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Looking for a challenging dataset for novel view synthesis and 3D semantics? Check out ScanNet++ at #ICCV23 (Oral)! 460+ scenes w/ 1mm laser scans, semantics, DSLR images, iPhone RGBD video Chandan Yeshwanth Yueh-Cheng Liu Matthias Niessner

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Angela Dai

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10,284 views • 2 years ago •via X (Twitter)

Science & Technology#ICCV23
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Abdullah Hamdi2 years ago

@chandan__yes @liuyuehcheng @MattNiessner Congrats for the team on the amazing work !

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Excited to announce ScanNet++ v2!🎉 Chandan Yeshwanth and Yueh-Cheng Liu have been working tirelessly to bring: 🔹1006 high-fidelity 3D scans 🔹+ DSLR & iPhone captures 🔹+ rich semantics Elevating 3D scene understanding to the next level!🚀 w/ Matthias Niessner
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Excited to announce ScanNet++ v2!🎉 Chandan Yeshwanth and Yueh-Cheng Liu have been working tirelessly to bring: 🔹1006 high-fidelity 3D scans 🔹+ DSLR & iPhone captures 🔹+ rich semantics Elevating 3D scene understanding to the next level!🚀 w/ Matthias Niessner

Angela Dai

47,552 views • 1 year ago

📢 ScanNet++ v2 Benchmark Release! 🏆 Test your state-of-the-art models on: 🔹 Novel View Synthesis 📸➡️🖼️ 🔹 3D Semantic & Instance Segmentation 🤖🔍🕶️ Shoutout to Chandan Yeshwanth and Yueh-Cheng Liu for their incredible work👏 🚀Check it out:
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📢 ScanNet++ v2 Benchmark Release! 🏆 Test your state-of-the-art models on: 🔹 Novel View Synthesis 📸➡️🖼️ 🔹 3D Semantic & Instance Segmentation 🤖🔍🕶️ Shoutout to Chandan Yeshwanth and Yueh-Cheng Liu for their incredible work👏 🚀Check it out:

Angela Dai

13,428 views • 1 year ago

🚀Join the 𝐒𝐜𝐚𝐧𝐍𝐞𝐭++ 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞 #CVPR2026 2025! Think your method can handle large-scale 3D scenes? Put it to the test: Updates: ✅ Preprocessed, undistorted DSLR images ✅ 3DGS demo: by Yueh-Cheng Liu, Chandan Yeshwanth
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🚀Join the 𝐒𝐜𝐚𝐧𝐍𝐞𝐭++ 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞 #CVPR2026 2025! Think your method can handle large-scale 3D scenes? Put it to the test: Updates: ✅ Preprocessed, undistorted DSLR images ✅ 3DGS demo: by Yueh-Cheng Liu, Chandan Yeshwanth

Angela Dai

12,619 views • 1 year ago

📢 QuickSplat: Fast 3D Surface Reconstruction via Learned Gaussian Initialization Yueh-Cheng Liu learns 2DGS initialization, densification, and optimization priors from ScanNet++ => fast & accurate reconstruction! Project:
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📢 QuickSplat: Fast 3D Surface Reconstruction via Learned Gaussian Initialization Yueh-Cheng Liu learns 2DGS initialization, densification, and optimization priors from ScanNet++ => fast & accurate reconstruction! Project:

Angela Dai

27,301 views • 1 year ago

How can we generate high-fidelity, complex 3D scenes? Quan Meng's LT3SD decomposes 3D scenes into latent tree representations, with diffusion on the latent trees enabling seamless infinite 3D scene synthesis! w/ Lei Li, Matthias Niessner
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How can we generate high-fidelity, complex 3D scenes? Quan Meng's LT3SD decomposes 3D scenes into latent tree representations, with diffusion on the latent trees enabling seamless infinite 3D scene synthesis! w/ Lei Li, Matthias Niessner

Angela Dai

29,422 views • 1 year ago

📢Panoptic Lifting for 3D Scene Understanding with Neural Fields #CVPR23 highlight! Given only posed RGB images of a scene, we optimize a panoptic radiance field representing color, depth, semantics, and instances at any point in space. Vid: Yawar Siddiqui
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📢Panoptic Lifting for 3D Scene Understanding with Neural Fields #CVPR23 highlight! Given only posed RGB images of a scene, we optimize a panoptic radiance field representing color, depth, semantics, and instances at any point in space. Vid: Yawar Siddiqui

Matthias Niessner

29,830 views • 3 years ago

IllumiNeRF 3D Relighting without Inverse Rendering Existing methods for relightable view synthesis -- using a set of images of an object under unknown lighting to recover a 3D representation that can be rendered from novel viewpoints under a target illumination
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IllumiNeRF 3D Relighting without Inverse Rendering Existing methods for relightable view synthesis -- using a set of images of an object under unknown lighting to recover a 3D representation that can be rendered from novel viewpoints under a target illumination

AK

12,369 views • 2 years ago

Novel view synthesis has long been a core challenge in 3D vision. But how much 3D inductive bias is truly needed? —Surprisingly, very little! Introducing "LVSM: A Large View Synthesis Model with Minimal 3D Inductive Bias"—a fully transformer-based approach that enables scalable, generalizable, and fully data-driven novel view synthesis, from sparse posed inputs. 🧵(1/6) Project Page:
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Novel view synthesis has long been a core challenge in 3D vision. But how much 3D inductive bias is truly needed? —Surprisingly, very little! Introducing "LVSM: A Large View Synthesis Model with Minimal 3D Inductive Bias"—a fully transformer-based approach that enables scalable, generalizable, and fully data-driven novel view synthesis, from sparse posed inputs. 🧵(1/6) Project Page:

