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Meta releases VGGSfM Visual Geometry Grounded Deep Structure From Motion Structure-from-motion (SfM) is a long-standing problem in the computer vision community, which aims to reconstruct the camera poses and 3D structure of a scene from a set of unconstrained 2D images. Classical frameworks solve this problem in an incremental... manner by detecting and matching keypoints, registering images, triangulating 3D points, and conducting bundle adjustment. Recent research efforts have predominantly revolved around harnessing the power of deep learning techniques to enhance specific elements (e.g., keypoint matching), but are still based on the original, non-differentiable pipeline. Instead, we propose a new deep SfM pipeline VGGSfM, where each component is fully differentiable and thus can be trained in an end-to-end manner. To this end, we introduce new mechanisms and simplifications. First, we build on recent advances in deep 2D point tracking to extract reliable pixel-accurate tracks, which eliminates the need for chaining pairwise matches. Furthermore, we recover all cameras simultaneously based on the image and track features instead of gradually registering cameras. Finally, we optimise the cameras and triangulate 3D points via a differentiable bundle adjustment layer. We attain state-of-the-art performance on three popular datasets, CO3D, IMC Phototourism, and ETH3D.show more

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Donut and Devotions. Every other Thursday I meet with the guys during lunch. I open up the shed/meeting room and try to create a quiet space where they can relax, take a deep breath and reflect on what is good. We share where we are feeling unrest in our lives. We read from the wisdom found Proverbs and then we spend time in prayer. Afterwards the kids have access to the field to throw and hangout together. It’s the highlight of my week. Connect and build community. That’s the business we are in.

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I met with representatives of the American think tank Hudson Institute and informed them about the consequences of Russian strikes in recent days. In light of these attacks, it is particularly important to bolster Ukraine’s air defense, and we are grateful to the United States of America and to all caring Americans for their support of Ukraine, which we have felt throughout all the years of this full-scale Russian aggression. During the meeting, we discussed cooperation with the U.S. Congress, diplomatic work to achieve a dignified peace, and the importance of active U.S. participation in the negotiation process. We are counting on an end to the pause in the negotiations and the reinvigoration of diplomacy. This is important for saving the lives of our people and restoring security in Europe.

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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.

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Industrial CT scans capture both geometry and relative density. We start by taking hundreds of X-ray images from different angles, then reconstruct them into a 3D model that can be sectioned and analyzed. A dark area in a 2D X-ray image could be either thicker material or denser material; by rotating the object we’re scanning, we can separate geometry from density. In this Lumafield CT scan, density is visualized as a blue-red color map.

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hi all, excited to join! i'm building an expressive mini "shoggoth" robot which will eventually be hooked up to gpt4o realtime voice. i'm currently working on the low-level policies, which are trained in a mujoco simulation with RL. to delay working on raw-pixels for now, i trained a pose-estimation model using deeplabcut and triangulate the position in 3d space using the stereo cameras. eventually, i'll use gpt4o's tool calling capabilities to activate several of these policies (closed and open loop) based on the dialog flow! captions: manual actuation of the tentacle / 3d pose estimation / target design

hi all, excited to join! i'm building an expressive mini "shoggoth" robot which will eventually be hooked up to gpt4o realtime voice. i'm currently working on the low-level policies, which are trained in a mujoco simulation with RL. to delay working on raw-pixels for now, i trained a pose-estimation model using deeplabcut and triangulate the position in 3d space using the stereo cameras. eventually, i'll use gpt4o's tool calling capabilities to activate several of these policies (closed and open loop) based on the dialog flow! captions: manual actuation of the tentacle / 3d pose estimation / target design

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General Mazlum of SDF: “In Syria, we are living through historic moments as we witness the fall of the authoritarian regime in #Damascus. This change presents an opportunity to build a new #Syria based on democracy and justice that guarantees the rights of all Syrians.” #Assadgone

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The U.S. Special Presidential Envoy for Ukraine, General Keith Kellogg Keith Kellogg, is in Kyiv today. I am grateful for a constructive meeting. We discussed various vectors of cooperation – how to achieve real peace and guarantee Ukraine’s security. These include projects within the PURL initiative for financing production and procurement of Patriot systems, strong bilateral agreements on co-production of drones and weapons that we have proposed to America. We count on a positive response from the United States. We had a substantive discussion on stepping up pressure on the Russians and what we can do together with partners in tariff and sanctions policy to enable a meeting at the leaders’ level at the earliest and bring this war to an end. A trilateral leaders’ format is undoubtedly the most effective. We also discussed the return of abducted Ukrainian children, international cooperation on this track, and the conditions in which our children are being held. I expressed condolences to the American people over the horrific murder of Charlie Kirk and thanked President Trump for his condolences and response to the brutal murder of Ukrainian citizen Iryna Zarutska in North Carolina. It is important that justice prevail every time violence seeks to take hold. We are also preparing for the 80th session of the UN General Assembly in New York. We discussed planned events, coordination between Ukraine and the U.S., and work within the Coalition of the Willing. We are working on potential meetings and various formats.

