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首次观测 · 记录 #1CVF · CVPR 2026:cvf:eee7735e932e3535a695916b5d43cfbcCVF · CVPR 2026

Radiance Meshes for Volumetric Reconstruction

Mai, Alexander, Hedstrom, Trevor, Kopanas, George, Kontkanen, Janne, Kuester, Falko, Barron, Jonathan T.

原文摘要

We introduce Radiance Meshes for representing radiance fields with constant density tetrahedral cells produced with a Delaunay tetrahedralization.Unlike a Voronoi diagram, a Delaunay tetrahedralization yields simple triangles that are natively supported by existing hardware. As such, our model is able to perform exact and fast volume rendering using both rasterization and ray-tracing. We introduce a new rasterization method that achieve faster rendering speeds than all prior radiance field representations (assuming an equivalent number of primitives and resolution) across a variety of platforms.Optimizing the positions of Delaunay vertices introduces topological discontinuities (edge flips). To solve this, we use a Zip-NeRF-style backbone which allows us to express a smoothly varying field even when the topology changes.Our rendering method exactly evaluates the volume rendering equation and enables high quality, real-time view synthesis on standard consumer hardware. Our tetrahedral meshes also lend themselves to a variety of exciting applications including fisheye lens distortion, physics-based simulation, editing, and mesh extraction.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

首次观测 · 记录 #1CVF · CVPR 2026:cvf:fb0028f49f814d745a8ae7a657560a5fCVF · CVPR 2026

ZipMap: Linear-Time Stateful 3D Reconstruction via Test-Time Training

Jin, Haian, Wu, Rundi, Zhang, Tianyuan, Gao, Ruiqi, Barron, Jonathan T., Snavely, Noah, Hołyński, Aleksander

原文摘要

Feed-forward transformer models have driven rapid progress in 3D vision, but state-of-the-art methods such as VGGT and \pi^3 have a computational cost that scales quadratically with the number of input images, making them inefficient when applied to large image collections. Sequential-reconstruction approaches reduce this cost but sacrifice reconstruction quality. We introduce ZipMap, a stateful feed-forward model that achieves linear-time, bidirectional 3D reconstruction while matching or surpassing the accuracy of quadratic-time methods. ZipMap employs test-time training layers to zip an entire image collection into a compact hidden scene state in a single forward pass, enabling reconstruction of over 700 frames in under 10 seconds on a single H100 GPU, more than 20x faster than state-of-the-art methods such as VGGT. Moreover, we demonstrate the benefits of having a stateful representation in real-time scene-state querying and its extension to sequential streaming reconstruction.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表