arXiv subjects

Dániel Baráth

Publications and source records attributed to Dániel Baráth.

4 recordsLinked to original sources

arXiv2026

ZipSplat: Fewer Gaussians, Better Splats

Feed-forward 3D Gaussian Splatting reconstructs scenes from images in seconds. Pixel-aligned methods keep every Gaussian grounded in an observation, but produce redundant, potentially misaligned Gaussians across views. Token-based methods instead aggregate all views into a fixed set of learned tokens but lack explicit observation support and use a fixed reconstruction budget. We introduce ZipSplat, a method that predicts a compact, observation-grounded scene representation whose size is set at inference. It derives each scene token from an image patch rather than a learned query, and decodes it into freely placed 3D Gaussians. Tokens across views are merged into a compact set whose size is adjustable at inference. However, without geometric supervision, decoded Gaussians can drift from their source patch, and merging can mix tokens from different surfaces. We therefore introduce two geometric losses to ground and shape this representation. A cone loss ties each token's Gaussians to its source patch, and GeoCE aligns token similarity with 3D proximity so that tokens observing the same surface merge together. Building on these observation--geometry associations, we further introduce TokenBA, a token-level bundle adjustment that refines scene tokens and camera poses in a few seconds. ZipSplat requires neither camera poses nor intrinsics, yet outperforms pixel-aligned and token-based baselines on DL3DV and RealEstate10K, even those given ground-truth cameras. It further generalizes zero-shot to Mip-NeRF360 and ScanNet++. Our project page is at https://veichta.com/zipsplat.

cs.CV↗

arXiv2024

Gravity-aligned Rotation Averaging with Circular Regression

Reconstructing a 3D scene from unordered images is pivotal in computer vision and robotics, with applications spanning crowd-sourced mapping and beyond. While global Structure-from-Motion (SfM) techniques are scalable and fast, they often compromise on accuracy. To address this, we introduce a principled approach that integrates gravity direction into the rotation averaging phase of global pipelines, enhancing camera orientation accuracy and reducing the degrees of freedom. This additional information is commonly available in recent consumer devices, such as smartphones, mixed-reality devices and drones, making the proposed method readily accessible. Rooted in circular regression, our algorithm has similar convergence guarantees as linear regression. It also supports scenarios where only a subset of cameras have known gravity. Additionally, we propose a mechanism to refine error-prone gravity. We achieve state-of-the-art accuracy on four large-scale datasets. Particularly, the proposed method improves upon the SfM baseline by 13 AUC@$1^\circ$ points, on average, while running eight times faster. It also outperforms the standard planar pose graph optimization technique by 23 AUC@$1^\circ$ points. The code is at https://github.com/colmap/glomap.

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arXiv2024

Global Structure-from-Motion Revisited

Recovering 3D structure and camera motion from images has been a long-standing focus of computer vision research and is known as Structure-from-Motion (SfM). Solutions to this problem are categorized into incremental and global approaches. Until now, the most popular systems follow the incremental paradigm due to its superior accuracy and robustness, while global approaches are drastically more scalable and efficient. With this work, we revisit the problem of global SfM and propose GLOMAP as a new general-purpose system that outperforms the state of the art in global SfM. In terms of accuracy and robustness, we achieve results on-par or superior to COLMAP, the most widely used incremental SfM, while being orders of magnitude faster. We share our system as an open-source implementation at {https://github.com/colmap/glomap}.

cs.CV↗

arXiv2024

P1AC: Revisiting Absolute Pose From a Single Affine Correspondence

Affine correspondences have traditionally been used to improve feature matching over wide baselines. While recent work has successfully used affine correspondences to solve various relative camera pose estimation problems, less attention has been given to their use in absolute pose estimation. We introduce the first general solution to the problem of estimating the pose of a calibrated camera given a single observation of an oriented point and an affine correspondence. The advantage of our approach (P1AC) is that it requires only a single correspondence, in comparison to the traditional point-based approach (P3P), significantly reducing the combinatorics in robust estimation. P1AC provides a general solution that removes restrictive assumptions made in prior work and is applicable to large-scale image-based localization. We propose a minimal solution to the P1AC problem and evaluate our novel solver on synthetic data, showing its numerical stability and performance under various types of noise. On standard image-based localization benchmarks we show that P1AC achieves more accurate results than the widely used P3P algorithm. Code for our method is available at https://github.com/jonathanventura/P1AC/ .

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