The U.S. Special Presidential Envoy for Ukraine, General Keith Kellogg Keith Kellogg, is in Kyiv today. I am grateful for a constructive meeting. We discussed various vectors of cooperation – how to achieve real peace and guarantee Ukraine’s security. These include projects within the PURL initiative for financing production and procurement of Patriot systems, strong bilateral agreements on co-production of drones and weapons that we have proposed to America. We count on a positive response from the United States. We had a substantive discussion on stepping up pressure on the Russians and what we can do together with partners in tariff and sanctions policy to enable a meeting at the leaders’ level at the earliest and bring this war to an end. A trilateral leaders’ format is undoubtedly the most effective. We also discussed the return of abducted Ukrainian children, international cooperation on this track, and the conditions in which our children are being held. I expressed condolences to the American people over the horrific murder of Charlie Kirk and thanked President Trump for his condolences and response to the brutal murder of Ukrainian citizen Iryna Zarutska in North Carolina. It is important that justice prevail every time violence seeks to take hold. We are also preparing for the 80th session of the UN General Assembly in New York. We discussed planned events, coordination between Ukraine and the U.S., and work within the Coalition of the Willing. We are working on potential meetings and various formats.

Volodymyr Zelenskyy / Володимир Зеленський

226,833 Aufrufe • vor 9 Monaten

I've been working a lot with SAM3 and the Momentum Human Rig (MHR). I finally integrated it into the data I'm working with Rerun. The progression I've taken looks as follows SAM3 + SAM3D-body on 1. a single image 2. a set of multiple images 3. a single video 4. A multiview video capture I took inspiration from the SAM3D-body paper and built a multiview fitting optimization pipeline. This pipeline involves using the 2D keypoints from the single-view pipeline, triangulating them, and employing an L1 loss between the 2D/3D keypoints. The temporal stability isn't great, so that's the next portion I'm going to focus on. One really frustrating thing about SAM3D-body is the lack of per-joint confidence values. It makes it harder to deal with occlusions. I'm probably going to need to use a separate model, or maybe add a confidence head.

I've been working a lot with SAM3 and the Momentum Human Rig (MHR). I finally integrated it into the data I'm working with Rerun. The progression I've taken looks as follows SAM3 + SAM3D-body on 1. a single image 2. a set of multiple images 3. a single video 4. A multiview video capture I took inspiration from the SAM3D-body paper and built a multiview fitting optimization pipeline. This pipeline involves using the 2D keypoints from the single-view pipeline, triangulating them, and employing an L1 loss between the 2D/3D keypoints. The temporal stability isn't great, so that's the next portion I'm going to focus on. One really frustrating thing about SAM3D-body is the lack of per-joint confidence values. It makes it harder to deal with occlusions. I'm probably going to need to use a separate model, or maybe add a confidence head.

Pablo Vela

42,267 Aufrufe • vor 5 Monaten

❗️“We are the first in the world to create drone-assault regiments, one of which served as the strike force in the recent de-occupation of Kupiansk,” — Ukraine’s new Minister of Defense Fedorov “We are moving toward full autonomy on the battlefield — from machine vision and data analytics to drone swarms. Our ambitious goal is to make Ukraine the first country in the world capable of predicting and neutralizing enemy attacks using AI.”

❗️“We are the first in the world to create drone-assault regiments, one of which served as the strike force in the recent de-occupation of Kupiansk,” — Ukraine’s new Minister of Defense Fedorov “We are moving toward full autonomy on the battlefield — from machine vision and data analytics to drone swarms. Our ambitious goal is to make Ukraine the first country in the world capable of predicting and neutralizing enemy attacks using AI.”

🪖MilitaryNewsUA🇺🇦

48,573 Aufrufe • vor 5 Monaten

DimensionX: Create Any 3D and 4D Scenes from a Single Image with Controllable Video Diffusion TL;DR: Create 3/4DGS from Video Diffusion Note: Some first inference code released (not all yet). Contributions (cited): • We present DimensionX, a novel framework for generating photorealistic 3D and 4D scenes from only a single image using controllable video diffusion. • We propose ST-Director, which decouples the spatial and temporal priors in video diffusion models by learning (spatial and temporal) dimension-aware modules with our curated datasets. We further enhance the hybriddimension control with a training-free composition approach according to the essence of video diffusion denoising process. • To bridge the gap between video diffusion and real-world scenes, we design a trajectory-aware mechanism for 3D generation and an identity-preserving denoising approach for 4D generation, enabling more realistic and controllable scene synthesis. • Extensive experiments manifest that our DimensionX delivers superior performance in video, 3D, and 4D generation compared with baseline methods.

DimensionX: Create Any 3D and 4D Scenes from a Single Image with Controllable Video Diffusion TL;DR: Create 3/4DGS from Video Diffusion Note: Some first inference code released (not all yet). Contributions (cited): • We present DimensionX, a novel framework for generating photorealistic 3D and 4D scenes from only a single image using controllable video diffusion. • We propose ST-Director, which decouples the spatial and temporal priors in video diffusion models by learning (spatial and temporal) dimension-aware modules with our curated datasets. We further enhance the hybriddimension control with a training-free composition approach according to the essence of video diffusion denoising process. • To bridge the gap between video diffusion and real-world scenes, we design a trajectory-aware mechanism for 3D generation and an identity-preserving denoising approach for 4D generation, enabling more realistic and controllable scene synthesis. • Extensive experiments manifest that our DimensionX delivers superior performance in video, 3D, and 4D generation compared with baseline methods.

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