3DET-Mamba: Causal Sequence Modelling for End-to-End 3D Object Detection

Authors: mingsheng  li
jiakang  yuan
sijin  chen
lin  zhang
anyu  zhu
xin  chen
tao  chen
Venue: NeurIPS_cc_2024_Conference
Published: 2024-04-23 03:28:46

Abstract: Transformer-based architectures have been proven successful in detecting 3D objects from point clouds. However, the quadratic complexity of the attention mechanism struggles to encode rich information as point cloud resolution increases. Recently, state space models (SSM) such as Mamba have gained great attention due to their linear complexity and long sequence modeling ability for language understanding. To exploit the potential of Mamba on 3D scene-level perception, for the first time, we propose 3DET-Mamba, which is a novel SSM-based model designed for indoor 3d object detection. Specifically, we divide the point cloud into different patches and use a lightweight yet effective Inner Mamba to capture local geometric information. To observe the scene from a global perspective, we introduce a novel Dual Mamba module that models the point cloud in terms of spatial distribution and continuity. Additionally, we design a Query-aware Mamba module that decodes context features into object sets under the guidance of learnable queries. Extensive experiments demonstrate that 3DET-Mamba surpasses previous 3DETR on indoor 3D detection benchmarks such as ScanNet, improving AP25/AP50 from 65.0\%/47.0\% to 70.4\%/54.4\%, respectively.

