CADGrasp: Learning Contact and Collision Aware General Dexterous Grasping in Cluttered Scenes
Authors: Jiyao Zhang
Zhiyuan Ma
Tianhao Wu
Zeyuan Chen
Hao Dong
Venue: NeurIPS 2025 poster ()
Keywords: Dexterous Hand
General Grasping
Primary area: applications

Abstract: Dexterous grasping in cluttered environments presents substantial challenges due to the high degrees of freedom of dexterous hands, occlusion, and potential collisions arising from diverse object geometries and complex layouts. To address these challenges, we propose CADGrasp, a two-stage algorithm for general dexterous grasping using single-view point cloud inputs. In the first stage, we predict a scene-decoupled, contact- and collision-aware representation—sparse IBS—as the optimization target. Sparse IBS compactly encodes the geometric and contact relationships between the dexterous hand and the scene, enabling stable and collision-free dexterous grasp pose optimization. To enhance the prediction of this high-dimensional representation, we introduce an occupancy-diffusion model with voxel-level conditional guidance and force closure score filtering. In the second stage, we develop several energy functions and ranking strategies for optimization based on sparse IBS to generate high-quality dexterous grasp poses. Extensive experiments in both simulated and real-world settings validate the effectiveness of our approach, demonstrating its capability to mitigate collisions while maintaining a high grasp success rate across diverse objects and complex scenes.

CADGrasp: Learning Contact and Collision A ware General Dexterous Grasping in Cluttered Scenes
Zhiyuan Ma Tianhao Wu Zeyuan Chen Hao Dong! 1.2f Center on Frontiers of Computing Studies, School of Computer Science, Peking University 2 National Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University Jiyaozhang@stu, pku. edu.
Jiyao Zhang 1.2
CADGrasp
Contact- Aware
Collision-
Aware
Estimated IBS
Optimization
Dexterous Grasp
Figure 1:
We propose CADGrasp, which learns contact- and collision intermediate repre- sentation as a constraint, and further obtains the dexterous grasp pose with an optimization method to achieve single-view dexterous hand grasping in cluttered scenes.
Abstract
Dexterous grasping in cluttered environments presents substantial challenges due to the high degrees of freedom of dexterous hands, occlusion, and potential collisions arising from diverse object geometries and complex layouts. To address these challenges, we propose CADGrasp, a two-stage algorithm for general dexter- ous grasping using single-view point cloud inputs. In the first stage, we predict
*: equal contribution, t: corresponding author
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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sparse IBS, a scene-decoupled, contact- and collision-awar representation, as the optimization target. Sparse IBS compactly encodes the geometric and con- tact relationships between the dexterous hand and the scene, enabling stable and collision-free dexterous grasp pose optimization. To enhance the prediction of this high-dimensional representation, we introduce an decupancy-diffuse voxel-level conditional guidance and force closure score filtering. In the second stage, we develop several energy functions and ranking strategies for optimization based on sparse IBS to generate high-quality dexterous grasp poses. Extensive experiments in both simulated and real-world settings validate the effectiveness of our approach, demonstrating its capability to mitigate collisions while maintaining high grasp success rate across diverse objects and complex scenes. More details and videos are available at httph::// //ipggra//gggppp...... pertraspppgghuhbt io/
model with
Introduction
Dexterous grasping in cluttered scenes is critical step toward enabling dexterous hand to au- tonomously perform diverse tasks in real-world environments. Compared to single-object dexterous grasping [
2 3
4
5 6 77, the diverse and complex layouts in cluttered scenes [8] 9 TO IT [2]
introduce additional challenges beyond generalizing to various objects. The stacking of objects leads to occlusion, resulting in partial observations without full object geometry. Moreover, the restricted graspability caused by stacking requires more precise grasp poses while avoiding collisions with surrounding objects prevent unintended outcomes, such as target object displacement due contact between the dexterous hand and nearby objects.
Current methods focus on constructing large-scale synthetic datasets to capture the distribution of potential cluttered scenes I0 [I] Based on these datasets, existing approaches typically first filter scene points with high graspability. Conditioned on these points, they employ either regression- based [10] or generative- based models [T]] to predict grasp poses. However, directly mapping partial point cloud observations grasp poses is challenging to generalize due to the non-linearity of the mapping from 3D point cloud space to pose space and the sensitivity of physical constraints to adopt two-stage framework that combines contact map prediction and optimization to enhance generalization. However, these methods are primarily designed for single-object grasping, assume access to complete object geometry for optimization, which is not available in real-world cluttered scenes due to partial observation.
small variations in hand pose [13]. Current methods [14] [5]
16 3
To tackle this problem, we propose predicting contact- and collision-awann intermediate repre- sentation to serve as the optimization target as shown in Figure [ The proposed representation, sparse IBS, is the interaction biscctor surface (IBS) 17 between the scene and the dexterous hand, incorporating compact contact indicators. This representation is decoupled from the scene, elim- inating the requirement for complete scene geometry, making it well-suited for cluttered scenes. Additionally, sparse IBS captures both geometric and contact information between the scene and the dexterous hand, making it effective for optimizing stable and collision-fre free dexterous grasp poses. To efficiently generate such a high-dimensional representation, we propose an occupancy-diffusion model with voxel-level conditional guidance and force closure score filtering. To further obtain stable and collision-free dexterous grasp poses, we design a set of energy functions tailored to sparse IBS.
