Generating 3D Scenes From Just One Image

The LucidDreamer project ties a variety of functions into a pipeline that can take a source image (or generate one from a text prompt) and “lift” its content into 3D, creating highly-detailed Gaussian splats that look great and can even be navigated.

Gaussian splatting is a method used to render NeRFs (Neural Radiance Fields), which are themselves a method of generating complex scenes from sparse 2D sources, and doing it quickly. If that is all news to you, that’s probably because this stuff has sprung up with dizzying speed from when the original NeRF concept was thought up barely a handful of years ago.

What makes LucidDreamer neat is the fact that it does so much with so little. The project page has interactive scenes to explore, but there is also a demo for those who would like to try generating scenes from scratch (some familiarity with the basic tools is expected, however.)

In addition to the source code itself the research paper is available for those with a hunger for the details. Read it quick, because at the pace this stuff is expanding, it honestly might be obsolete if you wait too long.

Slab Casting – A New Way To Combine 3D Printing And Ceramics

Slip casting can be messy both in processing and in making the original plaster mold. What if there was a better way, thanks to 3D printing?

[Allie Katz] has developed a new technique using 3D printed slab molds to make ceramics. By combining the ability of 3D printing to make intricate designs and the formability of clay, they have found a way to make reproducible clay objects without all that tedious mucking about with liquid clay.

[Katz] takes us through a quick “Mould Making 101” before showing how the slab casting press molds were made. Starting with a positive CAD design, the molds were designed to eliminate undercuts and allow for air infiltration since a plastic mold can’t suck the water out of the clay like a plaster one would. Some cookie clay cutters were also designed to help with the trickier bits of geometry. Once everything was printed, the molds were coated with cornstarch and clay was pressed in. After removal, any final details like handles can be added and the pieces are then fired as normal.

If you’d like to see some more 3D printing mixed up with ceramics, check out 3D printing glass with a laser, reliable ceramic slurry printing, or this TPU-based approach.

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A Transistor, But For Heat Instead Of Electrons

Researchers at UCLA recently developed what they are calling a thermal transistor: a solid-state device able to control the flow of heat with an electric field. This opens the door to controlling the transfer of heat in some of the same ways we are used to controlling electronics.

Heat management can be a crucial task, especially where electronics are involved. The usual way to manage heat is to draw it out with things like heat sinks. If heat isn’t radiating away fast enough, a fan can be turned on (or sped up) to meet targets. Compared to the precision and control with which modern semiconductors shuttle electrons about, the ability to actively manage heat seems lacking.

This new device can rapidly adjust thermal conductivity of a channel based on an electrical field input, which is very similar to what a transistor does for electrical conductivity. Applying an electrical field modifies the strength of molecular bonds in a cage-like array of molecules, which in turn adjusts their thermal conductivity.

It’s still early, but this research may open the door to better control of heat within semiconductor systems. This is especially interesting considering that 3D chips have been picking up speed for years (stacking components is already a thing, it’s called Package-on-Package assembly) and the denser and deeper semiconductors get, the harder it is to passively pull heat out.

Thanks to [Jacob] for the tip!

Some Bacteria Could Have A Rudimentary Form Of Memory

When we think of bacteria, we think of simple single-celled organisms that basically exist to consume resources and reproduce. They don’t think, feel, or remember… or do they? Bacteria don’t have brains, and as far as we know, they’re incapable of thought. But could they react to an experience and recall it later?

New research suggests that some bacteria could have a rudimentary form of memory of their experiences in the environment. They could even pass this memory down across generations via a unique mechanism. Let’s dive into the latest research that is investigating just what bacteria know, and how they happen to know it.

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Turbocharge Your Transient Sensors With Math

If you’ve made a robot or played around with electronics before, you might have used a time-of-flight laser distance sensor before. More modern ones detect not just the first reflection, but analyze subsequent reflections, or reflections that come in from different angles, to infer even more about what they’re looking at. These transient sensors usually aren’t the most accurate thing in the world, but four people from the University of Wisconsin managed to get far more out of one using some clever math. (Video, embedded below.)

The transient sensors under investigation here sends out a pulse of light and records what it receives from nine angles in individual histograms. It then analyzes these histograms to make a rough estimate of the distance for each direction. But the sensor won’t tell us how it does so and it also isn’t very accurate. The team shows us how you can easily get a distance measurement that is more accurate and continues by showing how the nine distance estimates can even distinguish the geometry it’s looking, although to a limited extent. But they didn’t stop there: It can even detect the albedo of the material it’s looking at, which can be used to tell materials apart!

Overall, a great hack and we think this technology has potential – despite requiring more processing power. Continue reading “Turbocharge Your Transient Sensors With Math”

Sine-wave Speech Demonstrates An Auditory One-way Door

Sine-wave speech can be thought of as a sort of auditory illusion, a sensory edge case in which one’s experience has a clear “before” and “after” moment, like going through a one-way door.

Sine-wave speech (SWS) is intentionally-degraded audio. Here are the samples, and here’s what to do:

  1. Choose a sample and listen to the sine-wave speech version (SWS). Most people will perceive an unintelligible mix of tones and beeps.
  2. Listen to the original version of the sentence.
  3. Now listen to the SWS version again.

Most people will hear only some tones and beeps when first listening to sine-wave speech. But after hearing the original version once, the SWS version suddenly becomes intelligible (albeit degraded-sounding).

These samples were originally part of research by [Chris Darwin] into speech perception, but the curious way in which one’s experience of a SWS sample can change is pretty interesting. The idea is that upon listening to the original sample, the brain — fantastic prediction and learning engine that it is — now knows better what to expect, and applies that without the listener being consciously aware. In fact, if one listens to enough different SWS samples, one begins to gain the ability to understand the SWS versions without having to be exposed to the originals. In his recent book The Experience Machine: How Our Minds Predict and Shape Reality, Andy Clark discusses how this process may be similar to how humans gain fluency in a new language, perceiving things like pauses and breaks and word forms that are unintelligible to a novice.

This is in some ways similar to the “Green Needle / Brainstorm” phenomenon, in which a viewer hears a voice saying either “green needle” or “brainstorm” depending on which word they are primed to hear. We’ve also previously seen other auditory strangeness in which the brain perceives ever-increasing tempo in music that isn’t actually there (the Accelerando Illusion, about halfway down the list in this post.)

Curious about the technical details behind sine-wave speech, and how it was generated? We sure hope so, because we can point you to details on SWS as well as to the (free) Praat software that [Chris] used to generate his samples, and the Praat script he wrote to actually create them.

Several video clips of a robot arm manipulating objects in a kitchen environment, demonstrating some of the 12 generalized skills

RoboAgent Gets Its MT-ACT Together

Researchers at Carnegie Mellon University have shared a pre-print paper on generalized robot training within a small “practical data budget.” The team developed a system that breaks movement tasks into 12 “skills” (e.g., pick, place, slide, wipe) that can be combined to create new and complex trajectories within at least somewhat novel scenarios, called MT-ACT: Multi-Task Action Chunking Transformer. The authors write:

Trained merely on 7500 trajectories, we are demonstrating a universal RoboAgent that can exhibit a diverse set of 12 non-trivial manipulation skills (beyond picking/pushing, including articulated object manipulation and object re-orientation) across 38 tasks and can generalize them to 100s of diverse unseen scenarios (involving unseen objects, unseen tasks, and to completely unseen kitchens). RoboAgent can also evolve its capabilities with new experiences.

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