Main paper: 
 introduction: 
 The aim of 3D object detection [24, 43, 26, 35] from point clouds is to locate and recognize objects present in 3D scenes. It is a challenging task since point clouds are often irregular, sparse, and unordered. To directly work with point clouds, VoteNet [31] utilizes PointNet++ [33] to extract features from irregular point clouds, which are then fed into a decoder to generate the 3D bounding boxes. Motivated by the success of Transformer [40] in computer vision [8, 34, 20, 3, 19] , some works [24, 14, 26, 4] try to design Transformer-based 3D detectors. 3DETR [26] proposes an endto-end transformer-based architecture to generate bounding boxes from raw point clouds. However, with limited computational resources, the quadratic complexity of the attention mechanism struggles to encode detailed representations, as it relies on increasing the point cloud resolution (i.e., longer point cloud sequences).Recently, state space models (SSMs) [10, 45, 16] have received significant attention due to their linear complexity and long-sequence modeling ability. As Mamba [10] demonstrates a strong ability to handle long sequences in natural language processing, it has rapidly been employed on different tasks (e.g., image and 3D object classification [23, 60, 21, 22] , video understanding [18, 1] and motion generation [55, 46] ) and achieves great success. This motivates us to take advantage of Mamba in capturing long-range dependencies to extract more detailed representations in complex 3D scenes. However, directly integrating Mamba [10] into the off-the-shelf detectors cannot achieve satisfactory results on 3D object detection tasks due to the following challenges. Firstly, SSMs like Mamba are causal models designed for handling 1-D sequence data, making it difficult to model unordered and non-causal 3-D point clouds. Secondly, the original Mamba block focuses on modeling long-range global information but lacks the ability to extract local features, which are essential for point cloud learning [25, 29] . Besides, previous works primarily use Mamba as an encoder for single object analysis, it remains unexplored for more complex scene-level point clouds and detection tasks.To tackle the above challenges and exploit the potential of Mamba [10] in 3D scene understanding, in this paper, we propose 3DET-Mamba, an end-to-end 3D detector that fully takes advantage of Mamba. To effectively extract scene representations from unordered point cloud sequences, we introduce a local-to-global scanning technique that can capture local geometry as well as global representation. Specifically, the local-to-global scanning technique utilizes the Inner Mamba block to capture finer details in each local patch and then uses Dual Mamba blocks to further extract scene features in a global view. Additionally, given that the naive Mamba block struggles to effectively model the relationship between object queries and scene features, we propose a query-aware Mamba block that decodes scene context information into object sets more effectively, guided by box queries. Extensive experiments on standard benchmarks show that our method can outperform 3DETR [26] on ScanNet and SUN RGB-D. Moreover, the performance can be further improved by increasing the input point cloud and learnable query sequences.Our contribution can be summarized as follows:• We introduce Mamba into 3D scene perception for the first time and construct an end-to-end detector named 3DET-Mamba which fully takes advantage of Mamba. Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?Answer: [Yes]Justification: The main claims made in our abstract and introduction that our 3DET-Mamba can boost previous 3DETR with the help of our novel SSM-based architecture accurately reflect the paper's contributions and scope.Guidelines:• The answer NA means that the abstract and introduction do not include the claims made in the paper. • The abstract and/or introduction should clearly state the claims made, including the contributions made in the paper and important assumptions and limitations. A No or NA answer to this question will not be perceived well by the reviewers. • The claims made should match theoretical and experimental results, and reflect how much the results can be expected to generalize to other settings. • It is fine to include aspirational goals as motivation as long as it is clear that these goals are not attained by the paper. 
related work: 
 In this section, we will briefly introduce existing works on 1) 3D object detection, and 2) state space models and Mamba, especially Mamba in vision tasks.With the development of robotics, 3D object detection [31, 9, 2, 51, 52] from point clouds has attracted increasing attention. Existing works can be divided into grid-based and point-based methods. Gridbased (voxel-based) methods convert the irregular point clouds into 2D grids [17] or 3D voxels [42, 35, 36, 59, 50] to utilize the strong feature extraction ability of CNN. Among them, FCAF3D [35] proposes a fully convolution anchor-free framework. CAGroup3D [42] designs a two-stage pipeline by introducing class-aware proposal generation and RoI-conv pooling. However, these grid-based methods struggle to utilize the inherent sparsity of the data and incur significant computational expenses due to the 3D convolution operations [31] . Inspired by PointNet family [32, 33] , Pointbased methods [37, 26, 24, 31, 57, 48, 5] directly generate 3D proposals from raw points. As a pioneer, VoteNet [31] proposes a voting mechanism to generate accurate proposals by generating new points close to the objects' center. RBGNet [43] design a ray-based representation which largely improves the performance. Motivated by the success of Transformer on image detection, 3DETR [26] and Groupfree [24] introduce a transformer-based architecture and boost the detection accuracy. However, the quadratic complexity of the attention mechanism struggles to model long-range dependencies with limited computational resources. In this paper, we present a novel detection framework that can serve as a promising foundational module in 3D detection.State Space Models and Mamba. Inspired by the success in control theory, state space model (SSM) [13, 38, 28, 12] has been utilized to model long-range dependence. Structured State-Space Sequence (S4) [13] replaces CNN and Transformer with SSM in vision and language tasks by combining linear SSM and HiPPO [11] framework. Smith et al. [38] propose S5 layer which designs a multi-input, multi-output SSM which outperforms S4 layer in both performance and efficiency. More recently, Mamba [10] with selective SSM is introduced which achieves higher performance than Transformer and leads to lots of further research on SSM [30, 44, 41] . For example, MoE-Mamba [30] and GraphMamba [41] combine Mamba with MoE and Graph data, respectively.Mamba in Vision. Thanks to the breakthrough in natural language processing, Mamba has been rapidly transferred to various vision tasks [60, 23] . Vim [60] proposes a bi-directional SSM that can efficiently compress the vision representation and achieve satisfactory results on multiple vision tasks at low cost. VMamba [23] employs a cross-scan module to enable 1D selective scanning in 2D image space. Mamba-Unet [47] and MedMamba [53] introduce Mamba to medical image segmentation and classification, respectively. Video Mamba Suite [1] and VideoMamba [18] verify the effectiveness and efficiency of Mamba in various video tasks. QueryMamba [58] combines a query-based transformer decoder and the Mamba encoder to handle video action forecasting tasks. TM-Mamba [46] modifies the Mamba parameters as the function of the input and text query to ground the human motion. More recently, several works [54, 22, 21, 15] have explored the feasibility of Mamba on 3D tasks by introducing different point cloud ordering strategies. For example, PointMamba [21] utilizes Mamba to model the global information of 3D point clouds through a reordering mechanism and largely reduces the computation cost. Despite the great success achieved in object-level classification and part segmentation, Mamba in 3D scenes is under-explored. In this paper, we propose 3DET-Mamba which fills the gap of Mamba in 3D scene perception with designs like local-to-global scanning, dual Mamba, and query-aware Mamba.Question: Does the paper discuss the limitations of the work performed by the authors?Answer: [Yes] Justification: We discuss the limitations of our work in Section 6.Guidelines:• The answer NA means that the paper has no limitation while the answer No means that the paper has limitations, but those are not discussed in the paper. • The authors are encouraged to create a separate "Limitations" section in their paper.