In our experiments, we conduct extensive evaluations in simulation environments featuring 670 diverse cluttered scenes containing over 1300 objects. Comparative results demonstrate the effectiveness of our framework, while ablation studies further validate the effectiveness of our design. Our analysis highlights the stability and collision- awwrenesss of the generated grasp poses. Additionally, we evaluate our method against other baselines in real-world settings to validate its practicality.
In summary, our contribution is as follows:
We propose two-stage framework consisting of scene-decoupled contact- and collision-aware in- termediate representation prediction and constrained grasp pose optimization for general dexterous grasping in cluttered scenes.
We propose an cccuancy-diiffusin model with voxel-level conditional guidance and force closure score filtering to enhance representation prediction, along with several energy functions and ranking strategies to improve final grasp pose optimization.

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We conduct comprehensive simulation and real-world experiments to demonstrate the effectiveness of our method.
2 Related Work
2.1 One-stage Dexterous Grasp Pose Prediction
One-stage dexterous grasp pose generation M
19 20 aims train an end-to-end model to assume one-to-one mapping between
predict grasp poses. Regression-basee the object or scene and the grasp pose, which limited capturing the multi-modal dexterous grasp pose distribution due to the high degree of freedom. Generative- -aase methods [18] 22 23 [4
methods 19
20
21
can
model complex distributions, making them more suitable for dexterous grasp pose generation. Current approaches leverage physical constraints 3 2
such as contact and penetration, to enhance grasp pose quality. However, end-to-end methods struggle with generalization due the non-linearity between the observation space and pose space, well as the sensitivity of physical constraints to small errors in grasp poses [3]. This challenge becomes more pronounced in cluttered environments, where the diversity of objects and layouts necessitates extremely large-scale grasp pose datasets [[ limiting generalization. contrast, we propose a two-stage framework that first predicts an intermediate representation in the observation space, followed by optimization based on this representation to enhance generalization.
2.2 Two-stage Dexterous Grasp Pose Prediction
Two-stage dexterous grasp pose generation [16
14
25
26
27
28 decomposes grasp pose prediction
into two stages to mitigate the challenges of direct mapping. Typically, the first stage predicts
either the grasp pose [25] 26 27 29 30 or an intermediate representation [28] 16 14], followed by
optimization based on physical constraints between the object and the dexterous hand. However, these methods are specifically designed for single-object grasping and assume prior knowledge of complete object geometries, which is often unavailable cluttered scenes due to object stacking [1] 10, even with multi-view cameras. This limitation restricts the applicability of two-stage methods in cluttered environments. In contrast, we propose a sparse IBS representation that serves as an intermediate representation, enabling the application of two-stage framework in cluttered scenes.
2.3 Hand-Object Representation
Hand-object representation can be primarily utilized in two ways. The first is serve the observation 31 32 28 33), compressing the observation feature space and reducing the complexity of geometric feature learning, thereby enhancing grasp pose generation [28] grasp policy learning [31] 32). The second is to act as an intermediate representation for two-stage dexterous grasp pose generation methods 14 16]. The most common intermediate representation which computes the distance between each point on the object and the dexterous hand. However, this representation requires complete object geometry, which is not accessible in cluttered scenes. The most relevant to our work is Interaction Bisector Surface (IBS) [32], which computes a surface between the scene and the dexterous hand along with various spatial and contact information. However, such representation has only been used an observation representation [32 33). contrast, we propose using IBS as an intermediate representation for grasp pose optimization. To reduce the difficulty of predicting such representation, we design more compact sparse IBS representation and develop a specialized module to predict sparse IBS along with optimization strategies tailored to sparse IBS representation.
two-stage grasp pose
generation is the contact map 16
15 13],
3 Method
Problem Formulation. this work, we consider the problem of generating a set of grasp poses for dexterous hand to grasp objects in cluttered environment. We define grasp posc of dexterous hand as a tuple g = (T, where T E SE(3) indicates the wrist pose, E I is the joint configuration of the hand, and is the degree of freedom (DoF) of the hand. Given single view pointeloud P N of cluttered scene, we estimate the grasp poses G = {g:]][] and forlision-free for dexterous grasping.
