BDC-Occ: Binarized Deep Convolution Unit For Binarized Occupancy Network
Zongkai Zhang, Zidong Xu, Wenming Yang, Qingmin Liao, Jing-Hao Xue
TL;DR
This paper introduces BDC-Occ, a binarized 3D occupancy network that uses a novel BDC unit to enhance performance and efficiency, enabling deployment on edge devices with limited resources.
Contribution
The paper proposes a new BDC unit with theoretical and practical innovations to improve binarized 3D occupancy networks, allowing deeper binarization with minimal error impact.
Findings
BDC-Occ achieves state-of-the-art performance among binarized 3D occupancy networks.
The BDC unit effectively increases the depth of binarized convolutional layers without significant error.
Experimental results demonstrate superior accuracy and efficiency of BDC-Occ.
Abstract
Existing 3D occupancy networks demand significant hardware resources, hindering the deployment of edge devices. Binarized Neural Networks (BNN) offer substantially reduced computational and memory requirements. However, their performance decreases notably compared to full-precision networks. Moreover, it is challenging to enhance the performance of binarized models by increasing the number of binarized convolutional layers, which limits their practicability for 3D occupancy prediction. To bridge these gaps, we propose a novel binarized deep convolution (BDC) unit that effectively enhances performance while increasing the number of binarized convolutional layers. Firstly, through theoretical analysis, we demonstrate that 1 \times 1 binarized convolutions introduce minimal binarization errors. Therefore, additional binarized convolutional layers are constrained to 1 \times 1 in the BDC…
Peer Reviews
Decision·ICLR 2025 Conference Withdrawn Submission
- It shows as main strength the theoretical insights that 1x1 binarized convolution is more robust to binarization and is thus used in to make the network deeper. Furthermore, it introduces an additional branch within the network to further refine the per-channel output of each layer based on this observation. - The results indicate an improvement over other binarization methods in terms of IoU and mAP. - Ablation studies are performed to show the impact of the various proposed changes.
- In terms comparing the FPS with FlashOCC, one of the main objectives of binarization is to provide FPS speedups, since the provided results are marginally improved at best it seems to make the proposed approach less necessary. Instead FPS should be compared with other binarization methods as well for fairness. - From the current writeup it is not clear how the proposed module can be plugged into other existing methods. - I am unconvinced that since in my understanding the backbone is left as
Originality: The paper demonstrates originality in its identification and solution for the performance degradation problem in binarized 3D occupancy networks. While BNNs have been explored in other domains, applying them effectively to 3D occupancy prediction, especially with a focus on maintaining performance with increasing network depth, is novel. The proposed BDC unit, with its 1x1 kernel constraint and the per-channel refinement branch, presents a creative combination of techniques tailored
1. **Limited exploration of binarization strategies**: The paper primarily focuses on binarizing convolutional layers using the BiSR-Conv method. Exploring alternative binarization techniques, such as XNOR-Net [1] or DoReFa-Net [2], and comparing their performance with BDC-Occ would strengthen the analysis and potentially reveal further insights. It's also important to investigate the impact of different activation functions specifically designed for BNNs, like those proposed in ReactNet [3].
1. This paper is the first study to apply binarization to the task of 3D occupancy prediction. The method of the authors significantly reduces the computational cost while maintaining the performance. 2. The proposed BDC unit significantly improves the performance of the network through theoretical analysis.
1. what is the contribution of BDC-V2 in Figure 3? It seems that it only increases the computational cost, with no performance improvement. Furthermore, MultiBiconv seems to be a multiple iteration of the technique from BDC-V1. 2. The paper seems to propose the design of a binarization methodology, not a design for Occupancy Prediction. While this is the first application of binarization to occupancy prediction, other binarization methodologies seem to be easily adaptable. 3. The design in Sect
Occupancy estimation plays a critical role in real-time applications like autonomous driving, where precise environment mapping is essential for safe and efficient vehicle navigation. However, the computational demands of occupancy estimation, especially when deployed on device-side platforms like Orin GPUs, create significant challenges in terms of efficiency and resource constraints. This research addresses these challenges by exploring strategies to minimize computational overhead, a vital ar
# Major Concern: While this work serves as an early exploration of binary quantization for the occupancy prediction task, several significant limitations are apparent. The study does present two main findings in the context of binary occupancy networks: - 1×1 binarized convolution introduces only minimal binarization errors as network depth increases. - Binarized convolution is notably less effective than full-precision convolution at capturing cross-channel feature importance. However, the pro
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAI in cancer detection
MethodsConvolution