CLASP: Complementary Chunk and Long Passes for Feed-Forward 3D Reconstruction

Feed-forward 3D reconstruction faces two distinct failures as sequences grow: camera predictions can deteriorate in a full-sequence pass while overlapping chunks accumulate pose and scale errors through their alignment chain. We present CLASP (Chunk-Linked, Aligned-Scale Pose graph), a training-free framework that combines both inference contexts of the same frozen model in one pose graph. Chunks supply local relative motion and output geometry. The long pass supplies a common scale reference through dense depth ratios between predictions of the same frame, independently of camera motion and overlap alignment. It also contributes long-range pose constraints where they agree with a chunk-derived trajectory. Separating these roles retains useful scale information when long-pass pose constraints are rejected, allowing one formulation to handle long and short captures without a dataset-specific switch. Experiments with three backbones on six datasets show that CLASP lowers trajectory error on long driving sequences and keeps pose accuracy close to or above a full-sequence pass on shorter captures.