191 that are stable
3

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Stage1: Conditional IBS Generation as Intermediate representation
Contact and Collision Aware
Conditional IBS Generation
Conditional Occupancy
Dexterous
Wrist Pose Estimation Canonicallzation
3D UNet
Nolsy Sparse IBS
Denoised Sparse IBS (40x40x40)
(40x40x40)
Target Object
Contact Points
Scene Geometry Condition
Stage2: Grasp Pose Optimization with IBS Constraint
Pose Optimization
Contact
Estimated IBS
Aware!
Collision
Aware!
Figure Overview of CADGrasp, two-stage framework for dexterous grasping in cluttered scenes. (I) Conditional IBS Generation: diffusion model is trained to model the conditional probability distribution p[[[P, T). (II) Grasp Pose Optimization: We optimize the grasp poses G with predicted sparse IBS Ī as constraints.
Overview. The overview of our method is shown in Figure 2 We propose a two-stage framework for dexterous grasping in cluttered scenes, called CADGrasp. We use the sparse IBS I that is aware of contact and collisions as an intermediate representation between the two stages, which can efficiently encode the geometric relationship between the dexterous hand and the scene corresponding to the successful grasp poses (Section 3.1 b. In the first stage, we model the conditional probability distribution P[I[P. T) by diffusion model, where I the sparse IBS, P the single-view observed scene point cloud, and T is the wrist pose Section 3.2). In the second stage, with the predicted sparse IBS 7 as constraints, we get dexterous grasp poses G via optimization algorithm Seection 3.3
3.1 Contact and Collision Aware IBS for Dexterous Grasping.
IBS (17] is the Voronoi diagram between close 3D geometric objects. Inspired by the efficiency of IBS in describing the spatial relationship between 3D objects and its successful application in dexterous hand manipulation [32 33),
we adapt IBS represent the geometric relationship between the dexterous hand and the environment as well as the object when a successful grasping state is achieved. We generate the sparse IBS
simulator for training, as shown in Figure 3 Specifically, for each dexterous grasp pose g, we define a canonical space centered at the grasp seed point with the rotation R of the wrist pose defined with a resolution of n, where the last three dimensions are:
[R as the direction. The sparse IBS I e ==xXxXx3 is
Occupancy of the IBS surface, which is if the voxel in I in IBS surface, otherwise it is -1.
Occupancy of contact points between the thumb and the target object, which is 1 if the voxel in I is a contact point between the thumb and the object, otherwise it is -1.
Occupancy of contact points between other fingers and the object, which is 1 if the voxel in I is contact point between other fingers and the object, otherwise it is -1.
The sparse IBS encodes the geometric relationship between the hand corresponding to a successful grasp pose g and the environment, which includes not only the configuration of the hand but also the

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Target Object
Canonical Space
IBS with Contact Points
Seed Point
Cluttered Scene
Canonical Space Definition
Contact and Collision Aware IBS
Creation of the sparse IBS for dexterous grasping. Given a cluttered seene, we first generate the grasp pose g using optimization algorithm. Then, we canonicalize and crop the scene point cloud P to obtain the canonicalized point cloud p Finally, we compute the sparse IBS based the canonicalized point cloud.
Figure 3:
contact relationship between fingers and objects, and specifies safety bound to ensure collision-free with environments.
3.2 Conditional IBS Generation.
In this section, our goal to generate the intermediate representation IBS to provide constraints for the subsequent grasp pose optimization stage. Specifically, shown in Figure given single- observed point cloud P. we first predict the wrist pose of the dexterous hand. Then, given and P. generate multiple IBS candidates {I,)=-, where is the number of candidates. Finally, rank the IBS candidates and select the optimal one I the final intermediate representation.
Wrist Pose Estimation. We use the same structure as DexGraspNet2.0 [ First, given the scene point cloud P Nx3 where N is the number of points, extract point-wise features {8t {xt Finally, after ranking and FPS sampling, we obtain the final set of grasp seed points {p', 1M where is the number of sampled points. For each grasp seed point Ps. condition
F = W using ResUNet14 (34) and predict point-wise graspness S
the corresponding point
feature f. and use denoising diffusion model [35] to directly model the joint probability distribution
f(T),) in Euclidean space and obtain the denoised wrist pose T with reverse ODE process.
Occupancy Denoising
3D Unet
Nolsy Sparse IBS (403)
Denoised Sparse IBS
Hierarchical Condition
3D Conv
53
10a
Scene Condition (403)
20
Figure IBS generation. We train conditional occupancy-diffusion model to model the conditional probability distribution P(I[P*), where P is the canonicalized and voxelized point cloud. The voxel-level alignment provides hierarchical conditions during the denoising process.
