PointWeave: Feed-Forward View Synthesis with Explicit Point Interface

We introduce PointWeave, a feed-forward novel-view renderer that uses explicit 3D points to guide learned appearance reconstruction. Existing 3DGS-based methods render explicit primitives efficiently, but recovering fine detail can require many primitives. Geometric-free methods offer flexible and scalable appearance modeling, but can struggle to maintain geometric consistency under unseen camera transformations. In contrast, PointWeave combines explicit geometry with learned image synthesis. It predicts 3D points with appearance features from input views, and uses learned ray-to-point attention to aggregate their features for each target ray. The resulting feature map is combined with colors retrieved from the input views using the inferred geometry and passed through a decoder to reconstruct the target image. On RealEstate10K, PointWeave achieves 31.38 dB PSNR with 8,192 points, outperforming recent 3DGS-based methods by 1.6 dB while using fewer primitives. It matches the performance of state-of-the-art geometric-free methods with up to 4× faster rendering and substantially better geometric consistency under camera roll, field-of-view, pixel-aspect and world-scale changes. It also outperforms the baselines in zero-shot transfer to unseen datasets, with 0.54–2.02 dB PSNR gains on ACID, DL3DV, ScanNet++, and DTU.