Extracting Triangular 3D Models, Materials, and Lighting from Images
The rate of progress in this field -- reverse rendering -- has just been phenomenal. It seems like groundbreaking research is published every couple of months.<p>Something I have mentioned in other similar forums is that up until recently, computers were catching up to mathematics. The algorithms we use are often centuries old, but didn&#x27;t have widespread practical applicability until computers could realise them.<p>The movie Prometheus (2012) made me realise that we had crossed a threshold. In one particular scene[1], some scientists throw up some drones up in the air. The drones zip down curved corridors, mapping them in <i>real time</i> using spinning lasers (LIDAR). At the time, that was <i>science fiction</i>. It was &quot;future tech&quot; that <i>appeared</i> to rely on not-yet-invented computer power.<p>But the computer power <i>was</i> available! GPUs of the era were already able to put out multiple teraflops, and should have been able to process that amount of data.<p>What was missing was the <i>mathematics</i>. We had the substrate, but not the code to run on it.<p>I doubt anyone had noticed that the tables had turned. That things had flipped over to the other side.<p>Since then, high-end workstations are rapidly approaching a petaflop of computer power. Suddenly the maths is <i>catching up</i>. This paper is a wonderful example of that happening!<p>Or to put it another way, I like to challenge computer scientists with this thought experiment:<p>&quot;Imagine being given a workstation with 1024 CPU cores, a terabyte of memory, and a GPU to match. Could you <i>fully</i> utilise this machine to solve everyday problems in your speciality? If not, why not? What are you <i>missing?</i>&quot;<p>-- more often then not, the answer is now &quot;the code&quot;.<p>[1] <a href="https:&#x2F;&#x2F;www.youtube.com&#x2F;watch?v=yO-eduvo904" rel="nofollow">https:&#x2F;&#x2F;www.youtube.com&#x2F;watch?v=yO-eduvo904</a>