IBS Candidates Generation. As shown in Figure 4 given the selected grasp seed point ps and the corresponding wrist pose T, we define a canonical space with ys as the coordinate origin and the rotation of T as the direction. We canonicalize and voxelize the original observed point cloud p
5

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to obtain p E R= where n is the voxelization resolution. The above operations simplify the modeling of the probability distribution p(Z]P: T) to modeling p(Z)P-), making the feature space more compact and reducing the training difficulty of the network. Specifically, inspired by the dominance of denoising diffusion models in 3D generation [36 35 we model p(Z]P") based on an accupancy-diffusio model [35]. The I E IаaxxxXx has the same resolution as PP", and this voxel-level alignment provides hierarchical conditions during the generation of IBS, improving both the generation quality and efficiency. As shown in Figure 4 for both occupancy network Q2 and point cloud network Sz,. we use 3D UNet as the backbone network. UNet has 4 levels: 408, 20 10 53, with feature dimensions of 32, 64, 128, and 256 respectively at each level. We achieve voxel-level condition guidance by concatenating the point cloud features at corresponding levels to the occupancy features. The training of the diffusion network uses the following loss:
LTo E...N... (,, -Toli
where c and To are the data sample and I, is the noisy sample at time step t. N is the Gaussian distribution, and U/ is the uniform distribution.
IBS Ranking. Considering that the sampling process has a certain probability of sampling in low- density regions, we sample multiple IBS candidates {ī, ) from the estimated distribution p(Z) (P). where is the number of samples. Furthermore, calculate the force closure score {2₂ }.m for candidates based on the contact points and directions of the thumb and other fingers, and obtain ranked sequence of IBS candidates Loo, > Lo, >
Irmm where:
Lo: Doz Dta
(2)
Finally, we select the top-ranked IBS as the final intermediate representation Î.
3.3 Grasp Pose Optimization with IBS Constraints.
Grasp Pose Optimization. Given the predicted IBS Σ, generate the dexterous grasp poses G through an optimization algorithm. Specifically, we use a gradient- optimization algorithm minimize the energy function E, which consists of four parts: 1) joint limits energy Ej. 2) self- penetration energy Egp- 3) contact energy Eg, and 4) collision energy Ep. We obtain the dexterous grasp poses G by minimizing the energy function. Specifically:
E, is used to limit the joint angles within a preset range, defined as:
d
E,
(max(0; - 0mmax 0) + max(0min - o,, d
(3)
where 0, the angle the i-th joint, 0max and @min are the maximum and minimum angles of the i-th joint, respectively, and d is the number of joints.
Exp used limit the self- self-penetration of the hand, defined as:
Exp
Σ Σ lo dl max(6 d(p,
(4)
PP
PÊP, PEP
where Py. is the set of points on the hand, d(-) calculates the Euclidean distance between two points, and is the safety distance for self-penetration of the hand.
E, is used to constrain the contact relationship between fingers and objects, defined as:
E
Ph
max
0, (0.-
(0.(11- [1--P]
1p
p p" prll - ))
(5)
PEP
where PA the set of points on the hand, p is the point on the hand, p² is the nearest point in the IBS point set Pz corresponding p, and n is the normal vector of the estimated IBS surface at point p".

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Eg is used to constrain the optimization of the hand in the safety space without collision with the environment, defined as;
Es
min Ip" -pl Pil PtEPt PEP:
Σ
Σ P*EP.
min
1p'-
C33
E pEP.,P
min P EPPPP
1p'2
Pol
PEPo
P: P
(6)
where Pt and P are the sets of points the IBS that are in contact with the thumb and other fingers, respectively, P; and Po are the sets of points on the thumb and other fingers, respectively. 0r 02. and 0g are hyperparameters used to balance the weights between different energy terms.
Overall, the final energy function is:
E1 + Ax + A3 A4
where A1, A2. Ag. and X the weights of each energy term.
Grasp Pose Ranking. Given the inherent uncertainties in the optimization process, we simulta- neously optimize multiple grasp configurations G based on a predicted sparse IBS Σ. We record the optimization residuals {Eg, ) where & denotes the number of optimization trials. The grasp configurations 9 are then ranked according to their residuals, and the configuration with the minimal residual is selected as the optimal grasp pose g-
4 Experiments
4.1
Experimental Setup
Datasets. We use the same object datasets and simulation environments as DexGraspNet 2.0 []]
The object datasets consist of 60 training objects from CraspNetlBillion [8] and 1259 testing objects from GraspNet Billion and ShapeNet [37]. We sample 100 scenes from the 7600 training scenes of DexGraspNet2 [I]] for the training of the IBS generation module. And we use the full 670 scenes from the testing set of DexGraspNet2 [[]] for the testing. The testing scenes are categorized into
three density levels: loose, random, and dense.
Baselines. We compare our method with the following dexterous grasp pose prediction methods.
DexGraspNet2.00111 An end-to-end diffusion-based pipeline for grasp pose generation in clut- tered scenes, directly mapping 3D point cloud observations to grasp poses.
HGC-Net A regression-based model for direct pose prediction cluttered scenes.