• The paper should point out any strong assumptions and how robust the results are to violations of these assumptions (e.g., independence assumptions, noiseless settings, model well-specification, asymptotic approximations only holding locally). The authors should reflect on how these assumptions might be violated in practice and what the implications would be. • The authors should reflect on the scope of the claims made, e.g., if the approach was only tested on a few datasets or with a few runs. In general, empirical results often depend on implicit assumptions, which should be articulated. • The authors should reflect on the factors that influence the performance of the approach.For example, a facial recognition algorithm may perform poorly when image resolution is low or images are taken in low lighting. Or a speech-to-text system might not be used reliably to provide closed captions for online lectures because it fails to handle technical jargon. • The authors should discuss the computational efficiency of the proposed algorithms and how they scale with dataset size. • If applicable, the authors should discuss possible limitations of their approach to address problems of privacy and fairness. • While the authors might fear that complete honesty about limitations might be used by reviewers as grounds for rejection, a worse outcome might be that reviewers discover limitations that aren't acknowledged in the paper. The authors should use their best judgment and recognize that individual actions in favor of transparency play an important role in developing norms that preserve the integrity of the community. Reviewers will be specifically instructed to not penalize honesty concerning limitations. 
method: 
 The overview framework of 3DET-Mamba is shown in Fig. 1 . To better illustrate 3DET-Mamba, we first briefly review SSMs in Sec. 3.1. Then, we provide an overview of our 3DET-Mamba in Sec. 3.2. In Sec. 3.3 and Sec. 3.4, we detail the design of the encoder and decoder, respectively.State Space Models (SSMs) are derived from continuous systems and have been widely used in sequence modeling recently. Through a hidden state h(t), SSMs can efficiently map 1D sequences input x(t) to the output y(t) using the following equations:EQUATIONwhere A ∈ R denotes the evolution matrix, and B ∈ R N ×1 , C ∈ R 1×N are the projection matrices. To make SSMs can handle discrete signals such as language and point clouds, S4 [13] and Mamba [10] use a timescale parameter ∆ to transform the continuous parameters A and B into discrete ones A and B. Specifically, Mamba uses zero-order hold (ZOH) method as follows:EQUATIONThen, the Eq. ( 1) can be reformulated to the discrete version as follows:EQUATIONFinally, a global convolution can be used to perform the model's calculation:EQUATIONwhere L denotes the length of input sequence and K ∈ R L presents a convolution kernel.As shown in Fig. 1 , our 3DET-Mamba takes a set of N points S ∈ R N ×d as input and generates a set of 3D (oriented) bounding boxes with semantic labels for all objects of interest. Specifically, 3DET-Mamba mainly consists of the Mamba-based scene feature aggregator and decoder designed for 3D object detection. The 3D feature aggregator combines Inner Mamba and Dual Mamba blocks to perform local-to-global scanning, extracting both the fine-grained local geometries and global contexts within the scene. On the decoding side, the decoder employs query-aware Mamba and Multi-Layer Perceptrons (MLPs) to decode the aggregated 3D features into discrete sets of objects.In the following sections, we will detail the designs of each component.Recent advancements in point cloud processing have demonstrated the importance of capturing both local geometric features and global scene information [25] . However, previous methods primarily focus on global modeling using state-space models [21, 22, 54] , resulting in a lack of fine-grained details. To tackle this challenge, we propose a novel local-to-global scanning technique that emphasizes scanning locally to uncover these finer details in each patch, followed by global scanning to capture the dependencies among local features.In this section, we introduce a novel Mamba-based scene feature aggregator designed for localto-global scanning, which is aimed at learning detailed representations of complex 3D scenes, as illustrated in Fig. 2 . Specifically, our scene feature aggregator includes the Inner Mamba block and Dual Mamba block, which will introduced in Sec. 3.3.1 and Sec. 3.3.2 respectively.Given an unordered N point cloud sequence {x 1 , x 2 , . . . , x n }, where x i ∈ R d and d is the dimension of features, farthest point sampling (FPS) is first used to select K key points from the input point cloud. Then k-nearest neighbors (KNN) is utilized to identify N 0 nearest neighbors for each key point, subsequently forming K patches P ∈ R K×N0×d .Previous works [33, 21, 22] employ MLPs to learn features from each patch, they struggle with the effective aggregation of local features. To address this, we treat the aggregation of local features as a sequence-to-sequence generation process and use a causal Mamba model to extract local features within a patch. Specifically, points within each patch are first normalized and then sorted based on their distance from the key point. These sequential points are fed into a lightweight Mamba block, of which the dimensions are reduced compared to the original Mamba block, to generate a new feature sequence. Finally, max-pooling is used to obtain the embedding for each patch, denoted as F s ∈ R K×C , where K denotes the number of patches and C is the embedding dimension.To encode point cloud data, previous works such as PointMamba [21] propose learning 3D representations by utilizing the original Mamba, which is initially designed for 1-D ordered sequences. Since understanding 3D data requires capturing global information, Point Cloud Mamba [54] suggests reversing the order of tokens and employing both forward and backward State Space Models (SSMs) to better capture the global context. However, due to the unordered and irregular nature of point T F l-1 : (B, K, C) ← T l-1 3: T N l-1 : (B, K, C) ← NPS(T l-1 ) 4: for o in {F , N } do 5: T o l-1 ′ ← Norm(T o l-1 )6:z o : (B, K, C ′ ) ← Linear zo (T o l-1 ′ ) 7:xo : (B, K, C ′ ) ← Linear xo (T o l-1 ′ ) 8:x′ o : (B, K, C ′ ) ← SiLU(Conv1d o (x o )) 9: B o : (B, K, D) ← Linear B o (x ′ o )10:C o : (B, K, D) ← Linear C o (x ′ o ) 11:/* softplus ensures positive ∆ o */ 12:∆ o : (B, K, C ′ ) ← log(1 + exp(Linear ∆ o (x ′ o ) + Parameter ∆ o )) 13: /* shape of Parameter A o is (C ′ , D) */ 14: A o : (B, K, C ′ , D), B o : (B, K, C ′ , D) ← Disc (∆ o , Parameter A o , B o ) 15: y o : (B, K, C ′ ) ← SSM(A o , B o , C o )(x ′ o ) 16:/* get gated y o */ 17:y ′ o : (B, K, C ′ ) ← y o SiLU(z o ) 18: end for 19: y ′ N : (B, K, C ′ ) ← FPS(y ′ N ) 20: