ZeroShape: Additional Qualitative Comparisons You Should Know About

Written by fewshot | Published 2025/01/03
Tech Story Tags: artificial-intelligence | zeroshape | what-is-zeroshape | zeroshape-details | how-does-zeroshape-work | omniobject3d | ortoc3d | pix3d

TLDRWe show additional qualitative results on OmniObject3D, Ocrtoc3D and Pix3D in Fig. 7, Fig. 8 and Fig. 9, respectively.via the TL;DR App

Table of Links

Abstract and 1 Introduction

2. Related Work

3. Method and 3.1. Architecture

3.2. Loss and 3.3. Implementation Details

4. Data Curation

4.1. Training Dataset

4.2. Evaluation Benchmark

5. Experiments and 5.1. Metrics

5.2. Baselines

5.3. Comparison to SOTA Methods

5.4. Qualitative Results and 5.5. Ablation Study

6. Limitations and Discussion

7. Conclusion and References

A. Additional Qualitative Comparison

B. Inference on AI-generated Images

C. Data Curation Details

A. Additional Qualitative Comparison

We show additional qualitative results on OmniObject3D, Ocrtoc3D and Pix3D in Fig. 7, Fig. 8 and Fig. 9, respectively. Comparing with prior arts, the reconstruction of ZeroShape better captures the global shape structure and visible geometric details.

This paper is available on arxiv under CC BY 4.0 DEED license.

Authors:

(1) Zixuan Huang, University of Illinois at Urbana-Champaign and both authors contributed equally to this work;

(2) Stefan Stojanov, Georgia Institute of Technology and both authors contributed equally to this work;

(3) Anh Thai, Georgia Institute of Technology;

(4) Varun Jampani, Stability AI;

(5) James M. Rehg, University of Illinois at Urbana-Champaign.


Written by fewshot | Spearheading research, publications, and advancements in few-shot learning, and redefining artificial intelligence.
Published by HackerNoon on 2025/01/03