# Enhanced 3D Object Detection via Diverse Feature Representations of 4D Radar Tensor

Seung-Hyun Song, Dong-Hee Paek, Minh-Quan Dao, Ezio Malis, Seung-Hyun Kong

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## To cite this version:

Seung-Hyun Song, Dong-Hee Paek, Minh-Quan Dao, Ezio Malis, Seung-Hyun Kong. Enhanced 3D Object Detection via Diverse Feature Representations of 4D Radar Tensor. IEEE Sensors Journal, In press. hal-05461950

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Submitted on 16 Jan 2026

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# Enhanced 3D Object Detection via Diverse Feature Representations of 4D Radar Tensor

Seung-Hyun Song, Student Member, IEEE, Dong-Hee Paek, Member, IEEE, Minh-Quan Dao, Member, IEEE, Ezio Malis, Member, IEEE, and Seung-Hyun Kong, Senior Member, IEEE

**Abstract**— Recent advances in automotive four-dimensional (4D) Radar have enabled access to raw 4D Radar Tensor (4DRT), offering richer spatial and Doppler information than conventional point clouds. While most existing methods rely on heavily pre-processed, sparse Radar data, recent attempts to directly leverage raw 4DRT incur high computational costs and limited scalability. To address these limitations, we propose a novel three-dimensional (3D) object detection framework that explicitly addresses the representation-level variability inherent in 4D Radar while preserving efficiency. Rather than assuming a single optimal Radar representation, our method introduces a Radar-centric multi-teacher knowledge distillation (KD) framework, where multiple teacher models are trained on point clouds derived from diverse 4DRT pre-processing techniques, each capturing complementary signal characteristics. These

teacher representations are fused via a dedicated aggregation module and distilled into a lightweight student model that operates solely on sparse Radar inputs. Experimental results on the K-Radar dataset demonstrate that our framework achieves improvements of 7.3% in AP<sub>3D</sub> and 9.5% in AP<sub>BEV</sub> over the baseline RTNH model when using extremely sparse inputs. Furthermore, it attains comparable performance to denser-input baselines while significantly reducing the input data size by about 90 times, confirming the scalability and efficiency of our approach.

**Index Terms**— 4D Radar, 3D object detection, Knowledge distillation, Radar pre-processing.

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## I. INTRODUCTION

AUTONOMOUS driving and intelligent transportation systems (ITS) fundamentally rely on accurate perception and robust localization. Accurate perception enables the detection of surrounding vehicles, pedestrians, and vulnerable road users, while robust localization guarantees consistent positioning even under long-term dynamic environmental changes [1], [2]. In particular, robust performance under all weather conditions is essential for ensuring overall safety [3]–[5]. While

LiDAR has traditionally been the primary sensing modality due to its ability to accurately measure three-dimensional (3D) distances, it struggles to function reliably in adverse conditions such as snow, rain, and fog, owing to its reliance on light signals [6]. In contrast, Radar remain robust under such conditions by utilizing electromagnetic waves, and recent advances have led to the development of four-dimensional (4D) Radars that augment range, azimuth, and Doppler velocity with elevation information. This enhancement enables Radar perception in the full 3D space, positioning 4D Radar as a promising modality for robust 3D object detection [7]–[9].

The raw measurements of 4D Radar can take various forms—such as microwave signals, digital signals, range-Doppler maps (RDM), 4D Radar Tensor (4DRT), and point clouds—depending on the signal processing stage. Due to inherent Radar characteristics, such as low signal-to-noise ratio (SNR), multipath reflections, and phase noise, Radar signals undergo complex processing steps involving distinct assumptions and filtering techniques. As a consequence, the same environmental scene can yield significantly different data distributions. For instance, applying different pre-processing techniques (e.g., polar vs. Cartesian coordinate conversions

Manuscript received [Submission Date]; revised [Revision Date]; accepted [Acceptance Date], Date of publication [Publication Date]; date of current version 30 December 2025. This work was supported by the Ministry of Trade, Industry and Resources (MOTIR) grant funded by the Korea government (No. NO2250174). (Corresponding authors: Seung-Hyun Kong.)

Seung-Hyun Song is with the Graduate School of Advanced Security Science and Technology, Korea Advanced Institute of Science and Technology, Daejeon, Korea, 34051 (e-mail: shyun@kaist.ac.kr)

Dong-Hee Paek and Seung-Hyun Kong are with the CCS Graduate School of Mobility, Korea Advanced Institute of Science and Technology, Daejeon, Korea, 34051 (e-mail: donghee.paek@kaist.ac.kr; skong@kaist.ac.kr)

Minh-Quan Dao and Ezio Malis are with Centre Inria d'Univerité Côte d'Azur (e-mail: minh-quan.dao@inria.fr; ezio.malis@inria.fr)
<figure><img src="image_4.png" /><figcaption>Fig. 1. Comparison of 4D Radar point clouds based on different pre-processing techniques: (a) original output from 4DRT, (b) Fixed percentile filtering in the polar domain, (c) Fixed percentile filtering in the cartesian domain with interpolation, and (d) CA-CFAR filtering.</figcaption></figure>

or varying filtering logics and density thresholds) to identical 4DRT inputs results in markedly diverse point distributions and semantic characteristics. This inherent variability and inconsistency pose significant challenges in designing perception systems that robustly learn consistent and informative feature representations from 4D Radar data.

To overcome this, two main research directions have emerged: (1) designing architectures that more effectively extract semantic and contextual information from sparse Radar inputs [10]–[19]; (2) exploiting richer raw measurements such as 4DRT, aiming to capture latent features in the early signal domain [20]–[24].

However, existing approaches remain insufficient in addressing this fundamental challenge. Methods based on sparse point clouds typically rely on a single pre-processed representation that is heavily filtered and lossy, limiting robustness under varying pre-processing assumptions and noise conditions. Although knowledge distillation (KD) has been widely explored in perception tasks to transfer knowledge from rich feature representations, prior Radar studies have largely depended on auxiliary sensors (e.g., LiDAR or camera) as teachers to compensate for noisy Radar signals [17], [25], [26]. Such sensor-dependent approaches, however, overlook the distinctive challenge of 4D Radar—the large variability in data distributions caused by different pre-processing techniques—and therefore fail to fully exploit the rich information inherent in raw 4DRT.

