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Google just revealed an ABSOLUTE depth estimation model 🤯 As opposed to recent depth models (Marigold, PatchFusion) which aim for maximum details, DMD aims to estimate the ABSOLUTE depth (in meters) within the image More details below ⬇️⬇️

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Alex Carlier

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199,532 Aufrufe • vor 2 Jahren •via X (Twitter)

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PatchFusion was just released. Compared to ZoeDepth, it predicts depth maps with much finer details, just look at the comparison below! 🤯 Its main contribution is a new Global-to-Local module and Consistency-Aware Training. More examples below (with code) ⬇️⬇️
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PatchFusion was just released. Compared to ZoeDepth, it predicts depth maps with much finer details, just look at the comparison below! 🤯 Its main contribution is a new Global-to-Local module and Consistency-Aware Training. More examples below (with code) ⬇️⬇️

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Some tweaks to the details to enhance depth. #Live2D #Live2DWIP
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Some tweaks to the details to enhance depth. #Live2D #Live2DWIP

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Discover the right 3D Geometric Foundation Model for your task—whether it’s stereo matching, multi-view depth estimation, video depth, pose estimation, semantic understanding, or novel view synthesis. Explore more insights in our #E3DBench #FoundationModel #3D #GaussianSplatting. Project Webpage:

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An image + a detailed normal map generated by GeoWizard --> a relightable image. Geowizard is a generative geometry estimation model that could jointly produce high-quality depth and normal with intricate details. Try our huggingface demo here: #huggingface #gradio #geowizard
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Video diffusion models are just overqualified depth estimators! Deterministic single-pass depth estimation based on WanV2.1. - SOTA 5.5 AbsRel on ScanNet - data-efficient than baselines; - no temporal flicker + infinite-length estimation w/ zero scale drift.
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A close-up only works when depth feels real. In this shot, we pushed blur and shading to shape the depth of field (DoF), not just for realism, but to deepen the emotion within the frame. Produced by Otsu Labs & Studio Munbou
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Calculating pothole volume from a 2D image is challenging but possible: > Assign each pothole an ID to avoid duplication. > Use basic trigonometry to convert pixels to real-world meters. > Apply a novel algorithm to estimate depth using road engineering data.
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Scientists at University of Western Australia set new record for deepest fish ever filmed at a depth of 8,336 meters. During an expedition that took place in August 2022, the research vessel DSSV Pressure Drop embarked on a two-month journey to investigate trenches in the Pacific Ocean near Japan. The primary objective of the expedition was to examine the populations of deep-sea fish. While conducting filming activities, the research team unexpectedly encountered a previously unknown species of snailfish at an astounding depth of 8,336 meters, equivalent to over 27,000 feet, specifically in the Izu-Ogasawara Trench. At a depth of 8,022 meters, scientists successfully obtained two fish from traps. This achievement is noteworthy because it includes the capture of a snailfish species known as Pseudoliparis belyaevi. Notably, this is the first time a fish has been collected from depths exceeding 8,000 meters, as these snailfish were previously observed solely at a shallower depth of 7,703 meters.

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gm So if you ask Nano Banana to make an image into a Depth Map, you get a grayscale image where white=close & black=far Then ask Google AI Studio for an app that turns depth maps into point clouds & lets you control them with hand gestures from webcam blam you’re Tony Stark
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gm So if you ask Nano Banana to make an image into a Depth Map, you get a grayscale image where white=close & black=far Then ask Google AI Studio for an app that turns depth maps into point clouds & lets you control them with hand gestures from webcam blam you’re Tony Stark

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