SlowGuess/NeurIPS_2023_OCR huggingface dataset:
The proposed ReTR framework not only enables accurate surface reconstruction through learnable rendering but also generates high-quality novel views. These capabilities open up possibilities for various downstream applications in fields such as virtual reality (VR), robotics, and view synthesis with geometry. While these applications offer numerous benefits, it is important to acknowledge that they also come with ethical considerations. As authors of the ReTR framework, we are committed to promoting ethical practices and responsible development. We recognize the potential for misuse, such as generating content without consent, and we prioritize fair representation and responsible usage of the technology. We strive to adhere to ethical guidelines and contribute to the development of responsible AI practices. It is crucial to ensure that technological advancements are leveraged for the betterment of society while minimizing potential negative impacts. By maintaining a focus on ethics, fairness, and responsible development, we aim to ensure that ReTR and its applications are aligned with the principles of responsible AI and contribute positively to the broader scientific community and society as a whole.
Figure 9: Comparison of VolRecon and ReTR in sparse view reconstruction with 3 input views.
View Synthesis
Depth Synthesis
Figure 10: View synthesis and depth synthesis visualization of our proposed ReTR.
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Figure 11: Visualization of full view generalization of a point cloud of our proposed ReTR.