Conversely, approaches that directly process raw 4DRT preserve richer signal information but incur prohibitive computational and memory costs for real-time or resource-constrained deployment. As such, neither sensor-dependent KD methods nor existing Radar-only techniques adequately resolve the intrinsic variability of Radar signals—leaving a critical gap in robust and scalable Radar-based perception.

To bridge this gap, we propose a 3D object detection framework that explicitly addresses the representation-level variability inherent in 4D Radar, while avoiding the prohibitive cost of directly processing raw 4DRT. Rather than assuming a single optimal Radar representation, we reformulate 4D Radar perception as a representation-variant learning problem, where different pre-processing techniques expose complementary but incomplete views of the same underlying 4DRT. In contrast to prior works that rely on auxiliary sensors or a fixed Radar representation, our approach is the first to adopt Radar-centric

KD, using multiple 4DRT pre-processing techniques as diverse teachers. Each teacher encodes distinct semantic and structural characteristics of Radar measurements. These complementary teacher representations are systematically fused through a dedicated aggregation module and distilled into a lightweight student model that operates solely on sparse Radar inputs. Through this design, the student learns to internalize rich and diverse 4DRT knowledge despite severe input sparsity, effectively mitigating representation-induced variability while maintaining computational efficiency. This enables robust and scalable Radar-based perception under practical deployment constraints.

To summarize, our key contributions are:

* To the best of our knowledge, we introduce the first Radar-centric multi-teacher KD framework that formulates 4D Radar perception as a representation-variant learning problem, leveraging diverse feature representations of the 4DRT obtained through multiple pre-processing techniques to enhance 3D object detection from sparse Radar inputs.

* By leveraging the proposed aggregation module—designed to fuse various 4DRT representations—the student model effectively distills rich features and achieves competitive 3D object detection using only sparse inputs with minimal computational cost.

* We conduct comprehensive experiments on the large-scale K-Radar dataset [9], demonstrating consistent performance improvements over RTNH baselines, including up to +7.3% in $AP_{3D}$ on sparse 99.9th percentile inputs, and showing state-of-the-art accuracy compared to existing 4D Radar detection models under sparse input settings.

## II. RELATED WORKS

### A. 4D Radar Pre-processing Techniques

Since the representational quality of 4D Radar point clouds is highly affected by pre-processing, various techniques have been actively investigated. Traditional 4D Radar pre-processing techniques are generally categorized into fixed thresholding and Constant False Alarm Rate (CFAR) techniques. Fixed thresholding selects points based on a global or percentile-based power threshold, while CFAR adaptively determines local thresholds using the statistical properties of surrounding cells.

RTNH [9] introduced a method that first transforms the 4DRT into Cartesian coordinates via interpolation, followed by percentile-based filtering. This approach preserves spatial consistency and enables the capture of meaningful low-power signals. Building on this, RTNH+ [24] proposed a CFAR-based two-level pre-processing (TLP) technique. It first applies a coarse Cell Average-CFAR (CA-CFAR) with a high threshold to retain as many relevant measurements as possible. Then, a second stage applies a range-wise percentile filter with a lower threshold along the azimuth axis to remove spurious points and enhance data reliability.

In this work, we aim to extract diverse feature representations from 4DRT using multiple pre-processing techniques and leverage them to improve 3D object detection performance.
<figure><img src="image_5.png" /><figcaption>Fig. 2. **4DR-MR**: Overall architecture of the proposed 3D object detection framework utilizing multi-representations of 4D Radar. Features from multiple teacher models, each using different Radar pre-processing, are fused and distilled into a compact student model for efficient inference.</figcaption></figure>

## B. 3D Object Detection Architectures with 4D Radar

Early studies on 3D object detection with 4D Radar have largely followed the development of LiDAR-based point cloud networks, extending them to effectively capture semantic and contextual information from sparse Radar inputs.

RPFA-Net [10] extends the PointPillars [11] framework by incorporating self-attention [27] to strengthen intra-pillar feature modeling. Radar PillarNet [28] generates Radar pseudo images using a pillar-based encoding scheme, processing spatial, velocity, and intensity features separately for more effective BEV learning. RTNH [9] explores how 4D Radar can be leveraged to effectively encode elevation information, introducing a sparse 3D convolutional backbone that integrates height-related features for improved 3D object detection. Building on this design, RTNH+ [24] further enhances the representation of elevation cues. Several works enhance Radar perception by integrating complementary features or views to mitigate the inherent sparsity of Radar point clouds. RadarMFNet [29] aggregates Radar frames across time, using Doppler velocity for motion compensation to produce denser input representations. SMURF [12] enriches pillar features with density-based representations derived from kernel density estimation. MVFAN [13] exploits a multi-branch architecture to combine bird's eye view (BEV) and cylindrical representations. RadarPillarDet [30] employs a multi-pillar feature fusion scheme with a dual stream encoder and self-attention to enrich sparse Radar features and enhance BEV representations. MUFASA [18] integrates spatially aware multi-view fusion through local geometry extraction (GeoSPA) and distributed attention (DEMVA). DADAN [14] introduces a dynamic-augmented and density-aware network that adaptively enhances sparse Radar representations by considering local density variations and leveraging dynamic augmentation strategies, achieving more accurate 3D object detection from 4D Radar inputs. MSPFNet [19] combines the enhancement of Radar point cloud with a multiscale feature fusion module,

generating virtual points and capturing cross-scale relations to address sparsity and noise. More recently, KD has been applied to enhance Radar perception by transferring information from auxiliary sensors. RadarDistill [17] uses LiDAR features as teachers for cross-modality alignment and feature distillation, while CRKD [25] extends this to camera-Radar settings with BEV-based distillation. SCKD [26] further introduces a semi-supervised framework where LiDAR-Radar fused teachers guide a Radar-only student, yielding significant gains under sparse inputs.