ISAGrasp 38]
This baseline is originally designed for single-object grasping using an end-to-end
regression-basee model. Following the adaptation strategy in DexGraspNe12.0 [11], we extend ISAGrasp to cluttered scenes.
two-stage, single-object grasping framework that needs complete point clouds for the second-stage optimization, which is impractical in cluttered scenes; we therefore adapt it for cluttered scenes as DexGrasNNet2.0 0 does and remove optimization stage. This two-stage framework is originally designed for single-object grasping and requires complete point clouds for second-stage optimization, which is not feasible in cluttered scenes. Therefore, following DexGraspNe1200 [II,, adapt GraspTTA for cluttered scenes and omit the optimization stage.
GraspTTA[[4]:
Evaluation in Simulation. We report the Success Rate, following the same evaluation protocol as DexGraspNet2.00 [1I. In the simulation, evaluate from two perspectives:
Object and Scene Generalization. We evaluate the performance of our method following the same protocol DexGraspNer2.0 [II]
Cross-Emil Eooodmembd Generalization. Different from other baselines, our method can directly zero-shot generalize to unseen embodiments, thanks to the proposed universal intermediate rep- resentation. We evaluate our method using an unseen dexterous hand (Allegro) in zero-shot manner.
we conduct real-world dexterous grasping experiments in clutter scenes, using Flexiv Rizon-4 robot arm equipped with Leap Hand [39] as the end-effector.
Real-world Setup. As shown Figure 5
7

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A third-person view RealSense D415 camera employed for perception. We selected 30 ob- jects with diverse shapes, sizes, and materials, as depicted Figure Following the simulation experiment setup, we also assess grasping under varying levels of clutter density, covering the en- tire object dataset across 5 cluttered scenes with 4 to objects per scene, as illustrated Figure 6 For each scene, the policy continues grasping until two consecutive failures occur. For each grasp execution, as in the simulation: pre-grasp, grasp, squeeze fingers, and lift. We report the Success Rate as the number successfully grasped objects divided by the total number of attempts.
Flexiv Rizon-4
RealSense
D415
Contalner
Leap Hand
Horlspace Sppree
follow the same sequence
Figure 5: Real-world experiment setup.
38
Figure 6: Real-world object datasets and evaluated cluttered scenes. The left image shows the objects used in real-world testing. The right image shows the layouts of objects in five different test scenes, with 4 to objects per scene.
GraspNet- 1Billion
ShapeNet Random
Method
Ratio
Dense
Random
Loose
Dense
Loose
HGC-Net
10
1
46.0
37.8
26.7
46.4
44.8
30.4
GraspTTA
[1
1
62.5
54.1
42.8
56.6
57.8
46.4
ISAGrasp 38
1
63,4
60.7
51.4
64.0
56.3
52.7
DexGraspNet2.00
1/1000
83.3
79.5
73.9
81.5
77.1
73.5
Ours
1/1000
86.5
85.5
80.1
79.3
77.7
75.7
Ours (Allegro)
1/1000
77.6
75.0
74.9
75.7
76.6
73.0
Table 1: Comparison results. indicates the results are from [11] Ratio refers to the ratio of the number of grasps for training compared to the whole dataset. Each Dense scene contains 8-11 objects, and each Random scene contains 1-10 objects, obtained by deleting objects from Dense scenes, and each Loose scene contains 1-2 objects. Allegro is the result evaluated with the Allegro, and others use the Leap hand for evaluation. We report only method, trained Leap Hand and tested on Allegro Hand, since other methods do not demonstrate cross-hard hand generalization.
4.2 Simulation Results
Comparison with Baselines. As shown in Table [ the regression- methods HGC-Net and ISAGrasp struggle with the complex dexterous grasp pose distribution. The generative-based method GraspTTA performs even worse, likely due to the absence of a second-stage optimization step. DexGraspNer2.0 achieves higher success rate by leveraging the diffusion model. However, its direct

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end-to-end mapping is susceptible to challenges associated with non- non-linear mapping and sensitivity to physical constraints. In contrast, our two-stage approach demonstrates superior performance. Furthermore, to evaluate robustness, we report success rates with standard deviations over 20 random seeds. The consistently small deviations indicate stable performance across initializations.
Generalization. Since our proposed sparse IBS representation is embodiment- agnostic, we directly use the same generated sparse IBS that was used to evaluate the Leap Hand to optimize the Allegro Hand grasp pose. As shown in Table [ the Allegro Hand also achieves comparable results the Leap Hand, demonstrating the universality of our proposed representation.
