PCF-MAR: Pose- and calibration-free 3D mesh-aligned surface attribute reconstruction from sparse-view images

Reconstructing mesh-aligned surface attributes from images is important for analysis and visualization, but it remains challenging in practice because multi-view capture is often sparse and camera intrinsics/extrinsics may be unavailable or unreliable. Most learning-based 3D reconstruction methods prioritize geometry recovery and rendering-oriented texturing and typically assume calibrated cameras, making them less suitable when the goal is analysis-ready per-vertex attributes on a given mesh under uncalibrated sparse views. We propose PCF-MAR, a pose- and calibration-free neural framework that predicts view-consistent per-vertex attributes from sparse, uncalibrated images conditioned on a geometry-only mesh. PCF-MAR replaces camera supervision with dense cross-view correspondences and a mesh prior: detector-free pairwise matches are distilled into local correspondence embeddings that modulate a shared transformer image encoder and a global correspondence embedding that summarizes cross-view agreement and yields a scalar reliability gate. A point-based mesh encoder maps the input mesh to triplane features for decoder initialization and provides a global mesh modulation vector. A mesh-anchored triplane transformer decoder performs geometry-aware fusion via mesh-conditioned layer normalization, while the correspondence gate scales cross-attention residuals to suppress inconsistent evidence and output an explicit reliability signal. PCF-MAR is trained end-to-end with mesh-space supervision, image-space photometric and perceptual losses, and triplane/mesh regularization. Experiments on Google Scanned Objects and OmniObject3D demonstrate improved mesh-aligned attribute reconstruction over representative baselines; relative to a recent baseline, PCF-MAR improves PSNR by +29.6% on average and reduces LPIPS and VC-MSE by 71.1% and 94.2%, respectively, without camera calibration or pose estimation.