Title: Real-Time Scene-Adaptive Tone Mapping for High-Dynamic Range Object Detection
Venue: NeurIPS 2025
Accepted: yes
Keywords: Tone Mapping

HDR RAW Detection

Real-time

Embedded System
Abstract: High dynamic range (HDR) images, with their rich tone and detail reproduction, hold significant potential to enhance computer vision systems, particularly in autonomous driving. However, most neural networks for embedded vision are trained on low dynamic range (LDR) inputs and suffer substantial performance degradation when handling high-bit-depth HDR images due to the challenges posed by extreme dynamic ranges.
In this paper, we propose a novel tone mapping method that not only bridges the gap between HDR RAW inputs and the LDR sRGB requirements of detection networks but also achieves end-to-end optimization with the downstream tasks.
Instead of relying on traditional image signal processing (ISP) pipeline, we introduce neural photometric calibration to regularize dynamic ranges and a scaling-invariant local tone mapping module to preserve image details.
In addition, our architecture also supports performance transfer finetuning, enabling efficient adaptation from the LDR model to the HDR RAW model with minimal cost. The proposed method outperforms traditional tone mapping algorithms and advanced AI-ISP methods in challenging automotive HDR scenes. Moreover, our pipeline achieves real-time processing of 4K high-bit-depth HDR inputs on the Nvidia Jetson platform.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper proposes a real-time scene-adaptive tone mapping method. Its core consists of a neural photometric calibration module for dynamically regularizing extreme dynamic ranges, and a scale-invariant local tone mapper that ensures linear response of mapping functions to input luminance scales, enhancing detail preservation and detection performance. Through end-to-end joint training optimization, this method improves the accuracy and generalization capability of HDR object detection.\n\nSTRENGTHS AND WEAKNESSES:\n- The paper  is clearly written and easy to comprehend.\n\n- The paper proposes an innovative neural photometric calibration module along with a scale-invariant tone mapping network, which effectively enhances detection performance.\n\n- In detection experiments under HDR scenarios, it also demonstrates outstanding performance, significantly surpassing existing state-of-the-art techniques.\n\nQUESTIONS:\n- If the dynamic range of HDR data is proportional to the square of the gradient magnitude, then why does training a detector based on HDR inputs lead to unstable gradient propagation, while training an HDR-to-LDR network does not exhibit such issues? Has there been any experimental analysis on the training stability with HDR inputs?\n\n- Neural Photometric Calibration is performed in a low-resolution image space, which means that the predicted weight map is patch-wise. Approximately how large is each patch? Would this patch-wise weight map affect the scale-invariance of subsequent processing?\n\n- The scale-invariant local tone mapping network proposed in the paper can effectively preserve the texture details of the image, while HDRNet is also a commonly used local tone mapping technique. The authors may consider comparing the differences between these two methods.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-19"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces a novel tone mapping framework that bridges the gap between HDR RAW inputs and LDR sRGB requirements for deeplearning alghrithoms, enabling end-to-end optimization with the object detection task.\nThe proposed approach replaces traditional ISP pipelines with neural photometric calibration and a scaling-invariant local tone mapping module to regulate dynamic ranges while preserving details.\nIt outperforms existing tone mapping and AI-ISP methods.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. The proposed framework jointly optimizes tone mapping and downstream detection tasks with improved performance over traditional ISP pipelines and existing improvement strategies.\n2. Fine-tuning from LDR to HDR models can be supported at a much lower computational cost, increasing the practicality for real-world applications. \n3. The efficiency of the proposed framework ensures high speed performance on embedded hardware suitable for autonomous driving applications.\n\nWeaknesses:\n1. Why is the learnable transformation function in Equation 4 designed in this particular form?\n2. There is some ambiguity in the model description regarding the use of \"Tone Mapper.\" From the contributions, Figure 2, and Section 3.3, it appears that the Tone Mapper is only one component of the proposed tone mapping framework, working alongside neural photometric calibration. However, in Sections 3.4 and 4.3 discussing model optimization, \"Tone Mapper\" seems to refer collectively to both components.\n3. In the experiments of Table 5, how exactly was the scaling-invariant property removed?\n4. In Table 1, different algorithms should be clearly labeled with their respective grouping characteristics to better highlight the focus of each algorithm category.\n\nQUESTIONS:\nSee Weaknesses.\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-01"}

comments: SUMMARY:
This work presents a method for HDR tonemapping as an input to detection networks. The authors present their method as an ISP-like approach that processes HDR captures fed into a pre-trained detection network. The method is evaluated on existing HDR datasets and using a prototype system that runs at real-time rates. The evaluation is focused on assessing various ISP and pre-processing methods that confirm the approach achieves consistent improvement over existing methods.

STRENGTHS AND WEAKNESSES:
Strengths:

- The pre-processing module (the ISP) is evaluated and compared to existing baseline methods in detail.
- The method is described in detail, and parameter settings are evaluated adequately.
- The approach is implemented on a realt-time platform.

Weaknesses:

- It is unclear if the evaluation is fair. My main concerns is here that the authors have not commented on whether and how they fine-tune the downstream network. As they already use detection losses, it remains unclear if the network has been finetuned here. 
- As such, baseline evaluations without any ISP (finetuned on RAW data) are missing. 
- The tonemapper is a convolutional network with scale invariant BN, which offers little technical novelty.

As such, overall, without extensive evaluations on fine-tuning I remain on the fence in this stage of the review process.

QUESTIONS:
Please comment on the finetuning of the detection network and missing RAW evaluations as discussed above.

LIMITATIONS:
Yes
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 2
DATE: 2025-07-01

comments: SUMMARY:
The paper proposes a tone mapping method that can be jointly trained end-to-end with neural object detection models. The method first applies a neural photometric calibration module to handle varying lighting conditions, followed by a scale-invariant tone mapping module. The entire pipeline is optimized jointly with the object detector to improve detection performance under diverse illumination.

STRENGTHS AND WEAKNESSES:
Strengths:
- The proposed method improves object detection performance when jointly trained with several commonly used detectors.
- The approach demonstrates low latency, making it practical for real-time deployment.

Weaknesses:
- Since the proposed tone mapping is specifically trained end-to-end with the detector, it is somewhat expected that detection performance would improve under this setup.
- It is unclear why this design choice is preferable, as joint end-to-end training sacrifices modularity and increases training pipeline complexity. This seems like a significant disadvantage that may outweigh the potential benefits of joint optimization, and the paper currently does not provide a clear justification for this trade-off.

QUESTIONS:
- Could the authors clarify why joint training of the tone mapper and object detector is preferable compared to using a modular tone mapping approach followed by detection?
- Are there scenarios where the added complexity of joint training is justified, and if so, could the authors provide practical examples or empirical evidence illustrating these benefits?

I am not an expert in this specific area, so I am open to reconsidering my rating if there are domain-specific factors I may have overlooked.

LIMITATIONS:
-
TITLE: Official Review
CLARITY: 2
IMPACT: 2
ORIGINALITY: 2
DATE: 2025-07-01