3d reconstruction

A translator at the negotiating table doesn't listen to every word; they grasp the meaning of whole phrases. Electron microscopy poses a similar challenge: you need to figure out the crystal's thickness and its tilt in the beam, and all you have is a diffraction pattern, a mosaic of thousands of bright and dark spots.

A sheet of paper left on a wet table warps and lifts its edges. No one controls that motion, but it follows the same laws as plant growth: some areas of the sheet shrink more than others, and the flat form becomes three-dimensional. Engineers have long dreamed of taming this process so that a flat blank folds itself into a desired sculpture. In the new work, that trick succeeded with accuracy down to one percent.

A deck of cards in which each card is slightly rotated cannot lie flat: the backs and faces will never align. But if the cards are flexible, they will start to adjust, shifting by fractions of a millimeter to find the perfect match. Something similar happens inside crystals when two thick layers of MoS2 or MoS2 with WS2 are stacked with a twist of 1–2 degrees.

In a dark room, a sunbeam passes through a pinprick in cardboard, and an upside-down image of the street appears on the wall. A pinhole camera needs no lenses: the tiny hole itself selects the rays coming from each point of the scene. Scientists at the ESRF applied this principle not to light but to X-rays, and made the camera see not shape but chemical composition.

A doctor looks at a screen and sees two images of the same part of the body. One shows where blood is rich in oxygen, the other shows how fast it rushes through the finest vessels. Previously, these images were made by different devices, and combining them was difficult. Now engineers have built a device that takes both images simultaneously.

Under an electron microscope, a protein looks like a series of blurry shadows — hundreds of thousands of images of the same molecule, but from different angles and with heavy noise. Previously, from these shadows, one first assembled a volumetric 'haze'—a map of how electric charge is distributed inside the protein.

Quantum systems made of many particles are the basis of future computers and secure networks. But the more particles there are, the harder it is to understand how they are arranged: the number of possible states grows like an avalanche. The usual approach, measuring everything, takes an incredibly long time.

A stenographer is recording a meeting, but halfway through the conversation she runs out of paper. She misses a few lines, then picks up where she left off. Later, she needs to reconstruct the full transcript without rewriting the entire conversation from scratch. That's exactly how the new method URA-SIM works for structured illumination microscopy (SIM).

Two scans of the same brain taken at different times are like photographs of the same house before and after renovation. To see what changed, you overlay them and look at the differences. But an MRI scanner is not a perfect camera: it adds its own distortions, as if the photo were taken through slightly warped glass.

An X-ray machine takes a scan, and the doctor sees only numbers — bone density, fat percentage. But the image holds far more. A team of researchers created a model called LeDXA that reads these scans like a map of hidden bodily signals. The model was trained on 11,540 scans from the Human Phenotype Project with no labels at all.

In a noisy hall, an interpreter tries to catch every word, but rustles and echoes muddle the meaning. That's roughly how conventional computer methods handle complex quantum systems: the more particles and the stronger their correlations, the faster calculations drown in interference.

Predicting the yield of a chemical reaction is a bit like trying to guess the harvest knowing only the soil composition. Traditionally, chemists have relied on long columns of numbers, quantum descriptors, that describe electron energies but say nothing about molecular shape.

To capture every detail of a statue's shape, you could break it into millions of tiny cubes and describe each one — slow, heavy, and your computer's memory would groan at the seams. That's how 3D medical scans are usually handled: a CT or MRI is a huge array of such cubes (voxels), and to create or improve an image, the machine has to plow through all of them.

In a three-dimensional scene, each point is like a candy whose color changes depending on the viewing angle. To describe this play of shades, dozens of numbers are stored for each point — a special mathematical function invented by Joseph Fourier. Memory fills up instantly.

Unlike regular cameras, event-based ones only record moments when light becomes brighter or dimmer. They don't produce snapshots; instead, they send an endless stream of such flashes. Scientists turned this stream into a sharp picture of the Earth while flying over it on a satellite. Each flash is like a piece of a mosaic; together they form a still image.

A drop of urine under a microscope isn't a flat puddle but a multi-layered volume. Like a stack of transparent pages: if you focus on one, the neighboring ones blur. The microscope's light only shows one depth layer sharply. To see all the cells, a lab technician had to take a series of snapshots, manually adjusting focus — tedious and slow.

The data from a single CT scan is comparable to several feature-length films. TCAM-Diff bypasses this bottleneck: the program compresses the 3D scene into three shadow projections — like a sculptor reconstructing a solid statue from silhouettes on three walls.

Medical scans are maps of tissue density. The brightness of each pixel reveals what’s there, much like photometry measures light intensity. But conventional algorithms analyze pixels in isolation, producing jagged organ boundaries. A new approach compels the program to prioritize smoothness.

Imagine photographing a statue in dim light. You face a dilemma: take one bright shot or a series of dim frames from different angles. In the first case, quality is limited by photon noise, the graininess that arises from a lack of photons, while in the second, details overlap, making it harder to reconstruct the 3D structure.

Tomographs don't produce ready-made pictures. They collect numerous 'shadows' — projections of innards from different angles or using various signals — and mathematically reconstruct a 3D picture from them.