T l : (B, K, C) ← Linear T (y ′ F + y ′ N ) + T l-1 21: Return: T lclouds, simply reversing the order of tokens cannot ensure the causal dependency of the point cloud sequence and may lead to unreliable results.To mitigate such a problem, we introduce a novel Dual Mamba block (as shown in Fig. 2 ) to model long-range dependencies from the global view. Specifically, we treat F s ∈ R K×C as tokens and sort them based on their coordinates into two categories: farthest and nearest neighbor orders. The former strategy enhances the model's perception of spatial distribution by maximizing the distances between adjacent points in the sequence, whereas the latter ensures that adjacent points remain spatial neighbors, thus maintaining local consistency. The Dual Mamba block incorporates two branches to handle FPS and NPS token sequences, which first undergo normalization and independent linear projection. For each sequence, an initial 1D convolution transforms it into x ′ o , which is then projected F o t-1 ′ ← Norm(F o t-1 )3:x o : (B, -, C ′ ) ← Linear xo (F o t-1 ′ ) 4: x ′ o : (B, -, C ′ ) ← SiLu(Conv o (x o )) 5:/* Disc and SSM */ 6:A: (B, -, C ′ , D), B: (B, -, C ′ , D), C :(B, -, C ′ , D) ← Disc(x ′ o )7:y o : (B, -, C ′ ) ← SSM(A, B, C)(x ′ o ) 8: z :(B, -, C ′ ) ← Linear z (F s t-1 )9:y ′ o : (B, -, C ′ ) ← y o ⊙ SiLu(z) 10: end for 11: y ′ q : (B, M, K) ← y q ⊙ y ′ s 12: F q t : (B, M, C) ← Linear F (y ′ s ) ⊙ y ′ q 13: F q t : (B, M, C) ← F q t + Norm(F q t-1 ) 14: return F q t ;DETR-based models leverage a set of object queries to extract features for object classification and localization. However, directly using scene context as a prefix and concatenating it with queries in the Mamba model leads to suboptimal performance, as it struggles to capture discriminative features for independent queries. To address this, we introduce the Query-aware Mamba block for the decoder, as shown in Fig. 3 , which effectively models the relationship between learnable queries and scene features to generate bounding boxes.We first generate object queries by selecting a defined number of M points from the set of K key points using FPS, ensuring these query points cover the entire scene. For each of these points, we follow 3DETR [26] to transform their spatial coordinates into positional embeddings using the Fourier Transform. These embeddings are subsequently processed through an MLP to produce the initialized query embeddings.In detail, the decoder is composed of D same blocks and each block consists of a query-aware Mamba block and multiple MLP layers. The Query-aware Mamba block takes box queries and scene context as input, extracting tasked-related features from the scene context guided by the learnable queries. Specifically, each query sequence F q is fed into a standard Mamba block to model the dependencies between queries. This process can be formulated as follows:EQUATIONMeanwhile, scene features undergo the same process as the query sequence. Then, by multiplying the scene features with query embeddings, scene contexts are integrated into the query embeddings, and the updated queries are then passed through multiple MLP layers. After D blocks decoding process, the bounding boxes and semantic categories can be generated using MLP-based heads. Please refer to the Algorithm 2 for more details.We adopt the same training objectives as [26] to train 3DET-Mamba. Specifically, we use the bipartite graph to match the set of predicted 3D bounding boxes { b} with the ground truth boxes {b}, denoted as L giou ( b, b). Then we calculate the discrepancies between { b} and {b} using ℓ 1 loss for centers and dimensions, and Huber loss for angular residuals:EQUATIONhere, λ c , λ d ,λ a are set as 5, 1, and 0.5. We also employ cross-entropy losses to assess angular and semantic classifications:EQUATIONhere, λ ac and λ s are set as 0.1 and 1. Finally, the total loss is formulated as:EQUATION)Question: For each theoretical result, does the paper provide the full set of assumptions and a complete (and correct) proof?Question: Does the paper provide open access to the data and code, with sufficient instructions to faithfully reproduce the main experimental results, as described in supplemental material?Answer: [No] Justification: Data and code are not included in the submission due to the time limit, we will release our code in the future.Guidelines:• The answer NA means that paper does not include experiments requiring code. • Providing as much information as possible in supplemental material (appended to the paper) is recommended, but including URLs to data and code is permitted. 
experiments: 
 In this section, we first describe our experiment setups, including our benchmark datasets, metrics, and implementation details in Sec. 4.1. Then we present our main results in Sec. 4.2 and take out ablation studies to analyze the effectiveness of the proposed component in Sec. 4.3. Finally, we showcase some visualization results in Section Sec. 4.4.Datasets. Following previous works on 3D indoor object detection, we evaluate our models on two challenging benchmarks: SUN RGB-D [39] and ScanNet [7] . The SUN RGB-D [39] dataset consists of 10,335 single-view RGB-D scans, with 5,285 used for training and 5,050 for validation. Each sample is annotated with rotated 3D bounding boxes. Following [31, 26] , we convert the RGB-D images into point clouds using camera parameters and evaluate models on the 10 most common object categories. ScanNet [7] comprises 1,201 training samples and 312 validation samples, with each sample annotated with axis-aligned bounding box labels for 18 object categories.Metrics. Following [24, 26] , we use standard evaluation protocols [31, 35] and report the detection performance on the validation set using mean Average Precision (mAP) at two different IoU thresholds (i.e.m 0.25 and 0.5), denoted as mAP@0.25 and mAP@0.5, respectively.Implementation Details. The input to our model is a point cloud P ∈ R N ×3 representing a 3D scene, with N set as 20,000 for SUN RGB-D [39] and 40,000 for ScanNet [7] . We employ a single-layer inner mamba block that generates 2048 patches, each with 256-dimensional features. The dual mamba encoder has 3 layers and outputs scene features with a hidden dimension of 256. The decoder has 8 layers and is closely followed by MLPs as the bounding box prediction head. During training, we employ standard data augmentation methods, including random cropping, sampling, and flipping. We use the AdamW optimizer with a base learning rate of 7 × 10 -4 , decayed to 10 -6 using a cosine schedule, and a weight decay of 0.1. Gradient clipping with an ℓ 2 norm of 0.1 is applied to stabilize training. The whole model is implemented in PyTorch, and all experiments are conducted on 8 NVIDIA 3090 GPUs (24 GB) with a total batch size of 64.In this section, we compare our 3DET-Mamba with previous 3D detectors. As shown in Tab. 1, our 3DET-Mamba can outperform previous 3DETR [26] (Tramsformer-based detectors) on both SUN RGB-D and ScanNet datasets. For example, with 256 queries and 2048 points, our method achieves 66.9% mAP@0.25 and 48.7% mAP@0.5 on ScanNet, surpassing 3DETR-m, which obtains 65.0% mAP@0.25 and 47.0% mAP@0.5 respectively. Besides, since Mamba can effectively handle long sequences, we further conduct experiments using point clouds with higher resolution and more box queries (i.e., 4096 point clouds and 512 box queries), we can observe that the performance can be further improved with longer point and query sequences (i.e., +5.7 mAP@0.5).Analysis of the encoder. [7] and SUN RGB-D [39] . We compare 3DET-Mamba against open-source methods that directly process point clouds, using PointNet++ [33] (PN) and Transformer [40] (Tran.) as backbones. 3DET-Mamba employs the same number of key points and queries as 3DETR [26] . 