Vista3D: Unravel the 3D Darkside of a Single Image (2409.12193v1)

Abstract: We embark on the age-old quest: unveiling the hidden dimensions of objects from mere glimpses of their visible parts. To address this, we present Vista3D, a framework that realizes swift and consistent 3D generation within a mere 5 minutes. At the heart of Vista3D lies a two-phase approach: the coarse phase and the fine phase. In the coarse phase, we rapidly generate initial geometry with Gaussian Splatting from a single image. In the fine phase, we extract a Signed Distance Function (SDF) directly from learned Gaussian Splatting, optimizing it with a differentiable isosurface representation. Furthermore, it elevates the quality of generation by using a disentangled representation with two independent implicit functions to capture both visible and obscured aspects of objects. Additionally, it harmonizes gradients from 2D diffusion prior with 3D-aware diffusion priors by angular diffusion prior composition. Through extensive evaluation, we demonstrate that Vista3D effectively sustains a balance between the consistency and diversity of the generated 3D objects. Demos and code will be available at https://github.com/florinshen/Vista3D.

• The paper introduces a dual-phase framework that reconstructs 3D models from a single image using Gaussian splatting for coarse shape estimation and SDF refinement for detail enhancement.
• The methodology optimizes geometry through a coarse-to-fine strategy that leverages Top-K densification and FlexiCubes to ensure structural and textural integrity.
• The framework achieves superior view diversity and reconstruction fidelity, as evidenced by improved CLIP-similarity metrics and efficient mesh generation benchmarks.

Vista3D: A Framework for Single Image 3D Reconstruction

The paper presents Vista3D, a sophisticated framework engineered to generate 3D models from single images. The dual-phase methodology delineated in this work addresses the intrinsic challenges associated with reconstructing unseen objects, endeavoring to maintain the structural integrity and textural consistency that previous endeavors often failed to achieve.

Vista3D employs a coarse-to-fine strategy in its operational framework. In the initial coarse phase, the framework utilizes Gaussian Splatting to rapidly construct foundational geometry from a given image, leveraging Top-K densification and additional regularization terms to enhance the efficiency of this stage. This phase critically optimizes the Gaussian parameters to tangible results in shape definition.

The subsequent fine phase intricately reforms the coarse geometry into a more refined structure using Sign Distance Fields (SDF) and FlexiCubes, a differentiable isosurface representation that offers superior fidelity. This step is pivotal in eliminating surface artifacts common with Gaussian splatting alone by employing dual network texture disentanglement. This approach distributes the learning task across separate hash encodings for visible and hidden object features, ensuring a nuanced texture synthesis.

An admirable element of Vista3D is the strategic angular diffusion prior composition, which integrates a second, distinct diffusion prior to enhance the diversity of unseen views. The meticulous implementation of score composition within this context ensures that image consistency is maintained without compromising the diversity introduced in novel views.

Quantitatively, Vista3D displays substantial improvements in CLIP-similarity and other metrics across various benchmarks, showcasing its competitive edge. The paper reports an impressive blend of speed and quality, achieving high-fidelity mesh generation in a streamlined timeframe—a marked advancement over alternatives like Magic123 and DreamGaussian in both computational efficiency and output precision.

Practical implications of Vista3D are vast, with potential applications ranging from 3D content generation for virtual environments to advanced object reconstruction in fields relying on limited visual data. The capability to extract and intelligently reconstruct unseen aspects promises utility in automation, augmented reality, and other domains where automated 3D model generation from limited imagery is advantageous.

Theoretically, Vista3D contributes to the broader discourse on 3D generative modelling by offering a robust model capable of employing multi-prior fusion and fine geometric control. Vista3D's approach could inspire further innovations in automated 3D reconstruction, particularly in enhancing texture detail and diversity without extensive multi-view datasets.

Future research may explore the scalability of the Vista3D framework to enhance applicability, with promise in generating a more expansive dataset repository to refine the framework's internal models for even broader 3D generation capabilities. The integration of more advanced AI paradigms and machine learning techniques could also be investigated to advance computational efficiency further and diversify the application of such models across varied technological domains.