In parallel, several studies have shifted toward directly processing early-stage Radar signals. The early work FFT-RadNet [20] performs object detection directly on the range-Doppler map (RDM). As RDMs lack explicit azimuth information, FFT-RadNet implicitly estimates azimuth within its latent feature space by learning an axis that encodes azimuthal variation. ADCNet [21] and T-FFTRadNet [22] further extend this direction by processing raw ADC signals. Leveraging learnable versions of the discrete Fourier transform, they introduce neural signal processing modules that transform the ADC signal into a range-azimuth-Doppler latent representation. More recently, DPFT [23] takes the 4DRT as input and projects it onto range-azimuth and azimuth-elevation planes for feature extraction. The resulting feature maps are then fused with camera data using deformable attention [31].

In summary, point cloud-based approaches remain limited by the inherent sparsity and noise of Radar data, while raw signal-level methods often face challenges in maintaining semantic richness and computational efficiency. These limitations highlight the need for continued exploration of architectures that can better balance efficiency and semantic understanding in 4D Radar perception.

## III. PROPOSED APPROACH

We propose a 3D object detection framework called 4D Radar Multi-Representation (4DR-MR). This section first
<figure><img src="image_6.png" /><figcaption>Fig. 3. Aggregation module: The module consists of a Representation Alignment Block that refines each teacher feature and an Attention-based Fusion Block that adaptively integrates the aligned features.</figcaption></figure>

presents an overview of the main components and data flow of our framework, followed by a detailed description of each module.

## A. Overview of 4DR-MR

The 4DR-MR framework adopts a multi-teacher KD, in which multiple teacher models and a single student model are jointly trained to transfer knowledge across diverse Radar representations (see Fig. 2).

Each teacher model receives its own Radar point cloud input and extracts BEV features $F^{T_i}$ using a non-shared RTNH backbone [9]. The resulting features from all teachers are passed into the Aggregation Module, which fuses them into a unified representation $F^T$. This aggregated teacher feature $F^T$ supervises the student model, which shares the same backbone architecture but operates on a separate sparse Radar input. The student feature $F^S$, refined by the Densify Module, is then compared to the fused teacher features for distillation.

In this work, both the backbone and the detection head adopt the same structure as RTNH [9], and are trained jointly with the student backbone during our training process, without any pretraining.

The entire framework is trained with object detection loss $L_{detect}$ and feature-level distillation loss $L_{distill}$, where $L_{distill}$ is calculated using ground truth-masked features to focus learning on object regions. At inference time, only the student model is used, enabling efficient 3D object detection without the overhead of teacher models or the aggregation module.

## B. Radar Pre-processing for 4DR-MR

We implement and analyze three pre-processing techniques to exploit the rich information embedded in the 4DRT. In addition to the type of processing applied, we also consider the impact of point cloud density by adjusting percentile thresholds or filtering criteria within each strategy.

1) Fixed Percentile in the Polar Domain: This method extracts Radar point clouds by directly applying percentile-based thresholding to the power tensor computed from the 4DRT. Specifically, the Doppler axis of the 4DRT is first collapsed by averaging to obtain a 3D power volume. A global power threshold is then set using the r-th percentile value across all power values. Each discrete [azimuth, range, elevation] coordinate is transformed to Cartesian coordinates, and points exceeding the threshold are retained. As illustrated in Algorithm 1, this approach preserves the Radar's native polar geometry and returns sparse but strong detections with minimal computational cost. However, due to the lack of

spatial regularity, the resulting point cloud is less compatible with voxel-based convolutional architectures.

<table><caption>Algorithm 1: Fixed percentile-based pre-processing</caption><thead><tr><th></th><th></th></tr></thead><tbody><tr><td>Input :</td><td>4DRT ∈ ℝ<sup>4</sup> (azimuth, range, elevation, Doppler),<br>percentile r ∈ ℝ</td></tr><tr><td>Output:</td><td>Radar point cloud<br>P = {p<sub>i</sub> = [x, y, z, power]}<br><br>/* Power Mapping */</td></tr><tr><td>1</td><td>power = mean(4DRT, dim=Doppler)</td></tr><tr><td>2</td><td>power_threshold = r percentile of power<br><br>/* Thresholding and Coordinate Transformation */</td></tr><tr><td>3</td><td>P = ∅</td></tr><tr><td>4</td><td>for each discrete coordinate [azimuth, range, elevation] of power<br>do<br><br>5 &nbsp;&nbsp;&nbsp;&nbsp;pw = power[azimuth, range, elevation]<br>6 &nbsp;&nbsp;&nbsp;&nbsp;azi, rg, ele = discrete_to continuous([azimuth,<br>range, elevation])<br>7 &nbsp;&nbsp;&nbsp;&nbsp;if pw ≥ power_threshold then<br>8 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;x = rg · cos(ele) · cos(azi)<br>9 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;y = rg · cos(ele) · sin(azi)<br>10 &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;z = eg · sin(ele)<br>11 &nbsp;&nbsp;&nbsp;&nbsp;add p = [x, y, z, power] to P<br>12 end<br>13 end</td></tr></tbody></table>

2) Fixed Percentile in the Cartesian Domain with Interpolation: This method follows the pre-processing technique proposed in RTNH [9]. The 4DRT is first interpolated into a regular Cartesian voxel grid using bilinear interpolation, transforming the data from its native polar coordinate system. Subsequently, percentile-based filtering is applied in the voxel space, using the same settings as RTNH [9]. This approach aligns well with voxel-based 3D CNNs and BEV detectors by ensuring spatial regularity. Although the interpolation step incurs additional computational overhead, it enables structural continuity and uniform resolution, making it particularly suitable for dense perception pipelines.

3) CA-CFAR in the Polar Domain: This method applies the CA-CFAR algorithm to the 4DRT in its native polar coordinate system. It retains points whose power values exceed a locally computed threshold based on surrounding cells, effectively suppressing background noise and emphasizing strong reflections, particularly along object boundaries. While this approach improves precision by reducing clutter, it often
removes low-power returns within object interiors, leading to sparser and less complete point clouds that are suboptimal for learning-based 3D detection models.

