CVPR 2026 Papers

We present AMB3R, a multi-view feed-forward model for metric-scale dense 3D reconstruction that addresses diverse 3D vision tasks. The key idea is to employ a sparse, yet compact, volumetric scene representation as our backend, enabling geometric reasoning with spatial compactness. We further introduce AMB3R-VO and AMB3R-SfM, two training-free, model-agnostic pipelines that extend multi-view networks to uncalibrated visual odometry (online) or structure from motion for any number of images without test-time optimization. Compared to prior 3D foundation models, AMB3R achieves state-of-the-art performance in camera pose, depth, metric-scale estimation, and 3D reconstruction, while AMB3R-VO and AMB3R-SfM even surpass optimization-based SLAM and SfM systems with dense reconstruction priors on common benchmarks.

To achieve compactness or cost reduction for optical lens systems, designers typically rely on commercial software to design lens systems independently of post-processing algorithms, leading to excessive dependence on designers' expertise and often requiring significant time. Recently, joint optimization approaches utilizing differentiable ray tracing have emerged, demonstrating significant potential in lens design tasks. However, existing pipelines lack accurate and efficient diffraction modeling capabilities for miniature optical systems. In this work, we propose a novel lens component deletion pipeline for miniature optical systems, which automatically deletes the suitable lens component, and then optimizes both the lens system and the post-processing network to achieve joint aberration correction. Additionally, we introduce a novel metric for evaluating the contribution of each lens component within an optical system, aimed at identifying the lens component that has the least impact on the system. We also develop an efficient differentiable point spread function estimation method based on the Rayleigh-Sommerfeld diffraction model, significantly reducing GPU memory consumption. Our proposed pipeline achieves lens component deletion while maintaining imaging quality comparable to the original lens system, thereby enabling the compactness or cost-effective optimization of miniature optical systems. Project page: https://github.com/WenguanZhang/HappyLens

Imaging system design is a complex, time-consuming, and largely manual process; LiDAR design, ubiquitous in mobile devices, autonomous vehicles, and aerial imaging platforms, adds further complexity through unique spatial and temporal sampling requirements. In this work, we propose a framework for automated, task-driven LiDAR system design under arbitrary constraints. To achieve this, we represent LiDAR configurations in a continuous six-dimensional design space and learn task-specific implicit densities in this space via flow-based generative modeling. We then synthesize new LiDAR systems by modeling sensors as parametric distributions in 6D space and fitting these distributions to our learned implicit density using expectation-maximization, enabling efficient, constraint-aware LiDAR system design. We validate our method on diverse tasks in 3D vision, enabling automated LiDAR system design across real-world-inspired applications in face scanning, robotic tracking, and object detection.

Estimating camera geometry typically involves solving minimal problems formulated as systems of multivariate polynomial equations, which often pose computational challenges when using existing Grobner-basis or resultant-based methods due to matrix inversion needed in the online solver. Here we propose a sampling-based, matrix inversion-free method that constructs the solvers using sparse hidden-variable resultants. The determinant polynomial in the hidden variable is efficiently reconstructed via inverse fast Fourier transform interpolation from sampled evaluations, avoiding symbolic expansion. Solving this polynomial yields the hidden variable, and the remaining unknowns are recovered by identifying rank-1 deficient submatrices and applying Cramer's rule. A greatest common divisor-based criterion ensures robust submatrix identification under noise. Experiments on diverse minimal problems demonstrate that the proposed solver achieves strong numerical stability and competitive runtime, particularly for small-scale problems, providing a practical alternative to traditional Grobner-basis and resultant-based solvers.