Cameron Smith

Researching self-supervised 3D learning and now for 3D-aware robotics too 🤠
Doing a PhD with Yue Wang @ USC!

[Cameron Smith @omcamsmith]

@jadroy2 make one for robot arms too 🙏

[Cameron Smith @omcamsmith]

IMO this is a much needed quality-of-life upgrade to the 3D-aware robotics stack for the new paradigm of vision-based robot control
Thanks as always to the great mentor team of @basilevanh @vitorguizilini @yuewang314

[Cameron Smith @omcamsmith]

Inferring a robot’s 3D state (robot↔camera pose + joint angles / link poses) is still weirdly clunky

Introducing Fiducial Exoskeletons: Image-Centric Robot State Estimation!
https://t.co/4u8wdD8xZ8

FidEx makes it fast + robust from a single image ���

[Cameron Smith @omcamsmith]

I've found it incredibly useful in my recent experiments -- camera pose and intrinsics from a single image and even moving camera stream, as well as for really easy one-second robot re-calibration

[Yue Wang @yuewang314]

Introducing the USC Physical Superintelligence (PSI) Lab (https://t.co/nACO3kGxdD). We are rebranding to better reflect our current focus. From here on out, we are tackling one thing: solving robotics and physical intelligence with every model, every bug, and every line of code. And yes, we are hiring at all levels, especially PhDs in this cycle and potential PostDocs who are excited about robotics. We hope you can join us in this journey! 1/9

[Yue Wang @yuewang314]

[Hiring!] I am hiring multiple PhDs @CSatUSC @USCViterbi for this cycle. If you're interested in scene representations, neural simulation, generative AI, and robotics, feel free to mention my name in your application (no need to email). For USC masters/undergrads who're interested in our research, feel free to fill in this form https://t.co/PgyUXhwddR.

[Ayush Tewari @_atewari]

I am looking for graduate students for my new lab at the University of Cambridge! Join me to understand and build models of visual perception.

[Vincent Sitzmann @vincesitzmann]

Introducing “FlowMap”, the first self-supervised, differentiable structure-from-motion method that is competitive with conventional SfM like Colmap!

https://t.co/ZwJGWfdytQ

IMO this solves a major missing piece for internet-scale training of 3D Deep Learning methods.

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[Boyuan Chen @BoyuanChen0]

I quit PhD (for a day) and opened a boba shop at @MIT - Generative Boba! It’s a huge success - right next to our office so all the AI researchers are enjoying it. Checkout our boba diffusion algorithm in the poster to understand why boba generation is so important to @MIT_CSAIL !

[Vincent Sitzmann @vincesitzmann]

Introducing pixelSplat: feed-forward Gaussian splats from image pairs! Led by @DavidCharatan and @sizhe_lester_li, collaborating with @taiyasaki! We propose a memory-efficient, fast and editable alternative to pixelNeRF based on 3D Gaussian Splatting! https://t.co/j3EaCtFCGR 1/n

[Boyuan Chen @BoyuanChen0]

I am presenting my paper “RaMP: Self-Supervised Reinforcement Learning that Transfers using Random Features” at poster 1427 from 5-7pm at Neurips 2023! Don’t miss it!

Website: https://t.co/jopV0V8UsC

[Vincent Sitzmann @vincesitzmann]

How can we learn to generate 3D scenes directly with diffusion models if we only have images, no ground-truth 3d scenes? Ayush, Tianwei and George will tell you at our poster “diffusion with Forward Models”, #202!

[Cameron Smith @omcamsmith]

Placing the camera on top of a pet is one of the coolest applications of NeRFs I've seen

[Chonghyuk (ND) Song @ndsong95]

Ever wondered what the world looks like to your pet dog? Our latest #ICCV2023 paper, Total-Recon, enables embodied view synthesis of deformable scenes from a casual RGBD video: https://t.co/zpMk5r40iQ

Drop by poster #10 on Friday 10:30~12:30pm in Rm. Foyer Sud to know more!

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[Ayush Tewari @_atewari]

Check out “Diffusion with Forward Models”. We learn to sample realistic 3D scenes from a single input image. Our models are trained on videos and do not require 3D training data!

https://t.co/xslKZeYVGp

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[Cameron Smith @omcamsmith]

One of the coolest 3D computer vision papers, ever!

[Vincent Sitzmann @vincesitzmann]

Introducing “Diffusion with Forward Models”, 𝗮 𝗺𝗼𝗱𝗲𝗹 𝘁𝗵𝗮𝘁 𝗰𝗮𝗻 𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗲 𝗱𝗶𝘃𝗲𝗿𝘀𝗲, 𝗿𝗲𝗮𝗹 𝟯𝗗 𝘀𝗰𝗲𝗻𝗲𝘀 𝗳𝗿𝗼�� 𝗮 𝘀𝗶𝗻𝗴𝗹𝗲 𝗶𝗺𝗮𝗴𝗲, 𝘁𝗿𝗮𝗶𝗻𝗲𝗱 𝘄𝗶𝘁𝗵 𝗶𝗺𝗮𝗴𝗲𝘀 𝘄/𝗼 𝗮𝗻𝘆 𝟯𝗗 𝗱𝗮𝘁𝗮!

https://t.co/8CbXGME46Q

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