3DET-Mamba † doubles the number of key points and queries to model longer sequences of point clouds. Compared to 3DETR [26] , 3DET-Mamba achieves superior performance.Backbone mAP@0.25 mAP@0.5 mAP@0.25 mAP@0. Mamba to PointNet++ results in a performance drop of 1.8% mAP@0.25 and 1.3% mAP@0.5 when using Transformer and Dual Mamba, respectively. This is because our Inner Mamba can effectively aggregate and propagate local features. Besides, our Dual Mamba block can bring +4.3% mAP@0.5 and +3.3% mAP@0.5 improvement compared to using Transformer as the spatial encoder since our Mamba layers can learn both spatial representation and local consistency at the same time. By combining the Inner and Dual Mamba, the best results can be achieved which further verifies our designs.To further show the advantage of our designed Dual Mamba block, we replace the Dual Mamba block with the original Mamba [10] block and Bi-Mamba block [60, 22, 54] , respectively. The original Mamba block only contains a forward SSM and the Bi-Mamba block is composed of a forward and a backward SSM. As shown in Tab. 3, our proposed dual Mamba block can outperform both the original Mamba block and Bi-Mamba block by +1.0% and +1.2% mAP@0.5. This is because our dual Mamba block can provide point cloud sequence with short-range and long-range dependencies and further make better use of the powerful causal modeling ability of Mamba.Effect of Query-aware Mamba. We conduct ablation studies on the decoder to demonstrate the effectiveness of the designed Query-aware Mamba block. We compare our module against the following baselines: (a) a transformer-based decoder as proposed in 3DETR [26] ; (b) a naive approach that directly concatenates 3D scene information with queries, feeding them into the Mamba block and leveraging Mamba's state transition matrix for further modeling; and (c) our proposed Query-aware Mamba block. Additionally, all experiments are conducted with the same encoder and training strategy to ensure a fair comparison. As demonstrated in Tab. 4, the decoder based on the Queryaware Mamba block achieves superior results compared to both baselines. This improvement can be attributed to two main factors: (1) Directly concatenating queries and 3D scene representations do not effectively model the relationship between them, making it difficult to capture the most relevant 3D features for each query. (2) Our Query-aware Mamba decoder more effectively aggregates dependencies between queries and scene features, enhancing the extraction of task-relevant features from the context.To demonstrate the effectiveness of 3DET-Mamba more intuitively, we show some visualization results. As shown in Fig. 4 , we can observe that our 3DET-Mamba can accurately detect objects.Our GT Figure 4 : Visualization of detection results. 3DET-Mamba is able to generate tight bounding boxes for objects of interest in these complex and diverse scenes. 
conclusion: 
 In this paper, for the first time, we exploit the potential of Mamba in 3D object detection tasks. Specifically, we introduce 3DET-Mamba, an end-to-end detector based on encoder-decoder architecture.We first propose an SSM-based encoder that uses an Inner Mamba block to capture local geometric information and uses Dual Mamba blocks to further aggregate features in a global view. Besides, a Query-aware Mamba module is designed to effectively decode scene representations into object sets with the guide of learnable box queries. Extensive experiments on standard benchmarks like ScanNet and SUN RGB-D demonstrate the effectiveness of 3DET-Mamba, proving Mamba as a promising building block for future 3D scene understanding. In addition, with the increased length of input point sequence and query sequence, the performance will be further boosted since Mamba is an expert in long sequence modeling. 
limitations and broader impacts: 
 While 3DET-Mamba has demonstrated effectiveness in modeling point cloud sequences, we have yet to explore its potential for handling other 3D data types, such as meshes. Additionally, given Mamba's strength in modeling long-sequence data, a promising future direction is to develop Mamba-based 3D foundation models capable of addressing a broader range of scene-level tasks, including 3D dense caption, visual grounding, and 3D QA. We leave these explorations for future work.NeurIPS Paper ChecklistQuestion: Does the paper specify all the training and test details (e.g., data splits, hyperparameters, how they were chosen, type of optimizer, etc.) necessary to understand the results?Answer: [Yes] Justification: We specify them in Section 4Guidelines:• The answer NA means that the paper does not include experiments.• The experimental setting should be presented in the core of the paper to a level of detail that is necessary to appreciate the results and make sense of them. • The full details can be provided either with the code, in appendix, or as supplemental material. 
experiment statistical significance: 
 Question: Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?Answer: [No] Justification: The results do not contain error bars.Guidelines:• The answer NA means that the paper does not include experiments.• The authors should answer "Yes" if the results are accompanied by error bars, confidence intervals, or statistical significance tests, at least for the experiments that support the main claims of the paper. • The factors of variability that the error bars are capturing should be clearly stated (for example, train/test split, initialization, random drawing of some parameter, or overall run with given experimental conditions).• The method for calculating the error bars should be explained (closed form formula, call to a library function, bootstrap, etc.) • The assumptions made should be given (e.g., Normally distributed errors). • It should be clear whether the error bar is the standard deviation or the standard error of the mean. Justification: There is no societal impact of the work performed. Guidelines:• The answer NA means that there is no societal impact of the work performed.• If the authors answer NA or No, they should explain why their work has no societal impact or why the paper does not address societal impact. • Examples of negative societal impacts include potential malicious or unintended uses (e.g., disinformation, generating fake profiles, surveillance), fairness considerations (e.g., deployment of technologies that could make decisions that unfairly impact specific groups), privacy considerations, and security considerations.• The conference expects that many papers will be foundational research and not tied to particular applications, let alone deployments. However, if there is a direct path to any negative applications, the authors should point it out. For example, it is legitimate to point out that an improvement in the quality of generative models could be used to generate deepfakes for disinformation. On the other hand, it is not needed to point out that a generic algorithm for optimizing neural networks could enable people to train models that generate Deepfakes faster. • The authors should consider possible harms that could arise when the technology is being used as intended and functioning correctly, harms that could arise when the technology is being used as intended but gives incorrect results, and harms following from (intentional or unintentional) misuse of the technology. • If there are negative societal impacts, the authors could also discuss possible mitigation strategies (e.g., gated release of models, providing defenses in addition to attacks, mechanisms for monitoring misuse, mechanisms to monitor how a system learns from feedback over time, improving the efficiency and accessibility of ML). 
safeguards: 
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 Appendix:  


