arXiv Computer Vision
Sep 17

CADSplat: Sparse-View 3D Gaussian Splatting Aided by CAD Models for Robust, Photorealistic Digital-Twin Reconstruction

CADSplat is a framework that reconstructs photorealistic, geometrically accurate digital twins from fewer than 15 wide‑baseline images by regularizing 3D Gaussian Splatting with an explicit CAD shape prior. It matches segmented object silhouettes to a CAD library to retrieve a suitable model and camera poses, then anchors Gaussian primitives to the model’s surface and jointly optimizes splat parameters, registration, and a non‑rigid deformation field. Experiments on two real‑world datasets show CADSplat outperforms baselines, especially in sparse and self‑occluded scenarios, and its gains mainly stem from constraining splats to a surface rather than the CAD shape itself.

By Kristof Overdulve, Lode Jorissen, Nick Michiels
arXiv Computer Vision
Sep 21

Field Tracking of Insects Using a Stereoscopic Event-Based Camera Setup

The paper presents a method for tracking insects in the field using a stereoscopic event-based camera setup. By converting asynchronous events into conventional video formats, the authors combine the high temporal resolution of event cameras with standard video processing techniques to capture detailed insect flight movements. The stereoscopic configuration enables continuous, low‑latency 3D tracking, reducing motion blur and improving accuracy in natural environments.

By Pratham G. Shenwai, Martin J. Lankheet, John T. Hrynuk, Mandiyam Y. Mahadeeswara, Mandyam V. Srinivasan, Sridhar Ravi