OmniObject3D: Large-Vocabulary 3D Object Dataset for Realistic Perception, Reconstruction and Generation (2301.07525v2)

Abstract: Recent advances in modeling 3D objects mostly rely on synthetic datasets due to the lack of large-scale realscanned 3D databases. To facilitate the development of 3D perception, reconstruction, and generation in the real world, we propose OmniObject3D, a large vocabulary 3D object dataset with massive high-quality real-scanned 3D objects. OmniObject3D has several appealing properties: 1) Large Vocabulary: It comprises 6,000 scanned objects in 190 daily categories, sharing common classes with popular 2D datasets (e.g., ImageNet and LVIS), benefiting the pursuit of generalizable 3D representations. 2) Rich Annotations: Each 3D object is captured with both 2D and 3D sensors, providing textured meshes, point clouds, multiview rendered images, and multiple real-captured videos. 3) Realistic Scans: The professional scanners support highquality object scans with precise shapes and realistic appearances. With the vast exploration space offered by OmniObject3D, we carefully set up four evaluation tracks: a) robust 3D perception, b) novel-view synthesis, c) neural surface reconstruction, and d) 3D object generation. Extensive studies are performed on these four benchmarks, revealing new observations, challenges, and opportunities for future research in realistic 3D vision.

• The paper presents a real-scanned 3D dataset with 6,000 objects across 190 categories, bridging synthetic and authentic data for enhanced model generalization.
• The methodology integrates detailed textured meshes, point clouds, multi-view images, and videos to support evaluations in 3D perception, novel view synthesis, and reconstruction.
• Experimental results highlight challenges in out-of-distribution generalization and sparse data reconstruction, urging development of more robust 3D vision algorithms.

Analyzing OmniObject3D: A 3D Object Dataset for Advanced Vision Tasks

The presented paper introduces OmniObject3D, a large-scale dataset designed to address challenges in 3D object perception, synthesis, reconstruction, and generation. The significance of OmniObject3D lies in its provision of real-scanned 3D objects, a critical advancement over synthetic datasets that often do not capture real-world subtleties. With its large vocabulary of 6,000 objects spanning 190 categories, the dataset aligns closely with popular 2D datasets like ImageNet and LVIS, facilitating the generation of generalizable 3D models that can seamlessly transition from synthetic to authentic settings.

OmniObject3D's comprehensive annotations, consisting of textured meshes, point clouds, multi-view rendered images, and real-captured videos, make it a versatile tool for numerous research applications. Unlike preceding datasets, OmniObject3D offers real-world fidelity, courtesy of professional scanning technologies that preserve intricate geometric and textural attributes.

The experimental validations exemplified through the four defined evaluation tracks—3D perception, novel-view synthesis, neural surface reconstruction, and 3D object generation—reveal critical insights. For 3D perception, the dataset provides unprecedented opportunities for addressing out-of-distribution (OOD) generalizations, challenging conventional model robustness against style and corruption differences. The results show that models exhibit varying degrees of robustness; however, there's an evident requirement for further exploration to tackle both OOD style and corruption robustly.

In the field of novel view synthesis, OmniObject3D facilitates evaluations across different methodologies, highlighting that voxel-based systems like Plenoxels offer superior performance for modeling high-frequency appearances but might fail in complex geometries, whereas more traditional methods like NeRF provide stability across varying conditions. The dataset's extensive and diverse shape repository propels advancements in scene-specific and generalized methodologies.

In neural surface reconstruction, the dense and sparse view settings underscore the dataset's potential to assess and develop methods like NeuS and SparseNeuS, which achieve varied success depending primarily on geometry detail and texture complexity. OmniObject3D extends a challenge to reconstructions from sparse views, emphasizing the need for approaches that can leverage limited visual input.

Finally, for 3D object generation, using OmniObject3D exposes critical issues of semantic distribution bias and exploration difficulties, especially with large vocabulary datasets. The dataset encourages a push toward models that can effectively balance generation diversity with quality while adequately handling complex textures and shapes.

The practical ramifications of OmniObject3D include its role as a benchmarking standard that advances 3D vision tasks closer to real-world applicability. Theoretically, the dataset serves as a robust foundation for developing more resilient and generalizable algorithms, shifting the focus from synthetic to realistic data paradigms in AI research. The unveiling of new research challenges through this dataset paves the way for strategic innovations in realistic 3D vision that can adapt to rapidly evolving technological landscapes.

Future research inspired by OmniObject3D may explore enhancements in model generalization from real-world data, addressing issues of semantic distribution bias and developing optimized frameworks for sparse data conditions. Overall, OmniObject3D lays a comprehensive groundwork for the next wave of advances in realistic 3D object understanding and modeling.