Decision: All three reviewers tendentially like the paper and mildly recommend acceptance. The rebuttal helped resolve some of the open issues, and caused one reviewer to raise their rating. Given the technically relevant contribution, the AC recommends acceptance. The authors are encouraged to update their paper in the camera-ready version to include all improvements from the rebuttal.

Reviews: review: paper_summary: paper_summary: This paper proposes leveraging Mamba blocks for 3D point cloud modeling in the form of 3DET-Mamba, an application of SSM for 3D object detection. Similar to the prior 3DETR model, this approach partitions the point cloud into "patch" using Mamba blocks to capture local information, complemented by global modeling through dual Mamba blocks (organized by farthest and nearest point order). Additionally, query-aware Mamba blocks, akin to transformer blocks, are designed to decode objects in a DETR-like manner. The novelty of the approach lies in its ability to enhance 3DETR by integrating SSMs innovatively for 3D indoor scene understanding, utilizing ScanNet and SUN RGB-D datasets.
strength_weakness: strength_weakness: 1. Mamba is praised for handling linear complexity and long-range sequences better than transformers, which have higher computational demands. However, the paper lacks theoretical analysis or experimental evidence to compare time and space complexities between Mamba and traditional architectures. The effects of changing point resolutions or patch sizes on performance are also not explored.