### C. Aggregation Module

To effectively transfer the various representation knowledge of 4DRT from multiple teacher models to a student model, we design a novel feature aggregation mechanism composed of two key components: (1) a Representation Alignment Block for teacher-specific feature transformation, and (2) an Attention-based Fusion Block for adaptive integration of multi-teacher features.

1) **Representation Alignment Block:** Each teacher model provides distinct feature representations that may differ in terms of scale, semantic content, and distribution. To address this heterogeneity, we introduce a Representation Alignment Block that independently processes the feature of each teacher $F^{T_i}$. As illustrated in Fig.3 (left), the Representation Alignment Block consists of three stages: an initial 1x1 convolution with batch normalization and ReLU activation to compress the feature channels, a ConvNeXt [32] inspired block to enhance channel-wise interactions and nonlinear representation, and a final projection to align the output to a common dimensionality C/4. This sequential transformation harmonizes the characteristics of the teacher features, enabling a more effective distillation of knowledge into the student model. Formally, for a teacher feature $F^{T} \in \mathbb{R}^{C \times H \times W}$, the aligned feature $F^{T_i}_{\text{Align}} \in \mathbb{R}^{C/4 \times H \times W}$ is obtained by:

$$F^{T_i}_{\text{Align}} = \text{RepresentationAlignmentBlock}(F^{T_i})$$

This alignment ensures that teacher features, though diverse in origin, are semantically harmonized and ready for fusion.

2) **Attention-based Fusion Block:** The aligned teacher features $F^{T_i}_{\text{Align}}$ are concatenated along the channel dimension and passed through the Attention-based Fusion Block. As shown in Fig.3 (right), this block first applies a Convolutional Block Attention Module (CBAM) to the concatenated features, selectively enhancing informative channels and spatial regions. Subsequently, a 1x1 convolution is employed to integrate the features into a final aggregated representation. The overall fusion operation can be formulated as:

$$F^T = \text{Conv}_{1 \times 1}(\text{CBAM}(\text{Concat}(F^{T_i}_{\text{Align}}))), i \in \{1, 2, \dots, N\}$$

This fusion strategy adaptively highlights the most informative aspects of multi-teacher knowledge, improving the efficiency and effectiveness of student learning.

### D. Densify Module

In our framework, features extracted from the teacher and student backbones exhibit a density mismatch due to differences in their input point clouds. While the teacher models, benefiting from occupancy leaking and denser inputs, generate relatively dense features, the student model—operating on extremely sparse inputs—produces under-represented BEV features even after several convolutional stages. This discrepancy poses a challenge for feature-level KD, as it leads to mismatched feature semantics and spatial structures.

To mitigate this issue, we extend the Densify Module originally proposed in [33], incorporating a dual-pass encoder–decoder refinement scheme for enhanced feature reconstruction. In our implementation, the student BEV feature first passes through an encoder–decoder block augmented with ConvNeXt-style residual layers. The output is then refined again via a second pass of the same encoder–decoder structure, effectively deepening the feature densification process. Finally, we fuse the densified features with the original sparse BEV feature using a residual connection, where separate fusion layers are applied to each.

The resulting feature is further processed by a sequence of convolutional layers to produce the final student BEV representation $F^S$, which is used in the distillation loss against the aggregated teacher feature.

### E. Loss Function

The loss function $\mathcal{L}_{total}$ used to train the student model is a weighted sum of object detection loss $\mathcal{L}_{detect}$ and KD loss $\mathcal{L}_{distill}$

$$\mathcal{L}_{total} = \mathcal{L}_{detect} + \alpha \cdot \mathcal{L}_{distill} \quad (1)$$

Here, $\alpha$ is a hyperparameter controlling the balance between the object detection loss and the KD loss, and is set to 1 in our experiments.

$\mathcal{L}_{distill}$ is the Mean Squared Error (MSE) between the aggregation of the teachers' feature $F^T$ and the student's densified feature $F^S$. Since features in regions where objects exist are more important for detecting objects than background, we mask both $F^T$ and $F^S$ using ground truth when calculating the distillation loss. Specifically, we transform each ground truth object into a 2D Gaussian and splat it to the BEV to obtain a heatmap. This heatmap is multiplied element-wise with $F^T$ and $F^S$ to realize the masking operation.

$$\mathcal{L}_{distill} = \text{MSE}(\text{mask}(F^T), \text{mask}(F^S)) \quad (2)$$

We use the loss function defined by RTNH [9] as the detection loss $\mathcal{L}_{detect}$.

## IV. EXPERIMENTS

### A. Experimental Setup

1) **Implementation Details:** Due to the limitation of our hardware, we set the number of teachers $N$ to 3. To ensure that each teacher learns knowledge from a distinct representation, we adopt the following configurations for the Radar pre-processing techniques: (1) For the fixed percentile method, we use two thresholds: 80th and 90th percentile to generate different levels of sparsity; (2) For the fixed percentile with interpolation method, we follow the same setting as used in RTNH [9]; (3) For the CA-CFAR method, we adopt the configuration used in the TLP algorithm from RTNH+ [24].