4.3 Real-world Results
As shown in Table2 our method achieves average grasp success rate of 93.3%, significantly surpassing the baseline at 83.9%. While the two methods exhibit comparable performance on medium-scale objects, objects, in line with the simulation analyses reported in Table7 This improvement can be attributed to the effectiveness of the IBS. By integrating IBS into the optimization, collisions with the table and surrounding objects, yielding safer grasps, which is particularly critical in densely cluttered scenes. The observed real- world performance underscores the practical applicability of our approach for deployment in real robotic systems.
approach demonstrates markedly greater robustness for small and flat
method explicitly avoids
Method
Scene #1
Scene
Scene #3
Scene #4
Scene #5
Overall
DexGraspNer2.0 0 (II]
100.0% (4/4)
50.0% (2/4)
71. 71.4% 5/7)
100.0% (7/7)
88.9 %. (8/9)
83.9% (26/31)
CADGrasp (Ours)
100.0% (4/4)
100.0% (5/5)
85.7% (6/7)
100% (7/7)
88.9 9% (8/9)
93.8% (30/32)
Table 2:
Real-world results. We evaluated 30 objects across 5 real-world cluttered scenes. In each scene, the policy continues attempting to grasp until two consecutive failures occur. The number to the left of //' indicates the number of successful grasps, while the number to the right indicates the total number of grasp attempts.
4.4 Computational Efficiency
We assess the inference efficiency of our method on a single NVIDIA RTX 4090 GPU by averaging 50 independent runs to suppress stochastic fluc- tuations in the sampling based optimization. detailed breakdown of the runtime is reported in Table 3 The end-to-end time to generate single grasp is 6.51 average, measured without any musk-specifie engineering optimizations. While not real-time, our method delivers substantial latency advantage over existing state-of-the-arr two-stage approaches (see Table 4 We anticipate that real-time feasibility can be pushed closer to real-time by
Module
Stage
Time (s)
Wrist Pose Estimation
1
1.38
IBS Generation
1
1.39
IBS Ranking Grasp Opt. & Rk.
1
0.71
2
3.03
Total
6.51
Table Runtime breakdown.
adopting faster samplers
40 and by increasing parallelism the optimization stage.
Method
Intermediate Rep.
Partial Obs
Cluttered
Runtime (s)
GraspTTA
Contact Map
X
x
43.23
UniGrasp
3 Contact Points
X
X
9.33
GenDexGrasp
Contact Map
X
X
16.42
CADGrasp (Ours)
Sparse IBS
V
6.51
Table 4: Runtime and capability comparison with representative two-stage methods. Our approach is faster and enables dexterous grasping under partial observations in cluttered scenes, both of which are essential for real-world deployment.

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4.5 Ablation Study.
We conduct comprehensive ablation studies on the dense scenes of GraspNet- -illlto test set to validate our design choices.
Module Interaction. first analyze interaction effects between key modules in Table The results show that removing any component leads performance drop, with the full model achiev- ing the best success rate 86.5%. Notably, decomposing the contact representation ("Decompose' for the thumb and other fingers provides substantial improvement (e.g.. 86.5% vs. 56.1% without it). This confirms the critical role the thumb dexterous grasping [42] and validates our design model it separately. Both the IBS and Grasp Pose Ranking modules are also proven effective.
IBS Ranking
Grasp Pose Ranking
Decompose
Success Rate (%)
73.1
53.8
83.9
X
26.9
X
75.7
56.1
86.5
Table 5: Ablation study. The interaction effects between key design elements in the dense-scene subset from the GraspNet-1Biin 1Billon test set.
Voxel Resolution. Next, we study the effect of voxel resolution in Table 6 of 5mm balance accuracy and efficiency. Finer resolutions (2.5mm) offer only marginal gains while significantly increasing memory usage, whereas coarser resolutions (10mm) lead to clear performance drop.
We select a voxel size
Object Size. Finally, analyze the performance objects of different sizes in Table 7 The results indicate that the success rate for small objects is indeed lower. However, combined with the findings in Table attribute this the inherently higher precision required for grasping small objects, rather than insufficient voxel resolution.
Voxel Size (mm)
Memory (GB)
SR (%)
Volume Range (m²)
SR (%)
Prop. (%)
2.5
2.12
81.2
(0. 0.00025)
77.0
33.0
0.84
81.0
[0.00025, 0.0005)
78.8
36.9
0.36
72.4
[0.0005. 0.001)
82.6
22.3
[0.001, 0.0015)
82.3
6.0
Table Ablation on voxel resolution. Bolded items denote selected hyperparameters, balanc- ing computational efficiency and performance.
[0.0015, +00)
91.5
1.9
Table 7: Success rate vs. object volume.
5 Conclusion
In this paper, we enhance general dexterous grasp pose prediction in cluttered scenes by proposing two-stage framework. The first stage predicts our proposed compact, scene-decoupled, contact- and collision-aware intermediate representation, which serves as the target for the second-star -ttage optimization. To ensure the quality of the predicted representation, diffusion model with voxel-level conditional guidance and force closure filtering. To generate stable and collision-free grasp poses, we further propose several energy functions and ranking strategies for pose optimization. Comprehensive simulation and real-world experiments demonstrate the effectiveness of our method.
introduce occupancy-
Limitations. Although our method demonstrates better generalization, it primarily struggles with small objects, which could be addressed by incorporating more small objects during training. Addi- tionally, the current second-sta -acged-stage optimization time-consuming due to the use of the DDPM 43 for sampling IBS. This can be further optimized by adopting the DDIM 44
10

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Acknowledgments and Disclosure of Funding
This work is supported by the National Natural Science Foundation of China General Program
(Project ID: 62376006), National Youth Talent Support Program (Project ID: 820080008
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Appendix
In this appendix, we first describe the implementation details in Section A followed by additional experimental results Section
A
Implementation Details
A.1 Training Details
train our model on 8 NVIDIA RTX 4090 GPUs with a batch size of 64. We use the Adam optimizer with learning rate of 6e-5 and trained the model for 130 epochs. The training procedure takes about days.