2. The clarity of some ablation studies is lacking. For example, the role of ranked queries, a key contribution, is not evaluated. The relevance of NPS and FPS ordering in modeling patch sequences remains ambiguous. Additionally, the rationale for query-aware Mamba blocks is unconvincing; they seem to be simply added to cross-attention blocks with extra links. Table 4 does not clearly show that improvements are due to the Mamba block rather than just increased computational power.

3. The paper does not provide solid proof of Mamba’s effectiveness in terms of performance or speed. It omits a comparison with the once leading FCAF3D detector, which scored 71.5 on ScanNet, without explanation. Comparisons of inference speeds across architectures are also absent.
questions: questions: Besides weakness part, there are some other questions:
1. Some terms could potentially cause confusion. For instance, FPS is an acronym for farthest point sampling, but in the context of the dual Mamba, it refers to farthest point order, while NPS refers to nearest point order.
2. Are there any existing studies that use Mamba in a query-based method? It would be beneficial to reference these works to better understand the uniqueness of this module.
limitations: limitations: Yes.
paper_summary: paper_summary: The paper proposes an end-to-end 3D detector named 3DET-Mamba that fully takes advantage of Mamba. 3DET-Mamba can model long-range global information (Dual Mamba) while exploiting local information (Inner Mamba). Experiments conducted on the ScanNet and SUN RGB-D datasets validate the effectiveness of the proposed method.
strength_weakness: strength_weakness: - The structure depicted in Figure 3 is inconsistent with the description provided in Algorithm 2. Specifically, the branch for the Query sequence in Figure 3 lacks the inclusion of Linear-SiLU, which is mentioned in Algorithm 2.
- In Table 4, 1) the first column should correctly be labeled as "mAP@0.25," and the second column should be labeled as "mAP@0.5." 2) when using the Transformer as a decoder, the value of mAP@0.5, which is 42.6%, appears abnormal compared to other results. Specifically, in Table 1, the model DETR-m achieved 65.0% mAP@0.25 and 47.0% mAP@0.5. However, in Table 4, the first model listed achieves 64.3% mAP@0.25 but only 42.6% mAP@0.5. Are there any mistakes?
- Line 241 `PointNet++ and Transforme` -> 'PointNet++ and Transformer'.
- Line 180-181, `We randomly choose an initial point and sort the remaining points based on their distance to this point`, are the experimental results stable? It seems unbelievable.
questions: questions: See the Weaknesses.
limitations: limitations: N/A
paper_summary: paper_summary: This paper proposes 3DET-Mamba, the first attempt to exploit the State Space Model for end-to-end 3D object detection. It introduces a local-to-global scanning technique, including an Inner Mamba block to capture local geometry and Dual Mamba blocks to extract scene features in a global view. Furthermore, it proposes a query-aware Mamba block to effectively decode scene context information into object sets with the guidance of box queries. Experiments show promising results compared to 3DETR and validate the effectiveness of the proposed modules with detailed ablation studies.
strength_weakness: strength_weakness: - One of the motivations is the computational costs of transformers compared to Mamba, but there are no analyses regarding memory costs, latency, etc. I am curious about more advantages of the Mamba framework in this task, such as computational efficiency and scaling up performance, except for the current traditional benchmarks.
- Although the paper proposes several methods to deal with the unordered point clouds in the ordered manner that is inherently designed in the State Space Model, even though the performance also seems good, the mechanism is still not most suitable for this task or handling point clouds. I would expect a more matching mechanism to deal with point clouds with the fundamental idea of the State Space Model instead of converting the problem with a non-perfect workaround.
- There are several typos and I just provide a few examples in Questions. The paper needs more check on such details.
questions: questions: Sec. 4.2 title: Obejct -> Object
line 241: Transforme -> Transformer
limitations: limitations: None.