X-ray magnetic circular dichroism is a precise tool that has long allowed the separation of spin and orbital magnetic moments. However, the classical approach resembles judging an orchestra by its volume: it relies on sum rules, reducing the entire fine structure of the spectrum to a couple of integrals.

Imagine a fleet of many ships, resembling a galaxy — a vast stellar system where every element follows the overall dynamics. From time to time, one ship breaks down, exploding like a supernova — a catastrophic event that can disrupt the integrity of the entire formation. If the damaged unit isn't replaced in time, the fleet loses combat capability.

Building a light chip is like a game where you have to guide a beam through a maze of tiny mirrors and beam splitters. Each element acts like a railroad switch: it either lets light pass straight through or directs it to an adjacent track. The goal is to produce a specific light pattern at the output while using as few 'switches' as possible. Usually, engineers spend years finding the shortest route for each task.

At the heart of almost every galaxy slumbers a supermassive black hole — the invisible conductor of a stellar orchestra. When galaxies collide, their central monsters draw closer, entwining in a gravitational dance. Sometimes a third partner joins the duo, and then a triple waltz begins, its outcome unpredictable.

On the screen is the result of a complex simulation: two planets collided, a shockwave ripples outward. A couple of clicks in an editor, and harsh shadows soften, noise smooths out, the background darkens. This kind of digital retouching (image editing to enhance appearance) has long been part of the scientist’s toolkit.

Creating a material with desired properties is an art that long relied on intuition and luck. Nature gives no hints: dozens of elements in the periodic table, and the number of their combinations tends to infinity. Previously, materials scientists resembled alchemists, trying everything in a row, or relied on statistical models that lost power beyond the known.

The brain doesn't learn every task from scratch: it transfers experience from one activity to another. But how do you know which tasks are truly related? Neuroscientists came up with a clever trick: let an artificial network "peek" at brain activity during one task and try to predict activity during another. If it works well, the tasks are close.

X-ray detectors work like honeycombs: particles flash here and there in hexagonal cells. Conventional algorithms stretch these combs onto a square grid, creating ghostly polarization artifacts. Scientists used a neural network that lives by the rules of the hive — it processes the hexagonal structure directly, preserving the natural pattern.

If you clap your hands in an empty room, the echo will tell you about its size and shape. Every object (a chair, a book, a person) adds its own signature to this echo: sound reflects off it in a particular way. The authors of the paper have figured out how to completely rewrite this acoustic signature. They surrounded a region of space with a closed surface of microphones and loudspeakers.

Through the ripples on the water, pebbles at the bottom of a pool look shifted and warped. In a similar way, the gravity of invisible dark matter bends the light of distant galaxies — a phenomenon known as weak lensing.

An X-ray tomograph is like a carpenter who examines a log from all sides to see where the void is inside. It takes dozens of flat shadow images, rotating the source and detector around the object. Then a 'reconstruction' program glues these shadows into a 3D model. The quickest reconstructor is the FBP algorithm (filtered back projection).

The vessels inside the skull resemble a mountain road's hairpin turns — sharp bends, narrowings, unexpected curves. When a traffic jam forms on such a serpentine, blood stops reaching the brain properly. To predict trouble, doctors need to see the entire flow: where it accelerates, where it swirls into eddies. The full picture comes from a special MRI, like slow-motion cinematography of fluid.

A glass chip, threaded with 128 optical waveguides, has been turned into a kind of quantum pinball machine. The role of balls is played by photons — quanta of light, and instead of mechanical flippers, microheaters control the routes, instantly altering the optical paths.

Imagine a river that doesn't flow in an open channel but through the inside of a sponge, with its currents shifting from place to place. That's a good way to think about the brain's glymphatic system: a network of ultra-fine channels around blood vessels, through which cerebrospinal fluid seeps into the tissue and washes away the waste that has piled up during the day.

You taste a fruit smoothie through two different straws. One lets through only the big chunks, the other only the liquid. Individually, the sensations are misleading, but together they give you the key: here, it seems, is banana, and here, strawberry.

Light passing through a turbid medium like milk or living tissue scatters in all directions. Reconstructing where light was absorbed more strongly is like guessing the shape of a fallen object from a puddle on the asphalt. Optical tomography solves this problem, but usually requires large computational resources.

A magnetic film just tens of atoms thick stores information in tiny magnetic vortices. But you can't look inside and see these vortices directly: the instrument only senses the weak magnetic field around the film. Reconstructing the magnetization pattern from such a field is like guessing the shape of an invisible relief from its shadow on a wall.

The X-ray machine moves around the breast in an arc, taking several images from different angles. From these flat frames, the computer assembles a 3D picture, like putting together a mosaic from pieces. But if the mosaic pieces are crumpled or smudged, the picture turns out blurry. In tomography, the role of "crumples" is played by noise and imperfections in the imaging.

A conventional lidar is like an artist who paints a landscape but sees only gray shadows. It measures distances to each point accurately, but can't tell whether it's looking at rock, wood, or plastic. To learn the composition, you need a spectrometer — a separate device that works slowly and can't keep up with a fast scanner. The new system takes a different approach.

A tomograph is like a photographer who walks around an object and takes pictures from different sides. Usually, it snaps at regular intervals and uses the same amount of light for each shot. But if you need to shoot quickly and not waste extra film, you have to choose: which angles matter more and where to direct more light.