A.2 Hyperparameters
The hyperparameters employed in our experiments are detailed in Table 8 The size of IBS volume is configured to 0.2 m x 0.2m x 0.2 m, adequately encompassing the interaction space between the dexterous hand and the object, while remaining sufficiently compact focus the critical local grasping region. The resolution of the IBS volume is set to 40 x 40 x 40, striking a balance between computational efficiency and the accuracy of the IBS surface representation. Both the IBS sampling and grasp pose optimization processes are executed concurrently, with the number of IBS candidates and grasp poses each limited The weights the contact energy E are meticulously adjusted to balance the contacts between the object and the thumb, as well as the other fingers, whereas the weights in the overall energy E are calibrated to harmonize the various energy terms. The hyperparameter for denoising timesteps is adopted from 335
Hyperparameter
Value
IBS Volume Size
0.2m x 0.2m x 0.2m
IBS Resolution (n)
40 x 40 x 40
Number of IBS Candidates (m)
Number of Grasp Poses (A:)
Weights in Contact Energy Ea (o1, 02 02)
80, 100, 2
Weights in Overall Energy E (A1, X2, X3, X4)
5, 1, 1000, 50
Denoising Timesteps
Table 8: Hyperparameters used in our experiments.
B
More Results
In this section, we provide additional results our method to demonstrate the effectiveness, robust- scalability, and generalization ability of our method.
B.1 Qualitative Results
In this section, we present additional qualitative evaluations of our proposed method. Figure 7 showcases the perception results of method in both simulated and real-world environments. The results clearly demonstrate that our approach successfully discerns the sparse IBS volume pertinent to a success grasp pose from single-view point cloud in cluttered settings. effectively differentiates the contact regions of the thumb and other fingers with the object. Furthermore, the second-stage optimization, guided by Sparse IBS constraints, adaptly refines collision-free and plausible grasp poses. Notably, the adoption of point cloud representation minimizes the sim-to-real gap, ensuring that our method can generalize to real-world settings without additional training, thereby achieving robust dexterous grasping capabilities.
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Figure 7: Qualitative results of our method in simulated (upper panel) and real-world environments (lower panel). From left to right: the initial single-view point cloud input, the sparse IBS prediction from the initial stage, and the optimized grasp pose from the subsequent stage. The purple areas indicate thumb contact, red areas denote contact by other fingers, and green areas represent non- contact regions the IBS surface.
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B.2 Grasp Diversity Analysis
We analyze the distribution of joint configurations for all predicted grasp poses by CADGrasp and DexGraspNet2.0 [T]] within the GraspNet- IB loose scenarios [II]. This analysis aimed to compare the diversity of grasp poses generated by the two methods. Taking the thumb as example. Figure illustrates that our method can generate diverse grasps with significantly higher dexterity compared Dex DexGraspNet2.0. O. This highlights our approach's capability to produce wider range of effective grasp configurations, enhancing its applicability in complex scenarios.
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Figure 8: Grasp diversity analysis of CADGrasp and DexGraspNet2.00 within the GraspNet- IB loose scenarios. The histogram illustrates the distribution of joint configurations for the thumb.
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Figure 9: Penetration depth analysis of predicted grasp poses by CADGrasp and DexGraspNet2.0 within the GraspNet- 1B loose scenarios. The histogram illustrates the distribution of maximal penetration depths.
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B.3 Grasp Quality Analysis
In evaluating grasp quality, the proximity and penetration between the predicted grasp pose and the object serve crucial indicators. We analyzed the maximal penetration depth (in cm) of all predicted grasp poses by CADGrasp and DexGraspNet2.00 within the GraspNet-1P IB loose scenarios. This metric is defined as the maximal penetration depth from the object point cloud to the hand meshes. As illustrated in Figure our method demonstrates concentration of penetration depths near the object's s surface (penetration depth 0), attributed to the constraints imposed by the IBS Surface and contact points. This further underscores the superiority of our approach in achieving precise and effective grasping.
