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Doesn't look like the super low-res depth maps from the iPhone Pro's LiDAR sensor would be too useful as supervision (or sanity check) for recent monocular depth estimators.

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Chris Offner

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66,068 просмотров • 1 год назад •via X (Twitter)

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Let me share how the maps look like. Normal was estimated from the source image with StableNormal ( and Refined Depth came from promptDA ( + Lidar Depth. If you pause the video, you will see the differences in depths.
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Let me share how the maps look like. Normal was estimated from the source image with StableNormal ( and Refined Depth came from promptDA ( + Lidar Depth. If you pause the video, you will see the differences in depths.

Chris make some 3D scans

44,280 просмотров • 1 год назад

AI-Enhanced LiDAR. Left vs right. A real LiDAR device costs thousands of dollars. What's in your iPhone Pro is a "baby LiDAR". Limited depth resolution, noisy output, not really built for high-precision 3D. You can't change the hardware. So we built an ML layer on top. Denoising, geometry completion, detail recovery. Processed server-side. Same sensor. Very different result.
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AI-Enhanced LiDAR. Left vs right. A real LiDAR device costs thousands of dollars. What's in your iPhone Pro is a "baby LiDAR". Limited depth resolution, noisy output, not really built for high-precision 3D. You can't change the hardware. So we built an ML layer on top. Denoising, geometry completion, detail recovery. Processed server-side. Same sensor. Very different result.

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Some updates on the multiview vistadream pipeline with Rerun! Rerun came in extremely useful here, as being able to visualize depths at each stage of the pipeline allowed me to debug some nasty bugs. Since the last time, I was only working with a single image input. I've added in VGGT as my multiview pose + depth estimator. It works REALLY well for getting camera poses, but the depths are not that great. To try and fix that, I estimated depth maps from MoGeV2 for each of the views, and scale+shift aligned them so that they would match up to the confident sections of VGGT's depth predictions. You can see in the video just how much sharper the visualized 2d depth maps are! The biggest issue continues to be the multiview consistency 🫠 That's up next, along with actually training the Gaussian splat. Lots of work went into actually understanding inputs+outputs for VGGT. I had some funky bugs where the confidence values would all collapse to true I'm also really excited for this pipeline to use Difix3D+ Nvidia instead of Flux Inpainting, it seems like a better suited for a multiview pipeline.
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Some updates on the multiview vistadream pipeline with Rerun! Rerun came in extremely useful here, as being able to visualize depths at each stage of the pipeline allowed me to debug some nasty bugs. Since the last time, I was only working with a single image input. I've added in VGGT as my multiview pose + depth estimator. It works REALLY well for getting camera poses, but the depths are not that great. To try and fix that, I estimated depth maps from MoGeV2 for each of the views, and scale+shift aligned them so that they would match up to the confident sections of VGGT's depth predictions. You can see in the video just how much sharper the visualized 2d depth maps are! The biggest issue continues to be the multiview consistency 🫠 That's up next, along with actually training the Gaussian splat. Lots of work went into actually understanding inputs+outputs for VGGT. I had some funky bugs where the confidence values would all collapse to true I'm also really excited for this pipeline to use Difix3D+ Nvidia instead of Flux Inpainting, it seems like a better suited for a multiview pipeline.

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I was today years old when I found out Apple Maps works even offline in flight mode It's super fun and easy to look down from the plane and know what city or country you're looking at The landing was fun too I'm like a child when I'm on a plane, sorry 🙃
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I was today years old when I found out Apple Maps works even offline in flight mode It's super fun and easy to look down from the plane and know what city or country you're looking at The landing was fun too I'm like a child when I'm on a plane, sorry 🙃

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you don't need threejs for this effect just apply `perspective` to the container and animate each image with `translate3d(...)` over time, where depth is something like `z = z0 + speed * dt` and the rest is handled by the browser easy to map opacity or scroll to speed too
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you don't need threejs for this effect just apply `perspective` to the container and animate each image with `translate3d(...)` over time, where depth is something like `z = z0 + speed * dt` and the rest is handled by the browser easy to map opacity or scroll to speed too

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Dear artist friends working w/ AI, I can now confirm that AI art is art :) Our piece at MoMA got reviews from 3 biased art critiques using same descriptions with super shallow and like catchy titles. At the meantime 6 true researcher critiques looked for the depth and meaning. :)
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Dear artist friends working w/ AI, I can now confirm that AI art is art :) Our piece at MoMA got reviews from 3 biased art critiques using same descriptions with super shallow and like catchy titles. At the meantime 6 true researcher critiques looked for the depth and meaning. :)

