Top Tweets for #DL3DV
If you have limited computing source, come by and see what we can help on testing your methods on #DL3DV-140!
We provide an easy-to-use and flexible interface for benchmarking on #DL3DV-140. Just send us a request via Hugging Face or GitHub — we’ll help you visualize and compare your method against SOTA such as triangle-splat, 2D/3DGS, ZipNeRF.
Save time. Benchmark smarter!
Many Congratulations to @jianyuan_wang, @MinghaoChen23, @n_karaev, Andrea Vedaldi, Christian Rupprecht and @davnov134 for winning the Best Paper Award @CVPR for "VGGT: Visual Geometry Grounded Transformer" 🥇🎉 🙌🙌 #CVPR2025!!!!!!
Although #DL3DV contains only 10K samples, this project was entirely self-initiated and unfunded, relying solely on the commitment of our team members. Over 80% of contributors has/is pursuing a PhD. I am incredibly proud of their determination in achieving this mileston.
Thanks @_akhaliq.
It takes a lot of effort to collect this large-scale video dataset (more than 10K video) for 3D vision community. We hope you enjoy the gift!
I am thrilled that #DL3DV is contributing to advancements in camera control for Cosmos. It reminds me of the incredible challenges we faced to bring this student-led project to life and create a real impact on the community. The journey was tough but truly rewarding!
We are releasing Cosmos, a world foundation model platform for building and advancing Physical AI. It offers diffusion and autoregressive pretrained models ranging from 4B to 14B.
🔧Try it, finetune it, & explore: https://t.co/bxTt7ICGNM.
🔍Dive into our 75-page tech report for all the details! https://t.co/Lo2WXQq2tx
Long-LRM: Long-sequence Large Reconstruction Model for Wide-coverage Gaussian Splats
@chenziwee, @HaoTan5, @KaiZhang9546, @Sai__Bi, @fujun_luan, @YicongHong, @fuxinli2, @zexiangxu
tl;dr: Mamba2 blocks+transformer blocks; token merging+Gaussian pruning
https://t.co/3MnUaX1bIg
@Eng_Hemdi @CVPR Great work! We proposed a new benchmark #DL3DV-140 for NVS tasks with diverse scenes including indoor/outdoor, reflection, transparency, etc., #DL3DV-140 would better represent the complexity of the world for assessing the current NVS methods.
We will present "#DL3DV-10K: A Large-Scale Scene Dataset for Deep Learning-based 3D Vision" at #AI4CC @CVPR 2024. Our poster is June 17th 11:20 am- 12:20 pm. Feel free to come by and let's talk about #GenAI in 3D vision!
Welcome to our fifth AI for Content Creation (https://t.co/AQ7r77cKVq) workshop at CVPR 2024 on June 17th at Seattle Convention Center — Summit 342! Our late-breaking talks are Sora by @_tim_brooks and Genie by @YugeTen & @jparkerholder 🔥🔥🔥
- Three training views on #DL3DV-10K datasets
- Camera poses & intrinsics are unknown
- Rendering by interpolation
- Resolution: 1920x1080
I can tell, pseudo-views are needed to enhance the quality.
Speeding your view synthesis(<40s) with #InstantSplat!
Our large-scale, pose-free method trains in just 37 seconds from sparse views—no #COLMAP, no intrinsics needed.
Achieving nearly 30dB test PSNR with just 12 images, New standard in #NVS and new training efficiency.
Project page 👉https://t.co/WqMEEZmFud
Paper 📷: https://t.co/G7i8ii9908
@WayneINR @JeromeRevaud Maybe you do not need to collect by your own and we can save your time. Take a look at #DL3DV (https://t.co/pDGiSTe0hk). We have diverse scenes with complex lightings for you.
@JeromeRevaud #DUSt3R is definitely a great work and deserves at least an oral. You are not alone. Same case here where our #DL3DV got 4.67 ratings but a poster. I believe time will tell.
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