Benchmarking Efficient & Effective Camera Pose Estimation Strategies for Novel View Synthesis

2026-03-20

AI Summary

This work addresses the common trade-off in efficient camera pose estimation, where speed is often achieved at the expense of accuracy, and proposes a hybrid paradigm that integrates neural network-based initializations with classical Structure-from-Motion (SfM) optimization. The approach maintains high reconstruction accuracy while significantly reducing the number of required feature points. To systematically evaluate the efficacy of sparse matching and neural initialization in guiding bundle adjustment, the authors construct a novel SfM benchmark tailored for novel view synthesis. Experiments demonstrate that merely lowering feature density can accelerate conventional SfM pipelines, yet the combination of neural priors with traditional optimization achieves the best balance between efficiency and accuracy. The publicly released benchmark aims to advance research in high-precision, efficient SfM methods.

Technology Category

• Computer Vision: Learning & Optimization for CV
• Intelligent Robots: State Estimation
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Security and Privacy: Large-scale security measurements

Abstract

Novel view synthesis (NVS) approaches such as NeRFs or 3DGS can produce photo-realistic 3D scene representation from a set of images with known extrinsic and intrinsic parameters. The necessary camera poses and calibrations are typically obtained from the images via Structure-from-Motion (SfM). Classical SfM approaches rely on local feature matches between the images to estimate both the poses and a sparse 3D model of the scene, using bundle adjustment to refine initial pose, intrinsics, and geometry estimates. In order to increase run-time efficiency, recent SfM systems forgo optimization via bundle adjustment. Instead, they train feed-forward (transformer-based) neural networks to directly regress camera parameters and the 3D structure. While orders of magnitude more efficient, such recent works produce significantly less accurate estimates. To stimulate research on developing SfM approaches that are both efficient \emph{and} effective, this paper develops a benchmark focused on SfM for novel view synthesis. Using existing datasets and two simple strategies for making the reconstruction process more efficient, we show that: (1) simply using fewer features already significantly accelerates classical SfM methods while maintaining high pose accuracy. (2) using feed-forward networks to obtain initial estimates and refining them using classical SfM techniques leads to the best efficiency-effectiveness trade-off. We will make our benchmark and code publicly available.

Problem

• Structure-from-Motion
• Novel View Synthesis
• Camera Pose Estimation
• Efficiency-Accuracy Trade-off
• Benchmarking

Innovation

• camera pose estimation
• Structure-from-Motion
• novel view synthesis
• efficiency-effectiveness trade-off
• bundle adjustment