MonoRec: Semi-Supervised Dense Reconstruction in Dynamic Environments from a Single Moving Camera (2011.11814v3)

Abstract: In this paper, we propose MonoRec, a semi-supervised monocular dense reconstruction architecture that predicts depth maps from a single moving camera in dynamic environments. MonoRec is based on a multi-view stereo setting which encodes the information of multiple consecutive images in a cost volume. To deal with dynamic objects in the scene, we introduce a MaskModule that predicts moving object masks by leveraging the photometric inconsistencies encoded in the cost volumes. Unlike other multi-view stereo methods, MonoRec is able to reconstruct both static and moving objects by leveraging the predicted masks. Furthermore, we present a novel multi-stage training scheme with a semi-supervised loss formulation that does not require LiDAR depth values. We carefully evaluate MonoRec on the KITTI dataset and show that it achieves state-of-the-art performance compared to both multi-view and single-view methods. With the model trained on KITTI, we further demonstrate that MonoRec is able to generalize well to both the Oxford RobotCar dataset and the more challenging TUM-Mono dataset recorded by a handheld camera. Code and related materials will be available at https://vision.in.tum.de/research/monorec.

• The paper proposes a semi-supervised method that achieves dense 3D reconstruction in dynamic environments from a single moving camera.
• It leverages both limited labeled data and abundant unlabeled data to effectively handle moving objects and scene variations.
• The approach demonstrates improved accuracy and efficiency versus traditional multi-view reconstruction techniques in challenging real-world settings.

Overview of "LaTeX Guidelines for Author Response"

The document titled "LaTeX Guidelines for Author Response" delineates the precise formatting and submission protocols to be adhered to when preparing an author response for the CVPR (Computer Vision and Pattern Recognition) conference. This document serves as a meticulous guide for authors who intend to address reviewers' comments following the initial review process of their submitted papers.

The primary function of the author response, as outlined, is to provide a structured opportunity for authors to address factual inaccuracies or to offer additional clarifications as specifically requested by reviewers. Notably, the document emphasizes that the response is not a venue for introducing novel contributions, such as new theorems, algorithms, or experimental results absent from the original submission. This guideline aligns with the 2018 motion passed by PAMI-TC, which explicitly advises reviewers against requesting additional experiments during the rebuttal phase. Similarly, authors are cautioned against including any new experimental data, with reviewers instructed to disregard such inclusions in their final recommendations.

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Implications of the Guidelines

The guidelines underscore the necessity of precision and clarity in the academic peer review process, particularly within the CVPR community. By enforcing strict standards for responses, the document seeks to maintain the integrity and focus of the review phase, allowing discussions to remain centered on the existing contributions of the paper. This unambiguous approach could contribute to more effective and efficient evaluation cycles, minimizing potential biases or discrepancies that could arise from incorporating new data post-submission.

Speculation on Future Developments

As the CVPR and broader academic conference landscapes continue to evolve, these guidelines may reflect a growing standardization trend in author and reviewer interactions. It is plausible that future iterations may incorporate enhanced digital tools to streamline formatting compliance checking or introduce dynamically adaptive templates to accommodate varying submission types. Ultimately, this could lead to a more accessible and equitable review process, beneficial to both seasoned researchers and newcomers to the domain.

In conclusion, the document "LaTeX Guidelines for Author Response" encapsulates a comprehensive framework designed to streamline the rebuttal process for CVPR authors, thereby reinforcing the collective aim of fostering rigorous and constructive academic discourse in computer vision research.