Haian Jin

114,757 views • 1 year ago

📢 Intrinsic Image Fusion for Multi-View 3D Material Reconstruction 📢 We combine generative material priors with inverse path tracing: 1) define a parametric texture space 2) fuse monocular predictions across views into consistent textures 3) optimize low-dimensional parameters for physically-grounded reconstructions. The results are relightable PBR textures for 3D scenes: check out the result on a real-world 3D scan from the ScanNet++ dataset! 🌍 🎥 Great work by Peter Kocsis Lukas Höllein!
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📢 Intrinsic Image Fusion for Multi-View 3D Material Reconstruction 📢 We combine generative material priors with inverse path tracing: 1) define a parametric texture space 2) fuse monocular predictions across views into consistent textures 3) optimize low-dimensional parameters for physically-grounded reconstructions. The results are relightable PBR textures for 3D scenes: check out the result on a real-world 3D scan from the ScanNet++ dataset! 🌍 🎥 Great work by Peter Kocsis Lukas Höllein!

Matthias Niessner

22,897 views • 6 months ago

Zionists have always been good w semantics. They called their barbaric army “IDF” for a reason. Watch
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Zionists have always been good w semantics. They called their barbaric army “IDF” for a reason. Watch

Abier

32,408 views • 1 year ago

📢Diff3r: fast feed-forward 3DGS + per-scene optimization Yueh-Cheng Liu predicts optimization-ready 3DGS init end to end, computing implicit gradients via Implicit Function Theorem + Gauss-Newton approximation for fast & stable results Check it out:
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📢Diff3r: fast feed-forward 3DGS + per-scene optimization Yueh-Cheng Liu predicts optimization-ready 3DGS init end to end, computing implicit gradients via Implicit Function Theorem + Gauss-Newton approximation for fast & stable results Check it out:

Angela Dai

17,800 views • 3 months ago

Generative Novel View Synthesis with 3D-Aware Diffusion Models abs: project page:
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Generative Novel View Synthesis with 3D-Aware Diffusion Models abs: project page:

AK

304,708 views • 3 years ago

It’s over for him. He’s done. The rest is semantics and time.
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It’s over for him. He’s done. The rest is semantics and time.

MAGA Cult Slayer

238,354 views • 7 months ago

📢Introducing 360Anything, our method for lifting any perspective image or video to gravity-aligned 360° panoramas without using any camera or 3D information. This enables consistent novel view synthesis and 3D scene reconstruction. Project page: 🧵
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📢Introducing 360Anything, our method for lifting any perspective image or video to gravity-aligned 360° panoramas without using any camera or 3D information. This enables consistent novel view synthesis and 3D scene reconstruction. Project page: 🧵

Ziyi Wu

60,784 views • 5 months ago

Image & video synthesis struggle with the scale of truly large 3D scenes. mschneider456 presents a geometry-first approach : - structure first: mesh scaffold defining the scene - then appearance: mesh-conditioned image synthesis Check it out:
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Image & video synthesis struggle with the scale of truly large 3D scenes. mschneider456 presents a geometry-first approach : - structure first: mesh scaffold defining the scene - then appearance: mesh-conditioned image synthesis Check it out:

Angela Dai

19,512 views • 3 months ago

and Wan2.2 again. MoCam - novel view synthesis. Lets you change the camera trajectory of a photo/video. - Video Re-camera - 3D Photo Conversion - Cinematic Control - Precise orbital/pan/zoom control - Corrects geometric artifacts
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and Wan2.2 again. MoCam - novel view synthesis. Lets you change the camera trajectory of a photo/video. - Video Re-camera - 3D Photo Conversion - Cinematic Control - Precise orbital/pan/zoom control - Corrects geometric artifacts

Wildminder

14,069 views • 1 month ago

We also worked with the folks at JigSpace to view one of our scans in 3D (this is a Luer lock–check out the thread for more on how these ingenious medical connectors work).
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We also worked with the folks at JigSpace to view one of our scans in 3D (this is a Luer lock–check out the thread for more on how these ingenious medical connectors work).

Jon Bruner

44,644 views • 2 years ago

Episode 188 | Semitics, Semantics, And Sensibility Full episode:
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The Pocket Report

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🚀 Introducing 𝐈𝐃𝐌-𝐕𝐓𝐎𝐍 : A novel diffusion model for image-based virtual try-on! 👗 😍 Improves garment fidelity and generates authentic visuals. 🔧 Uses two modules to encode garment semantics: visual encoder for high-level & parallel UNet for low-level. Links below👇
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🚀 Introducing 𝐈𝐃𝐌-𝐕𝐓𝐎𝐍 : A novel diffusion model for image-based virtual try-on! 👗 😍 Improves garment fidelity and generates authentic visuals. 🔧 Uses two modules to encode garment semantics: visual encoder for high-level & parallel UNet for low-level. Links below👇

Gradio

104,492 views • 2 years ago

Check out CAT3D! Image(s)-to-3D in 1 minute! Given any number of real or generated images, CAT3D uses a multi-view diffusion prior to create consistent novel views. These views are used to reconstruct a 3D scene using NeRF/3DGS.
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Check out CAT3D! Image(s)-to-3D in 1 minute! Given any number of real or generated images, CAT3D uses a multi-view diffusion prior to create consistent novel views. These views are used to reconstruct a 3D scene using NeRF/3DGS.

Philipp Henzler

12,737 views • 2 years ago