Comments: title: Final Decision
comment: Dear reviewer,

Thank you for taking the time to review our work and for your valuable comments!

The results in Table r1 fairly compare 3DETR and 3DET-Mamba in terms of Flops and latency. We further conduct experiments to show the effectiveness and efficiency of Query-aware Mamba (i.e., decoder). Specifically, we replace the Query-aware Mamba with Cross-attention Blocks of the same number of layers. The results are shown in the following table. Importantly, our Query-aware Mamba Blocks not only reduce the computational cost but also further improve detection accuracy.



| **Decoder**                         | **FLOPs (↓)** | **AP@25 (↑)** | **AP@50 (↑)** |
|-------------------------------------|---------------|-----------|-----------|
| Cross-attention Transformer Blocks  | 1.89          | 64.3      | 42.6      |
| Query-aware Mamba Blocks            | **1.76**          | **66.9**      | **48.7**      |

Note: the FLOPs provided here are specific to the decoder module.

We will add related works and additional experiments mentioned by the reviewer in the revision of our article. Thanks again for your approval of our work.

Sincerely,
Authors of paper112
Dear reviewer,

Thank you for taking the time to review our work and for your feedback! We will add experimental results of computational efficiency in the revision of our article.

Sincerely,
Authors of paper112
Thank you for the author’s rebuttal, which addresses some of my concerns.

Is the table r1 fairly comparing 3DETR and 3DET-Mamba with only transformer blocks? I am particularly interested in whether there is an ablation study that demonstrates the necessity of using query-based Mamba rather than cross-attention blocks in a relatively fair comparison, considering the trade-off between computation and accuracy.

Given the state of the original paper’s presentation and the missing reference, I can now raise the rating to 5.
Thanks for the author's response. I think the experiments regarding the computational efficiency are important supplements to support the method's value, but there are still concerns regarding whether the Mamba structure is suitable for solving the problem with unordered point clouds input, both from theoretical analysis and intuition. It also lacks more comparison with state-of-the-art voxel-based methods like FCAF3D in the experiments. Therefore, I agree that this paper, as the first attempt, still has a long way to go along this technical pathway, but I may give more credit to the first try with basically reasonable experimental results as the support, and I think it can bring some new insights to the community. Hence, I would keep my original rating.