We use the same RTNH-based backbone for both teacher and student models. Implementation details of the Aggregation Module and the Densify Module, including layer configurations and attention operations, are provided in Appendix I.
<table><caption>TABLE I
COMPARISON WITH RTNH BASELINES USING VARIOUS PRE-PROCESSING TECHNIQUES. **BOLD** INDICATES THE PROPOSED METHOD.</caption><thead><tr><th rowspan="2">Pre-processing</th><th rowspan="2"></th><th rowspan="2">Data [MB] /Frame</th><th rowspan="2">Network</th><th colspan="2">Sedan</th><th colspan="2">Bus or Truck</th></tr><tr><th>AP<sub>3D</sub></th><th>AP<sub>BEV</sub></th><th>AP<sub>3D</sub></th><th>AP<sub>BEV</sub></th></tr></thead><tbody><tr><td rowspan="4">(1) Fixed Percentile</td><td>99.9</td><td>0.1</td><td>4DR-MR</td><td><b>44.16</b></td><td><b>52.59</b></td><td><b>25.62</b></td><td><b>29.02</b></td></tr><tr><td>95</td><td>5</td><td>RTNH<sub>99.9</sub></td><td>36.84</td><td>43.10</td><td>19.55</td><td>25.60</td></tr><tr><td>90</td><td>9</td><td>RTNH<sub>95</sub></td><td>44.08</td><td>48.08</td><td>30.80</td><td>43.51</td></tr><tr><td>80</td><td>18</td><td>RTNH<sub>90</sub></td><td>43.86</td><td>53.36</td><td>27.62</td><td>36.80</td></tr><tr><td colspan="2">(2) Fixed Percentile with Interpolation</td><td>5</td><td>RTNH</td><td>47.08</td><td>55.23</td><td>33.11</td><td>37.91</td></tr><tr><td colspan="2">(3) CA-CFAR</td><td>1</td><td>RTNH<sub>cfar</sub></td><td>50.71</td><td>56.47</td><td>33.95</td><td>43.80</td></tr></tbody></table>

Both the teacher and student models are trained under the same settings as the baseline RTNH [7]. The training epoch, learning rate, and batch size are set to 30, 0.001, and 8, respectively. All experiments are conducted on an NVIDIA RTX 3090 GPU.

2) *Datasets and Evaluation Metrics:* We conduct our experiments on the K-Radar dataset [9], a multi-modal large-scale dataset collected under diverse driving environments, including adverse weather conditions. It provides access to dense 4DRT, which is crucial for our approach. The dataset contains 35,000 frames, evenly split into training and testing sets, and includes 100,000 annotated 3D bounding boxes across five object categories: Sedan, Bus or Truck, Pedestrian, Motorcycle, and Bicycle. Since the goal of this work is to validate the effectiveness of the proposed framework under robust conditions, we center our analysis on the dominant classes (i.e., Sedan and Bus or Truck) that provide sufficient samples for consistent and statistically meaningful evaluation, while the remaining classes are too underrepresented to allow reliable comparison. For the region of interest (RoI), we use [0, 72] m in the x-axis, [−16, 16] m in the y-axis, and [−2, 7.6] m in the z-axis. Following the K-Radar benchmark protocol, we evaluate Average Precision (AP) at a 0.3 IoU threshold. Inference speed is reported in frames per second (FPS) to assess the runtime efficiency of our framework.

## B. Impact of Proposed Approach

To demonstrate the effectiveness of the proposed 4DR-MR framework, we conduct a series of experiments focusing on three key aspects: (1) validating its ability to enhance detection performance under extremely sparse input conditions, (2) evaluating the effectiveness of incorporating diverse 4DRT representations through multi-teacher KD, and (3) verifying its overall competitiveness compared to existing 4D Radar-based object detection methods.

1) *Comparison with RTNH Baselines using Various Pre-processing Techniques:* To support a clearer understanding of our framework's design and the role of diverse data representations, we compare the performance of the networks employed as student and teacher models in our 4DR-MR framework. As shown in Table I, the teacher models are based on RTNH architectures trained on point clouds generated from different 4D Radar pre-processing techniques, including fixed percentile filtering (with thresholds of 90 and 80), interpolation-based

<table><caption>TABLE II
STUDENT PERFORMANCE COMPARISON ON THE SEDAN CLASS USING SINGLE VS. MULTI-TEACHER KD. MULTIPLE DIVERSE 4DRT-BASED TEACHERS ENHANCE REPRESENTATION LEARNING. **BOLD** INDICATES THE BEST RESULT IN EACH METRIC.</caption><thead><tr><th>Approach</th><th>Teacher</th><th>AP<sub>3D</sub></th><th>AP<sub>BEV</sub></th></tr></thead><tbody><tr><td>Baseline</td><td>N/A</td><td>36.84</td><td>43.10</td></tr><tr><td rowspan="4">Single-teacher</td><td>(1) RTNH<sub>80</sub></td><td><b>44.52</b></td><td>47.79</td></tr><tr><td>(1-1) RTNH<sub>90</sub></td><td>40.51</td><td>48.92</td></tr><tr><td>(2) RTNH</td><td>44.48</td><td>47.52</td></tr><tr><td>(3) RTNH<sub>cfar</sub></td><td>44.04</td><td>47.22</td></tr><tr><td rowspan="2">Multi-teacher</td><td>(1), (2), (3)</td><td>44.34</td><td>47.21</td></tr><tr><td>(1), (1-1), (2)</td><td>44.16</td><td><b>52.59</b></td></tr></tbody></table>

percentile filtering, and CA-CFAR. The student model, in contrast, is trained on point clouds generated using only the 99.9th percentile setting.

Despite sharing the same sparse input as RTNH<sub>99.9</sub>, the proposed 4DR-MR significantly improves detection performance—achieving gains of +7.3 AP<sub>3D</sub> / +9.5 AP<sub>BEV</sub> for Sedan and +6.1 AP<sub>3D</sub> / +3.4 AP<sub>BEV</sub> for Bus or Truck. This highlights the effectiveness of multi-representation distillation under extremely sparse Radar inputs. Furthermore, compared to RTNH<sub>90</sub>, which shows comparable detection performance, the proposed 4DR-MR reduces the per-frame input size by approximately 90 times, highlighting its advantage in terms of both lightweight efficiency and detection accuracy.

2) *Effectiveness of Teacher Diversity:* To demonstrate the advantage of leveraging diverse 4DRT representations, we compare student performance under single-teacher and multi-teacher KD. As shown in Table II, single-teacher distillation provides notable improvements over the baseline, confirming the effectiveness of KD when transferring knowledge from a single pre-processed representation. For instance, (1) RTNH<sub>80</sub> achieves 44.52 AP<sub>3D</sub>, while (1-1) RTNH<sub>90</sub> attains the highest single-teacher BEV accuracy of 48.92 AP<sub>BEV</sub>.

