PreView: Active View Selection for 3D Reconstruction Without Warm-up

Active view selection is a key component of 3D reconstruction, aiming to identify the most useful observations under a limited acquisition or reconstruction budget in practical scenarios. Effective view selection can reduce redundant data acquisition and reconstruction costs while preserving the quality of the final 3D model. However, many existing methods estimate the value of a view only after a warm-up reconstruction, where a preliminary scene representation must first be built from the initial observations. This paradigm not only introduces substantial computation before selection, but can also be unreliable when only a few initial observations are available, since the warm-up reconstruction itself may be incomplete or inaccurate. We observe that the value of a candidate view can instead be inferred from the new evidence it contributes beyond the currently observed views, without first constructing a reliable 3D scene. Based on this insight, we introduce PreView, a framework for view selection before 3D reconstruction. PreView uses feed-forward visual and geometric priors to extract features from available observations and explicitly models interactions between candidate views and the selected set to estimate each candidate's marginal contribution. This formulation supports different information regimes: when candidate images are available, PreView exploits appearance and geometric cues; otherwise, its pose-only variant performs viewpoint selection before acquisition using camera poses and the context established by the initial observations. Because candidate value is estimated directly from available observations, task preferences can also be incorporated naturally, steering the reconstruction budget between global scene quality and user-specified regions of interest. Experiments across multiple scenes show that PreView selects effective views without reconstruction warm-up, remains competitive in the strict pose-only setting, supports controllable task-aware reconstruction, and substantially reduces computation before selection.