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1,138,751 просмотров • 3 лет назад

China just dropped an open source model that turns any phone into a 3D scanner you point it at a room and walk through it the whole space builds itself into a 3D point cloud in real time as the camera moves, no LiDAR and no depth sensor anywhere it held a full 13 minute walkthrough at 25,000 frames without the map drifting or collapsing > ~20 fps on a single GPU > works indoors and outdoors from plain footage > the scan normally needs a $50,000 rig and a technician > Apache 2.0 and runs fully offline on your machine repo in the reply
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China just dropped an open source model that turns any phone into a 3D scanner you point it at a room and walk through it the whole space builds itself into a 3D point cloud in real time as the camera moves, no LiDAR and no depth sensor anywhere it held a full 13 minute walkthrough at 25,000 frames without the map drifting or collapsing > ~20 fps on a single GPU > works indoors and outdoors from plain footage > the scan normally needs a $50,000 rig and a technician > Apache 2.0 and runs fully offline on your machine repo in the reply

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A preview of what's next, visualized with Rerun and PlayCanvas supersplat ✨ (Also, feel free to send me a DM 📩; I’ll be in San Francisco from July 21–29, and I'm looking to meet like-minded folks!) I'm convinced that Gaussian Splats will be an integral part of any data engine as an underlying representation. So I've started putting together a repo that: 1. Given a single image, perform image outpainting 🖼️🖌️ 2. Estimate a monocular depth map on the outpainted image 📏 3. Train a Gaussian Splat initialized from the monocular depth 🎓✨ 4. Warp to new views, perform inpainting on the missing masks -> Train new splat 🔄🎨 This is going to be integrated into exo-egoforge, but I wanted to start with the simple single-image version before moving to a multi-video implementation There's some weirdness in the final rerun visualization, but the trained splat looks great 🎉! This is all based on the very cool VistaDream paper ( .github.io/) More on this next week!
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A preview of what's next, visualized with Rerun and PlayCanvas supersplat ✨ (Also, feel free to send me a DM 📩; I’ll be in San Francisco from July 21–29, and I'm looking to meet like-minded folks!) I'm convinced that Gaussian Splats will be an integral part of any data engine as an underlying representation. So I've started putting together a repo that: 1. Given a single image, perform image outpainting 🖼️🖌️ 2. Estimate a monocular depth map on the outpainted image 📏 3. Train a Gaussian Splat initialized from the monocular depth 🎓✨ 4. Warp to new views, perform inpainting on the missing masks -> Train new splat 🔄🎨 This is going to be integrated into exo-egoforge, but I wanted to start with the simple single-image version before moving to a multi-video implementation There's some weirdness in the final rerun visualization, but the trained splat looks great 🎉! This is all based on the very cool VistaDream paper ( .github.io/) More on this next week!

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This is the only way you’ll feel when you buy from me cause I too dey deliver. check out business page @Royeshoes for shoes you’ll like or send a dm with a request as what’s posted isn’t all we have.

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Just 29 clips of a normal day in tier 2 tournaments. All of them from the same guy in 2 maps ONLY. We start with an appetizer for you to check how bot he is to be that good. - Doesn't even know how to jump out of heaven, doesn't molly antirush B vs forcebuy and hides barrels.

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Robots can now reconstruct 3D scenes in real time from a single RGB camera. [📍 Projects page + paper] No depth sensor. No retraining. 30 FPS. Researchers at the Imperial College London introduced KV-Tracker, a training-free method that makes heavy models like π³ and Depth Anything 3 fast enough for real-time tracking. The idea is simple. These models use global self-attention, which is powerful but computationally expensive. KV-Tracker caches the key and value pairs from selected keyframes and reuses them for new frames. That cache becomes an implicit scene representation. Result: • Up to 30 FPS • 10 to 15x speedup • Accurate 6-DoF tracking on benchmarks like TUM RGB-D and 7-Scenes • Works with monocular RGB only It also supports object-level tracking with masks and allows saving the KV-cache for later reuse. For robotics, this reduces hardware constraints and moves real-time 3D perception closer to practical deployment. Credit to Marwan Taher (Marwan Taher) at Imperial’s Dyson Robotics Lab and many others who contributed to this! 📍 Save projects page + paper for later: Video: ——- if it matters in AI or Robotics you'll read it here first:

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Look at the Genius Idea He Had for These Old Barrels 🔨🌿 He turned barrels nobody wanted into something creative, useful, and full of charm. It is amazing what a little vision can do with forgotten materials. Would you try something like this too? 🛢️

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