More importantly, multi-teacher KD further enhances performance by integrating complementary information from multiple pre-processing techniques. The combination of (1), (1-1), and (2) achieves 52.59 AP<sub>BEV</sub>, surpassing all single-teacher cases and demonstrating that the student benefits from the richer and more diverse cues distilled from multiple teachers. This confirms that multi-teacher KD is able to more effectively exploit the inherent richness of 4DRT compared to
<figure><img src="image_7.png" /><figcaption>Fig. 4. Qualitative comparison of 4D Radar-based 3D object detection results. Black points denote LiDAR point clouds, colored points denote Radar point clouds; black boxes indicate ground truth, and red boxes indicate predictions.</figcaption></figure>

<table><caption>TABLE III
COMPARISON WITH OTHER 4D RADAR-BASED DETECTION NETWORKS ON THE SEDAN CLASS. **BOLD** INDICATES THE BEST RESULT IN EACH METRIC.</caption><thead><tr><th>Network</th><th>AP<sub>3D</sub></th><th>AP<sub>BEV</sub></th></tr></thead><tbody><tr><td>RTNH [9]</td><td>36.84</td><td>43.10</td></tr><tr><td>RTNH+ [24]</td><td>37.36</td><td>45.79</td></tr><tr><td>Radar PillarNet [28]</td><td>37.58</td><td>46.02</td></tr><tr><td>RPFA [10]</td><td>37.49</td><td>40.00</td></tr><tr><td>SMURF [12]</td><td>37.57</td><td>40.01</td></tr><tr><td>DADAN [14]</td><td>37.62</td><td>45.59</td></tr><tr><td>MVFAN [13]</td><td>43.91</td><td>47.34</td></tr><tr><td><strong>4DR-MR</strong></td><td><strong>44.16</strong></td><td><strong>52.59</strong></td></tr></tbody></table>

relying on any single representation.

It is worth noting that not all teacher combinations yield equal gains. For example, (1), (2), and (3) performs worse than (1), (1-1), (2), likely because the CA-CFAR emphasizes strong edge reflections while providing less dense object-interior information, which may limit its complementarity. These results suggest that while diversity is essential, teacher compatibility also plays a crucial role in achieving optimal distillation performance.

3) *Comparison with Other 4D Radar-based Detection Networks:* To evaluate the general effectiveness of the proposed 4DR-MR framework, we compare it against existing 4D

Radar-based 3D object detection networks. For a fair comparison, all methods in Table III (RTNH, RTNH+, Radar PillarNet, RPFA, SMURF, DADAN, and MVFAN) were tested using the same input data—sparse point clouds filtered at the 99.9 percentile threshold—identical to the inputs of the 4DR-MR student network. As shown in Table III, 4DR-MR consistently outperforms prior methods, achieving superior performance over both the RTNH baseline and other state-of-the-art approaches.

In addition to the quantitative evaluation, Fig. 4 presents qualitative comparisons of detection results across diverse and challenging driving scenarios. Existing methods often suffer from missed detections or unstable bounding box predictions under sparse and noisy Radar measurements. In contrast, the proposed 4DR-MR consistently produces more accurate and stable 3D bounding boxes, particularly for distant and partially observed vehicles. These qualitative results further demonstrate the robustness of 4DR-MR to severe sparsity and environmental degradation, complementing the improvements observed in quantitative metrics.

### C. Ablation Study

1) *Aggregation Module:* We compare our two-stage Aggregation Module against representative single-stage fusion strategies to validate its effectiveness.

The first baseline, Attention-based Fusion, concatenates the features from all teacher models after projecting them into
<caption>TABLE IV</caption>

COMPARISON OF DIFFERENT FUSION STRATEGIES FOR INTEGRATING
MULTI-TEACHER FEATURES ON THE SEDAN CLASS. **BOLD** INDICATES
THE PROPOSED METHOD.

<table><thead><tr><th>Fusion Method</th><th>AP<sub>3D</sub></th><th>AP<sub>BEV</sub></th></tr></thead><tbody><tr><td>Attention-based Fusion</td><td>43.78</td><td>46.80</td></tr><tr><td>Transformer-style Fusion</td><td>44.08</td><td>47.60</td></tr><tr><td><strong>Aggregation module</strong></td><td><strong>44.16</strong></td><td><strong>52.59</strong></td></tr></tbody></table>

a common space, and then applies a CBAM to highlight salient responses. This design directly fuses teacher features in one step, but does not provide an explicit mechanism to align representation spaces across teachers. The second baseline, Transformer-style Fusion, adopts a query-based attention mechanism. Teacher features are patchified and embedded, and a set of learnable queries attends to these features via a multi-head attention module. This formulation performs aggregation and fusion jointly within a single stage, enabling richer interactions between teachers but still coupling alignment and fusion into a single process. Without explicitly separating these roles, it is difficult to balance teacher-specific alignment with multi-teacher feature selection.

As shown in Table IV, both single-stage approaches produce suboptimal results, even falling below the performance of single-teacher distillation. In contrast, by explicitly decoupling representation alignment from attention-based fusion, our proposed two-stage Aggregation Module achieves the best performance, demonstrating a more balanced and effective integration of diverse 4DRT-derived features.

2) *Analysis of Qualitative Results:* To further validate the effectiveness of the proposed 4DR-MR framework, we provide qualitative analyses at the feature and scene levels.

At the feature level, Fig. 5 compares BEV feature maps from the baseline RTNH, the teacher models, and the 4DR-MR student model. RTNH<sub>99.9</sub>, trained on extremely sparse Radar inputs, fails to clearly highlight object-relevant regions. In contrast, teacher models trained on richer 4DRT representations generate more salient and diverse features, each capturing complementary aspects of the scene. Guided by this diversity, the 4DR-MR student model—despite using the same sparse input as RTNH<sub>99.9</sub>—produces more informative and semantically consistent feature maps. This demonstrates that multi-teacher KD effectively exploits latent 4DRT information, enabling robust feature learning under highly sparse input conditions.

