arXiv Computer Vision
Gaussians on Fire: High-Frequency Reconstruction of Flames
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arXiv AI
Jun 2
A Survey of 3D Reconstruction with Event Cameras
arXiv:2505. 08438v4 Announce Type: replace-cross Abstract: Event cameras are rapidly emerging as powerful vision sensors for 3D reconstruction, uniquely capable of asynchronously capturing per-pixel brightness changes.
arXiv AI
Jul 2
World from Motion: Generative Dynamic Gaussian Reconstruction from Monocular Video
arXiv:2607. 01202v1 Announce Type: cross Abstract: We present World from Motion, a method for generating freely renderable dynamic 3D Gaussian representations from monocular videos.
arXiv AI
Sep 28
Geometric-Photometric Event-based 3D Gaussian Ray Tracing
The paper introduces GPERT, a framework that separates event-based 3D Gaussian Splatting into two rendering branches: event-by-event geometry rendering and snapshot-based radiance rendering. By employing ray-tracing and warped event images, GPERT balances accuracy and temporal resolution, achieving state‑of‑the‑art results on real‑world datasets and competitive performance on synthetic data. The method operates without pretrained models or COLMAP initialization, offers flexible event selection, and produces sharp reconstructions of scene edges with rapid training.
arXiv Computer Vision
Oct 8
DynStream: Online Streaming 4D Gaussian Reconstruction of Dynamic Worlds from Unposed Video
arXiv:2610.09720v1 Announce Type: new Abstract: Online reconstruction of dynamic 4D scenes from long, unposed streaming videos requires both continuous processing and photorealistic rendering, which...
arXiv Computer Vision
Sep 22
Dynamic Thermal Gaussians: Multimodal 4D Gaussian Splatting
arXiv:2609.24531v1 Announce Type: new Abstract: Thermography plays a vital role in military and broader thermal analysis applications. Recent progress in 3D thermal reconstruction has extended temper...
arXiv Computer Vision
Sep 22
Revisiting Multi-View Stereo: A Sequence-to-Sequence Formulation
The paper proposes a new sequence-to-sequence formulation for multi-view stereo (MVS) that jointly predicts 3D geometry for all input views using a global transformer architecture. It introduces ray‑map embeddings to inject camera parameters into image tokens and a unified global cost volume to capture 3D structure across all views. Experiments on public benchmarks demonstrate state‑of‑the‑art performance, outperforming both traditional MVS and feed‑forward reconstruction baselines.