 Visualized with Rerun Using NVIDIA AI Developer cuVSLAM for GPU-accelerated multicamera tracking. I basically wrote zero code myself and fully used Claude Code for this. It worked because I had so many existing examples to point to that it just wrote everything the way I would have. A few technical wins: 1. Used rattler-build from prefix.dev to package the compiled cuVSLAM CUDA binaries, which made it SUPER easy to use across repos. This also means it works on the DGX Spark (ARM64) out of the box. 2. Zero-setup experience: git clone && pixi run track-robocap auto-downloads a 100MB dataset from HuggingFace and tracks frames. 3. Real-time 3D visualization with trajectories, landmarks, pose graphs, and video playback in Rerun. Still visual-only (not visual-inertial yet), and loop closure needs some debugging. Next steps are getting this into a Gradio interface, then into daggr, and extending it to work with other datasets from exoego-forge. The last piece I'm excited about: Rerun's RRD files now support layers for incremental data. Planning to build pipelines that go from raw sensor data → slam -> human pose → depth estimation → etc. Repo here:
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We have SLAM on the Robocap! 🎉 Visualized with Rerun Using NVIDIA AI Developer cuVSLAM for GPU-accelerated multicamera tracking. I basically wrote zero code myself and fully used Claude Code for this. It worked because I had so many existing examples to point to that it just wrote everything the way I would have. A few technical wins: 1. Used rattler-build from prefix.dev to package the compiled cuVSLAM CUDA binaries, which made it SUPER easy to use across repos. This also means it works on the DGX Spark (ARM64) out of the box. 2. Zero-setup experience: git clone && pixi run track-robocap auto-downloads a 100MB dataset from HuggingFace and tracks frames. 3. Real-time 3D visualization with trajectories, landmarks, pose graphs, and video playback in Rerun. Still visual-only (not visual-inertial yet), and loop closure needs some debugging. Next steps are getting this into a Gradio interface, then into daggr, and extending it to work with other datasets from exoego-forge. The last piece I'm excited about: Rerun's RRD files now support layers for incremental data. Planning to build pipelines that go from raw sensor data → slam -> human pose → depth estimation → etc. Repo here:

Pablo Vela

50,244 views • 3 months ago

A 50 meter Gopuram that touches the sky, a pinhole camera that flips it inside the sanctum, and 1300 years of Vijayanagara heritage - Virupaksha Temple is science + soul in ancient stone. Shivprabhat🙏🏾
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A 50 meter Gopuram that touches the sky, a pinhole camera that flips it inside the sanctum, and 1300 years of Vijayanagara heritage - Virupaksha Temple is science + soul in ancient stone. Shivprabhat🙏🏾

Āyudhika

15,312 views • 5 months ago

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Love and Deepspace

188,482 views • 2 months ago

His shop has a hole in the roof and it created a pinhole camera effect where you can see the clouds from outside which casts onto the concrete floor
0:41

Sensitive content

His shop has a hole in the roof and it created a pinhole camera effect where you can see the clouds from outside which casts onto the concrete floor

Dudes Posting Their W’s

693,595 views • 3 months ago

Migrating to #Zilliqa 2.0 is now super easy. No hassle, no confusion. Just a few clicks and you are done! We’ve built a simple step-by-step guide inside Torch to help you move your staked $ZIL almost instantly. Everything is unified. Legacy and EVM support, one seed phrase, Ledger integration. All in one wallet. Torch Wallet is ready. Download it today and be fully set for Zilliqa 2.0 as soon as the chain is live 📲
0:30

Migrating to #Zilliqa 2.0 is now super easy. No hassle, no confusion. Just a few clicks and you are done! We’ve built a simple step-by-step guide inside Torch to help you move your staked $ZIL almost instantly. Everything is unified. Legacy and EVM support, one seed phrase, Ledger integration. All in one wallet. Torch Wallet is ready. Download it today and be fully set for Zilliqa 2.0 as soon as the chain is live 📲

Torch Wallet 📲

15,047 views • 11 months ago

“Say a robot drives into a wall. Why did it do that?” Nikolaus West explains why robotics teams need clearer visibility into sensor data, and how Rerun is helping push physical AI from impressive demos to real reliability. Link to the full episode with Lukas Biewald in comments.
1:00

“Say a robot drives into a wall. Why did it do that?” Nikolaus West explains why robotics teams need clearer visibility into sensor data, and how Rerun is helping push physical AI from impressive demos to real reliability. Link to the full episode with Lukas Biewald in comments.

Weights & Biases

196,608 views • 5 months ago

I've built a setup in Geometry Nodes to get data from bones, so now I can do things like make Normals from them, and I'm experimenting with much more. The Normal here is basically a cylinder around each bone. So it doesn't work that great around the shoulders, but its great on the arms and legs. Its the same idea as when you do radial/sphere Normals for a face or hair, but for the whole body.
0:52

I've built a setup in Geometry Nodes to get data from bones, so now I can do things like make Normals from them, and I'm experimenting with much more. The Normal here is basically a cylinder around each bone. So it doesn't work that great around the shoulders, but its great on the arms and legs. Its the same idea as when you do radial/sphere Normals for a face or hair, but for the whole body.

aVersionOfReality

13,309 views • 7 months ago

Here’s a sneak peek using Rerun and Gradio for data annotation. It uses Video Depth Anything and Segment Anything 2 under the hood to generate segmentation masks and depth maps/point clouds. More to share next week.
0:30

Here’s a sneak peek using Rerun and Gradio for data annotation. It uses Video Depth Anything and Segment Anything 2 under the hood to generate segmentation masks and depth maps/point clouds. More to share next week.

Pablo Vela

36,719 views • 1 year ago