Zero-Shot Novel View and Depth Synthesis with Multi-View Geometric Diffusion (2501.18804v1)

Abstract: Current methods for 3D scene reconstruction from sparse posed images employ intermediate 3D representations such as neural fields, voxel grids, or 3D Gaussians, to achieve multi-view consistent scene appearance and geometry. In this paper we introduce MVGD, a diffusion-based architecture capable of direct pixel-level generation of images and depth maps from novel viewpoints, given an arbitrary number of input views. Our method uses raymap conditioning to both augment visual features with spatial information from different viewpoints, as well as to guide the generation of images and depth maps from novel views. A key aspect of our approach is the multi-task generation of images and depth maps, using learnable task embeddings to guide the diffusion process towards specific modalities. We train this model on a collection of more than 60 million multi-view samples from publicly available datasets, and propose techniques to enable efficient and consistent learning in such diverse conditions. We also propose a novel strategy that enables the efficient training of larger models by incrementally fine-tuning smaller ones, with promising scaling behavior. Through extensive experiments, we report state-of-the-art results in multiple novel view synthesis benchmarks, as well as multi-view stereo and video depth estimation.

An Analysis of Multi-View Geometric Diffusion for Novel View and Depth Synthesis

The paper "Zero-Shot Novel View and Depth Synthesis with Multi-View Geometric Diffusion" introduces a new approach to 3D scene reconstruction from sparse posed images using a diffusion-based architecture. The method, known as Multi-View Geometric Diffusion (MVGD), focuses on generating images and scale-consistent depth maps directly from novel viewpoints, bypassing the need for intermediate 3D representations such as voxel grids or Neural Radiance Fields (NeRF). This essay explores the methodologies, contributions, and implications of this paper within the context of computer vision and diffusion models.

Methodologies

The central premise of MVGD lies in its ability to perform direct pixel-level synthesis of images and depth maps from arbitrary input views. Unlike traditional methods that rely on constructing a parameterized 3D representation followed by volumetric rendering, MVGD leverages a diffusion model to implicitly learn and represent the 3D scene. This model operates in pixel space, facilitated by an efficient Transformer-based architecture known as Recurrent Interface Networks (RIN). The use of RIN allows the method to manage the complexity of attention operations effectively while maintaining scalability.

A novel aspect of MVGD is its Scene Scale Normalization (SSN) technique, which normalizes camera coordinates to ensure that generated depth maps are consistent with the scale defined by input view camera extrinsics. This approach addresses the challenge of ambiguous scale estimation across diverse datasets. Additionally, MVGD introduces learnable task embeddings to guide the diffusion process in multi-task scenarios, enabling the simultaneous generation of image and depth predictions.

Contributions

MVGD sets a new benchmark in several novel view synthesis and depth estimation benchmarks. Through a comprehensive dataset, comprising over 60 million multi-view samples from publicly available data, MVGD can generalize across various scenarios including indoor, outdoor, and object-centric scenes.

Moreover, the paper presents an effective incremental fine-tuning strategy, enabling the scaling of model complexity by expanding latent tokens without restarting training from scratch. This not only saves computational resources but also enhances model performance, leveraging prior knowledge embedded in smaller models. The model demonstrates promising results in generalizing to additional input views without retraining, a notable achievement given its training with only 2-5 conditioning views.

Implications and Future Directions

MVGD's ability to synthesize multi-view consistent predictions without explicit 3D representations presents a significant shift in paradigm for computer vision and novel view synthesis applications. By avoiding the construction of 3D models, it improves computational efficiency and flexibility in deployment across various environments and domains.

The implications of this research extend into potential applications in autonomous systems, robotics, and augmented reality, where real-time and accurate scene reconstruction is paramount. The methodology provides a framework for exploring further enhancements in learning geometric and appearance priors through diffusion models.

Looking forward, the model's implicit handling of dynamics and its ability to generalize across heterogeneous data suggest avenues for expansion into dynamic scene modeling. Future work could focus on integrating temporal embeddings to improve the handling of scenes with dynamic objects. Additionally, the exploration of larger, more complex models could further push the boundaries of performance in multi-view synthesis and depth estimation tasks.

In conclusion, the paper significantly contributes to the domain of zero-shot view synthesis with its novel diffusion-based method, MVGD. It not only redefines the approach to multi-view consistent generation but also sets a leading edge for future research in both theoretical development and practical applications within artificial intelligence and computer vision.