3D Reconstruction of Novel Object Shapes from Single Images (2006.07752v4)

Abstract: Accurately predicting the 3D shape of any arbitrary object in any pose from a single image is a key goal of computer vision research. This is challenging as it requires a model to learn a representation that can infer both the visible and occluded portions of any object using a limited training set. A training set that covers all possible object shapes is inherently infeasible. Such learning-based approaches are inherently vulnerable to overfitting, and successfully implementing them is a function of both the architecture design and the training approach. We present an extensive investigation of factors specific to architecture design, training, experiment design, and evaluation that influence reconstruction performance and measurement. We show that our proposed SDFNet achieves state-of-the-art performance on seen and unseen shapes relative to existing methods GenRe and OccNet. We provide the first large-scale evaluation of single image shape reconstruction to unseen objects. The source code, data and trained models can be found on https://github.com/rehg-lab/3DShapeGen.

• The paper introduces SDFNet, a novel dual-stage architecture that fuses a 2.5D sketch estimator with continuous signed distance functions to accurately reconstruct 3D shapes from single images.
• It demonstrates that employing a 3-DOF viewer-centered coordinate system and intermediary 2.5D representations significantly enhances reconstruction quality for unseen object categories.
• Extensive evaluations on large-scale datasets, including ShapeNet and ABC, confirm that SDFNet robustly generalizes across diverse rendering conditions and object orientations.

Evaluation and Analysis of 3D Reconstruction from Single Images

The paper "3D Reconstruction of Novel Object Shapes from Single Images" addresses a critical obstacle in computer vision: accurately reconstructing the 3D shape of arbitrary objects from single 2D images, an endeavor made challenging by the need to deal with both visible and occluded surfaces without relying on exhaustive datasets. This paper delineates a comprehensive investigation into the architectural and methodological factors that influence the performance of such models, culminating in the proposal of a novel architecture, SDFNet.

Architectural Advances with SDFNet

The SDFNet architecture introduces a combination of a 2.5D sketch estimator and a continuous signed distance function (SDF) for the shape representation. This dual-stage approach helps bridge the gap between capturing surface details through depth and normal estimations and predicting nuanced shape geometry via continuous implicit functions. Notably, SDFNet achieves state-of-the-art performance compared to previous methodologies like GenRe and OccNet for reconstructing both seen and unseen object categories.

Key Investigations and Findings

The paper explores four principal areas impacting reconstruction fidelity and generalization:

1. Coordinate System Representation: The authors examine the implications of using object-centered versus viewer-centered (with varying degrees of freedom) coordinate systems. The experimental results decidedly favor the use of 3-DOF viewer-centered representation, illustrating significant improvements in reconstructing unseen classes in arbitrary poses due to the increased variability in the training data.
2. Intermediary Representations via 2.5D: The inclusion of a 2.5D sketch as an intermediary step between the RGB input and the latent shape vector significantly boosts the model's robustness, enabling better generalization to novel shapes and lighting conditions, as evinced through comparative assessments with the input of direct RGB images.
3. Rendering Realism: The paper explores how variations in rendering, including lighting, reflectance, and background, affect model performance. Models trained under highly variable conditions, accounting for real-world image complexities, are shown to exhibit superior generalization capabilities, underscoring the requirement for diverse training data.
4. Extensive Dataset Evaluation: Uniquely, this paper extends evaluations across the entirety of ShapeNetCore.v2, training on 13 categories but rigorously testing across all available meshes, a scale and scope not previously undertaken. The inclusion of cross-dataset assessments with ShapeNet and ABC datasets reveals that SDFNet retains considerable generalization capability across these markedly different datasets.

Implications and Future Directions

The substantive contributions of this paper advance the state of 3D object reconstruction from single images significantly, with meaningful implications for both the underlying methodologies and practical applications. By proving the efficacy of certain architectural choices like continuous signed distance functions and 3-DOF viewer-centered representations, the paper sets a new benchmark for future research.

The large scale of the experiments not only fortifies the robustness of the conclusions but also opens avenues for additional scrutiny into generalization issues across even more diverse datasets. Future research could continue to refine intermediate representations and place greater emphasis on understanding and modeling occluded surfaces to enrich the semantic comprehension and applicability of reconstructed 3D shapes.

The exhaustive scope of this work, coupled with the promising results from SDFNet, undoubtedly lays the groundwork for more sophisticated and universally adaptable 3D reconstruction systems, marking a promising trajectory for further advancements in the field.