Summary
The paper introduces a novel framework, termed Multi-modal Zero-shot Offboard Panoptic Perception (ZOPP), specifically designed for autonomous driving applications. This innovative approach integrates zero-shot recognition capabilities with 3D representations generated from point cloud data, enhancing the model's ability to interpret complex driving environments without the need for extensive labeled datasets.
Strengths
1. The proposed work by the authors is significant and unique.
2. The proposed ZOPP approach is more capable of handling diverse types of data compared to any existing model, making it more robust.
Weaknesses
1. The manuscript is severely deficient in addressing the issues arising from labelling constraints and offers no substantive solutions within the proposed model to mitigate these problems.
2. The authors have egregiously failed to provide any details regarding the computational resources utilized, including the computational cost and the necessary hardware specifications, leaving the readers in the dark about the feasibility and scalability of the proposed approach.
3. The manuscript is conspicuously lacking in structure, offering an incoherent presentation of the proposed work, which undermines the clarity and comprehensibility of the research.
4. Despite the purported significance of the proposed work, the evaluation is lamentably limited, providing insufficient empirical evidence to substantiate the claimed advancements and benefits.
5. The manuscript is grossly inadequate in offering detailed information about the training and testing procedures, thereby failing to provide the essential methodological transparency required for replication and validation of the results.
Questions
1. The authors mention that data labeling is a crucial factor and a primary hurdle in training within existing work. Could the authors elaborate on how they propose to counter this issue or if they have developed any specific labeling mechanism to address this challenge?
2. How have the authors handled data from multi-view cameras? Specifically, how do they manage instances of the same object appearing in different views?
3. (With respect to Fig. 3), how does the proposed convolution filtering operation for removing background 3D points from foreground pixels account for varying disparity values at the upper edges of foreground objects? What specific kernel design is employed to optimize this process?
4. In the context of disparity occlusion handling around the upper edges of foreground objects (as seen in Fig. 3), what is the mathematical formulation for projecting 3D background points into the pixel regions of foreground objects? Additionally, how does the proposed convolution filtering algorithm differentiate between true foreground and occluded background points at a sub-pixel level?
5. What is the likelihood of the model's success in real-world applications? Have the authors conducted any real-world testing, and if so, what were the results?
Limitations
1. The abstract is grossly inadequate for the proposed topic.
2. The manuscript fails to deliver a coherent and sequential exposition of the proposed work.
3. The authors have entirely neglected to address the computational cost associated with the work or necessary for deployment.
4. The manuscript is deficient in presenting a comparative analysis with the visual results of existing works.