By conducting a comparative evaluation across diverse driving scenarios (e.g., weather conditions, road environments, lighting), we demonstrate that the proposed framework leverages the characteristics of 4D Radar for more robust object detection under various driving conditions. As shown in Fig.6, (a), (c), and (d), conventional RTNH fails to extract sufficient feature information from sparse data, leading to a failure in detecting objects in inference results. On the other hand, 4DR-MR utilizes knowledge transferred from multiple teachers to extract object feature information from sparse data, being able to detect objects at a higher precision. In Fig.6, (b), (e), and (f), it can be seen that RTNH produces more false positives

<figure><img src="image_8.png" /><figcaption>Fig. 5. Qualitative comparison of BEV feature maps and detection results. (a) baseline RTNH<sub>99.9</sub>; (b) teacher models trained on (1) RTNH<sub>80</sub>, (2) RTNH with interpolation, and (3) RTNH<sub>cfar</sub>; (c) 4DR-MR student model.</figcaption></figure>

<caption>TABLE V</caption>

COMPARISON OF THE PROPOSED 4DR-MR AND THE RTNH BASELINE
ACROSS DIFFERENT INPUT REPRESENTATIONS ON THE SEDAN CLASS.
**BOLD** INDICATES THE BEST RESULT IN EACH METRIC.

<table><thead><tr><th rowspan="2">Network Data</th><th colspan="2">RTNH</th><th colspan="2">4DR-MR</th></tr><tr><th>AP<sub>3D</sub></th><th>AP<sub>BEV</sub></th><th>AP<sub>3D</sub></th><th>AP<sub>BEV</sub></th></tr></thead><tbody><tr><td>Fixed Percentile<sub>99.9</sub></td><td>36.84</td><td>43.10</td><td><strong>44.16</strong></td><td><strong>52.59</strong></td></tr><tr><td>Fixed Percentile<sub>95</sub></td><td>44.08</td><td>48.08</td><td><strong>46.63</strong></td><td><strong>55.28</strong></td></tr><tr><td>CA-CFAR</td><td><strong>46.3</strong></td><td>51.9</td><td>43.99</td><td><strong>52.82</strong></td></tr></tbody></table>

than 4DR-MR because it fails to distinguish 4D Radar noise from measurements corresponding to objects. Overall, these qualitative results confirm that 4DR-MR effectively leverages multi-teacher knowledge to overcome sparse Radar limitations, producing more precise detections and robust performance across diverse driving conditions.

3) *Input Representations for the Student Network:* To further validate the significance of the proposed 4D Radar representation, we conducted an ablation study with three types of student inputs: (i) Fixed percentile<sub>99.9</sub> (very sparse), (ii) Fixed percentile<sub>95</sub> (denser), and (iii) CA-CFAR. As summarized in Table V, the proposed multi-teacher KD (with the same teacher models as described in the main framework) yields the largest gains under the sparsest setting (fixed percentile<sub>99.9</sub>: +7.3 AP<sub>3D</sub>, +9.5 AP<sub>BEV</sub> over the RTNH baseline), while also providing consistent improvements for the fixed percentile<sub>95</sub> input (+2.6 AP<sub>3D</sub>, +7.2 AP<sub>BEV</sub>). For CA-CFAR, KD slightly improves BEV accuracy (+0.9 AP<sub>BEV</sub>) but reduces 3D accuracy (-2.3 AP<sub>3D</sub>), likely because CA-CFAR emphasizes strong edge reflections while providing less dense object-interior information. This makes elevation cues harder to model for the sparse 3D backbone and limits its complementarity when used as either a student input or a teacher representation. Overall, the quantitative evaluations consistently indicate that our framework is most beneficial in the regime it targets—sparse Radar inputs—achieving substantial accuracy gains while preserving the computational advantages of low-point processing.

V. CONCLUSION
<figure><img src="image_9.png" /><figcaption>Fig. 6. Visualization of the predictions and feature map of RTNH<sub>99.9</sub> and 4DR-MR<sub>99.9</sub> across various weather conditions. The black boxes represent the ground truth while the red ones indicate detected objects.</figcaption></figure>

To address the inherent variability and inconsistency of 4D Radar signals—which pose significant challenges for robust perception—we proposed 4DR-MR, a novel 3D object detection framework fundamentally different from prior approaches. By leveraging multiple pre-processing techniques to extract diverse representations from 4DRT, our multi-teacher KD enables a lightweight student model to learn rich features from sparse inputs. Through the proposed aggregation module and efficient distillation process, our method effectively bridges the gap between raw data utilization and resource efficiency. Experimental results on the K-Radar dataset validate the

effectiveness of 4DR-MR, demonstrating superior detection performance with high computational efficiency.

Our findings highlight the necessity of embracing diverse signal representations in Radar perception, and point to future research on efficient methodologies for leveraging 4DRT in a resource-conscious yet accurate manner.

# APPENDIX I
DETAILED NETWORK ARCHITECTURE
## A. Aggregation Module Implementation

Each teacher feature map is passed through a separate alignment block designed to normalize feature semantics prior to fusion. The alignment block begins with a 1 × 1 convolution that reduces the channel dimension from 768 to 256, followed by batch normalization and ReLU activation. Next, a ConvNeXt-style block is applied, consisting of three consecutive 1 × 1 convolutions with intermediate LayerNorm and GELU activations. This block expands the channel dimension to 768 and then reduces it back to 256. A final 1 × 1 convolution and batch normalization reduce the feature to a common embedding dimension of 128, followed by ReLU activation.

The aligned features from each teacher are concatenated along the channel dimension and passed through a CBAM to emphasize informative regions. CBAM comprises two components:

* **ChannelGate:** Applies average and max pooling across spatial dimensions, followed by a two-layer MLP with reduction ratio $r = 8$.

* **SpatialGate:** Applies average and max pooling across channels, concatenates the results, and applies a 7 × 7 convolution for spatial attention.

Finally, a 1 × 1 convolution is applied to produce the aggregated teacher feature.

## B. Densify Module Implementation

To densify sparse student features and reduce mismatch with dense teacher features, we apply a two-stage encoder-decoder architecture extended from [33]. Each stage consists of:

* A ConvNeXt-style encoder: sequential residual blocks with downsampling to capture global context.

* A decoder: transposed convolutions to restore spatial resolution.

