Recollection from Pensieve: Novel View Synthesis via Learning from Uncalibrated Videos

TL;DR

This paper introduces a two-stage self-supervised approach for novel view synthesis from uncalibrated videos, combining implicit scene learning with explicit 3D primitive prediction to achieve high-quality results without prior geometric information.

Contribution

It proposes a novel two-stage training strategy that enables view synthesis from raw uncalibrated videos without geometric priors, bridging the gap between implicit and explicit 3D representations.

Findings

Achieves high-quality novel view synthesis without camera calibration.

Provides accurate camera pose estimation from uncalibrated videos.

Demonstrates effectiveness on large-scale uncalibrated video datasets.

Abstract

Currently almost all state-of-the-art novel view synthesis and reconstruction models rely on calibrated cameras or additional geometric priors for training. These prerequisites significantly limit their applicability to massive uncalibrated data. To alleviate this requirement and unlock the potential for self-supervised training on large-scale uncalibrated videos, we propose a novel two-stage strategy to train a view synthesis model from only raw video frames or multi-view images, without providing camera parameters or other priors. In the first stage, we learn to reconstruct the scene implicitly in a latent space without relying on any explicit 3D representation. Specifically, we predict per-frame latent camera and scene context features, and employ a view synthesis model as a proxy for explicit rendering. This pretraining stage substantially reduces the optimization complexity and encourages the network to learn the underlying 3D consistency in a self-supervised manner. The learned latent camera and implicit scene representation have a large gap compared with the real 3D world. To reduce this gap, we introduce the second stage training by explicitly predicting 3D Gaussian primitives. We additionally apply explicit Gaussian Splatting rendering loss and depth projection loss to align the learned latent representations with physically grounded 3D geometry. In this way, Stage 1 provides a strong initialization and Stage 2 enforces 3D consistency - the two stages are complementary and mutually beneficial. Extensive experiments demonstrate the effectiveness of our approach, achieving high-quality novel view synthesis and accurate camera pose estimation, compared to methods that employ supervision with calibration, pose, or depth information. The code is available at https://github.com/Dwawayu/Pensieve.