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arXiv Computer Vision

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.

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arXiv Computer Vision
Oct 12

NovaSplat: Model-Free 6D Object Pose Estimation via 3D Gaussian Splatting Onboarding and Refinement

NovaSplat is a model‑free pipeline for 6D object pose estimation that builds a 3D Gaussian Splatting (3DGS) model from a short smartphone video during onboarding. The 3DGS model serves both as a template for detection and coarse pose estimation and as a differentiable backbone for pose refinement, which optimizes pose using photometric and geometric losses under a continuous rotation parametrization. The method outperforms all model‑based methods on a household‑object benchmark and all model‑free competitors on a challenging industrial benchmark, while a faster variant, NovaSplat‑Lite, achieves comparable accuracy with a 3.1× speedup.

By Georgios Floros, Evangelos Sartinas, Orestis Vangelis, Maria Pateraki
computer-visionbenchmarks
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arXiv Computer Vision
Sep 21

VGGT-CAD: Reconstructing Parametric CAD 3D Model with Geometric Grounding

VGGT-CAD is a geometry‑aware framework that reconstructs parametric CAD 3D models from single and multi‑view images. It incorporates pretrained 3D geometric priors by encoding camera parameters as condition tokens and jointly modeling them with image tokens. The method introduces a variable‑view cross‑view context aggregation module and a training‑free geometry‑aware view selection strategy, and decodes the learned representation into CAD command sequences using a non‑autoregressive decoder. Additionally, VideoCAD, a large‑scale multi‑view video benchmark derived from existing CAD data, is presented to evaluate the approach.

By Chunan Yu, Tianrun Chen, Fu Shen, Cheng Chen, Lanyun Zhu, Yang Yang
benchmarks
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Hugging Face Trending Papers
Aug 19

CoMVS-GS: Collaborative Multi-View Stereo and 3D Gaussian Splatting for Surface Reconstruction

CoMVS‑GS is a surface‑reconstruction framework that fuses Multi‑View Stereo (MVS) with 3D Gaussian splatting. It initializes Gaussian primitives from dense MVS points, uses PatchMatch‑3DGS mutual supervision to refine depths and normals, and replaces voxel‑based meshing with a Delaunay graph‑cut pipeline. Experiments on DTU, GauU‑Scene V2, and MatrixCity demonstrate competitive object‑level results and improved geometric accuracy and mesh compactness in outdoor scenes while preserving high rendering quality.

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arXiv AI
2d ago

Aligning Misaligned Benchmarks for Single-View Novel View Synthesis

The paper addresses the lack of standardized evaluation for single‑view novel view synthesis by highlighting the importance of aligning a method’s reference frame with the ground‑truth camera pose. It proposes using traditional 2D‑to‑2D and 2D‑to‑3D image matching to align 3D reconstructions and restricts evaluation to co‑visible regions where a correct ground truth exists. Experiments show that with proper alignment, 3D Gaussian‑based methods reach state‑of‑the‑art performance, and a simple baseline using a video generator and geometry estimator matches specialized novel‑view synthesis models without task‑specific training.

By Yang-Che Tseng, Erqun Dong, Jeong-gi Kwak, Gopal Sharma, Shakiba Kheradmand, Stephan Garbin, Hossam Isack, Kwang Moo Yi
benchmarkssafety
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arXiv Computer Vision
Oct 9

PointVGGT: Zero-Shot Multiview RGB-D Point Cloud Registration with Visual Geometry Foundation Priors

The paper introduces PointVGGT, a zero‑shot framework for multiview RGB‑D point cloud registration that replaces the traditional pairwise‑then‑global pipeline. It employs a foundation‑then‑refinement paradigm, using visual geometry foundation models to directly recover metrically consistent global poses and then refining them with voxelized spatial hashing and IRLS‑based bundle adjustment. Experiments on indoor, object‑centric, and outdoor datasets demonstrate superior registration accuracy and computational efficiency without any training.

By Haobo Jiang, Liang Yu, Jianmin Zheng
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