The student backbone feature is first passed through the first encoder-decoder block to produce an intermediate dense representation. This is then passed through a second encoder-decoder block to further refine feature quality. The output of the second decoder is fused with the original student feature via a residual connection. Finally, the fused representation is processed by a sequence of convolutional layers to yield the final student feature $F^S$.

# APPENDIX II
## ADDITIONAL RESULTS ON ALL CLASSES

For completeness, we also report comparisons on the Bus or Truck class in Table VI, alongside the Sedan results presented in the main text. While 4DR-MR shows the best overall performance, its results on Bus or Truck remain comparable to prior works. This may stem from the RTNH baseline's relatively lower performance on large vehicles, which affects downstream improvements despite overall gains. In this work, we focus on validating the effectiveness of the proposed framework using RTNH [9] as the baseline, since it is the fundamental model provided in the K-Radar benchmark for processing 4DRT. As our framework is architecture-agnostic, it

<table><caption>TABLE VI
COMPARISON WITH OTHER 4D RADAR-BASED DETECTION NETWORKS ON THE SEDAN AND BUS OR TRUCK CLASS. **BOLD** INDICATES THE BEST RESULT IN EACH METRIC.</caption><thead><tr><th rowspan="2">Network</th><th colspan="2">Sedan</th><th colspan="2">Bus or Truck</th></tr><tr><th>AP<sub>3D</sub></th><th>AP<sub>BEV</sub></th><th>AP<sub>3D</sub></th><th>AP<sub>BEV</sub></th></tr></thead><tbody><tr><td>RTNH [9]</td><td>36.84</td><td>43.10</td><td>19.55</td><td>25.60</td></tr><tr><td>RTNH+ [24]</td><td>37.36</td><td>45.79</td><td>19.90</td><td>27.51</td></tr><tr><td>Radar PillarNet [28]</td><td>37.58</td><td>46.02</td><td>30.68</td><td><strong>41.84</strong></td></tr><tr><td>RPFA [10]</td><td>37.49</td><td>40.00</td><td>23.19</td><td>32.50</td></tr><tr><td>SMURF [12]</td><td>37.57</td><td>40.01</td><td>27.80</td><td>39.84</td></tr><tr><td>DADAN [14]</td><td>37.62</td><td>45.59</td><td><strong>33.47</strong></td><td>38.36</td></tr><tr><td>MVFAN [13]</td><td>43.91</td><td>47.34</td><td>29.28</td><td>38.98</td></tr><tr><td><strong>4DR-MR</strong></td><td><strong>44.16</strong></td><td><strong>52.59</strong></td><td>25.62</td><td>29.02</td></tr></tbody></table>

can be readily extended to other 4D Radar detection networks, which we consider a promising direction for future research.

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<figure><img src="image_10.png" /></figure>

**Seung-Hyun Song** received the B.S. degree in Department of Automobile and IT Convergence from Kookmin University, South Korea, in 2024. He is currently pursuing the M.S. degree in the Graduate School of Advanced Security Science and Technology at KAIST. His research interests include 4D Radar, AI-based perception, and AVs.

<figure><img src="image_11.png" /></figure>

**Dong-Hee Paek** received the B.S. degree in robotics from Kwang-Woon University, South Korea, in 2019, the M.S. degree from The Robotics Program at the KAIST in 2021, and the Ph.D. degree from the CCS Graduate School of Mobility at KAIST in 2025. He was with Zeta Mobility, where he worked on the commercialization of 4D Radar AI technologies. He is currently a postdoctoral researcher at KAIST. His research interests include AI technologies for 4D Radar and sensor fusion.

<figure><img src="image_12.png" /></figure>

**Minh-Quan Dao** received the M.Sc (2020) and PhD (2023) degrees in Robotics from École Centrale de Nantes, France. He has been working as a postdoc researcher in the ACENTAURI team at INRIA Sophia-Antipolis Méditerannée. His research interests include label-efficient methods for scene understanding with a focus on collaborative perception.

<figure><img src="image_13.png" /></figure>

**Ezio Malis** is Research Director at Inria at Université Côte d'Azur and leads the ACENTAURI team. He graduated from SUPELEC and Politecnico di Milano in 1995, and earned a Ph.D. in Robotics and Automation from the University of Rennes in 1998. After a postdoctoral position at the University of Cambridge, he joined Inria Sophia Antipolis, where he later received his HDR in 2008. He spent 9 years in the ICARE and AROBAS teams before founding and leading the EVOLUTION team (2009–2010) to support technology transfer. In 2010, he co-founded Robocortex S.A.S., serving as CEO and R&D Director until 2020, developing autonomous robotics and AR/IoT software. His research interests include efficient optimization (including quantum computing), hybrid AI, and sensor-based control in autonomous robotics. He has authored about 100 papers.

<figure><img src="image_14.png" /></figure>

**Seung-Hyun Kong** (M'06-SM'16) is an Associate Professor in the CCS Graduate School of Mobility, Korea Advanced Institute of Science and Technology (KAIST), where he has been a faculty member since 2010. He received a B.S. in Electronics Engineering from Sogang University, Seoul, Korea, in 1992; an M.S. in Electrical and Computer Engineering from Polytechnic University (now part of NYU), New York, in 1994; and a Ph.D. in Aeronautics and Astronautics from Stanford University in 2005. From 1997 to 2004 and again from 2006 to 2010, he worked at Samsung Electronics (Telecommunication Research Center), Nexpilot (Korea), Polaris Wireless (San Jose), and Qualcomm Corporate R&D (San Diego), focusing on advanced R&D in mobile communication, wireless positioning, and assisted GNSS. His current research interests include deep neural networks for 4D Radar-based perception and localization, sensor fusion, reinforcement learning for end-to-end autonomous driving, and compression for embedded systems. He has authored over 100 journal and conference papers, holds 14 patents, and his group received the President's Award at the 2018 International Student Autonomous Driving Competition in Korea. He was Program Co-Chair of IEEE ITSC 2019 (New Zealand), General Chair of IEEE IV 2024 (Korea), and has served as an Associate Editor of IEEE T-ITS since 2017.