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Reviews:
[{"review_id": "rKpkrdKEch", "reviewer": "Reviewer_vrT5", "summary": "This paper presents CADGrasp, a two-stage framework for dexterous grasping in cluttered scenes using single-view point cloud inputs. The approach introduces sparse IBS (Interaction Bisector Surface) as a contact and collision aware intermediate representation that serves as an optimization target. The first stage uses an occupancy-diffusion model to predict sparse IBS from point clouds, while the second stage optimizes grasp poses using energy functions based on the predicted IBS.", "questions": "**Representation Design Justification**: Could you provide more theoretical or empirical analysis of why the specific three-channel design for sparse IBS is optimal? Have you experimented with alternative representations?\n\n**Computational Efficiency and Scalability**: Can you provide detailed timing analysis comparing your method to baselines? Given the two-stage nature with diffusion sampling and optimization, how does this scale to real-time applications? \n\n**Generalization Boundaries**: While cross-embodiment results are impressive, what are the limits of this generalization? How sensitive is the method to significant differences in hand morphology, scale, or degrees of freedom? Could you provide analysis of failure cases where the representation breaks down?", "limitations": "Yes", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**Strength**\n\n**Novel Intermediate Representation**: The sparse IBS representation is a compelling contribution that addresses the core challenge of cluttered scene grasping. By encoding geometric relationships and contact information in a scene-decoupled manner, it enables optimization without requiring complete object geometry—a significant advantage over existing two-stage methods.\n\n**Strong Experimental Validation**: The paper provides comprehensive evaluation across 670 diverse cluttered scenes, demonstrating consistent improvements over strong baselines. The real-world experiments with 93.3% success rate provide convincing evidence of practical applicability.\n\n**Cross-Embodiment Generalization**: The method's ability to generalize across different hand embodiments without retraining is noteworthy and demonstrates the universality of the proposed representation.\n\n**Technical Soundness**: The two-stage framework is well-motivated and technically sound. The occupancy-diffusion model with voxel-level conditional guidance is appropriately designed for the high-dimensional IBS prediction task.\n\n**Weaknesses**\n\n**Limited Theoretical Analysis**: The paper lacks theoretical justification for why sparse IBS should be more effective than alternative representations. While empirical results are strong, deeper analysis of the representation's properties would strengthen the contribution.\n\n**Computational Efficiency Concerns**: The method requires DDPM sampling for IBS generation followed by gradient-based optimization, which the authors acknowledge is time-consuming. The paper doesn't provide detailed timing comparisons or discuss scalability to real-time applications.\n\n**Incomplete Ablation Studies**: While the paper includes ablation studies, some design choices lack sufficient justification. For example, the choice of three channels for sparse IBS (surface occupancy, thumb contact, other finger contact) could benefit from more systematic analysis.", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely appreciate your constructive feedback and are excited that you find our work \"novel\", \"strong experimental validation\", and \"technical soundness\"! We want to clarify your concerns and questions in the following.\n\n> **Q1: Limited Theoretical Analysis: The paper lacks a theoretical justification for why sparse IBS should be more effective than alternative representations. While empirical results are strong, a deeper analysis of the representation's properties would strengthen the contribution.**\n\n**A1:** We thank you for pointing out the importance of deeper theoretical analysis. We provide further clarification from two perspectives:\n- **Compared to one-stage direct grasp pose prediction:** Directly mapping 3D point cloud observations to grasp poses is challenging due to the highly non-linear nature of the mapping from 3D point cloud space to pose space, resulting in low sample efficiency[1][2]. Furthermore, such representations are usually embodiment-specific, limiting cross-embodiment generalization. Our sparse IBS representation is spatially aligned with the 3D point cloud, improving sample efficiency and enabling our two-stage approach to achieve cross-embodiment generalization, as shown in our experiments.\n- **Compared to two-stage methods that optimize from predicted contact map:** Approaches using the contact map as intermediate representations generally assume that complete point cloud observations are available, which is unrealistic in real-world settings with partial observation. Our proposed sparse IBS representation, in contrast, does not rely on this assumption. As shown in both the quantitative experiments in the manuscript and qualitative visualizations in the supplementary material, our method achieves robust dexterous grasp pose prediction using only single-view partial point cloud inputs, making it suitable for complex, real-world tasks.\n\nWe appreciate your suggestions and agree that further theoretical insights would strengthen our work. We will include additional theoretical analysis and visualizations in the final version.\n\n> **Q2: Computational Efficiency Concerns: The method requires DDPM sampling for IBS generation followed by gradient-based optimization, which the authors acknowledge is time-consuming. The paper doesn't provide detailed timing comparisons or discuss scalability to real-time applications.**\n\n**A2:** We appreciate your comment. We provide the following clarifications and will include a discussion of this in the final version.\n- **Runtimes:** \nWe thank the reviewers for highlighting the importance of computational efficiency. We performed 50 inference runs on a single NVIDIA RTX 4090 GPU and report the average runtime for each stage of our method in Table 1 below. Though our method does not meet real-time requirements, we believe this is reasonable given its high performance and the potential of the proposed novel representation for cross-embodiment generalization. We would also like to note that the reported runtime is without engineering-level optimization—real-time feasibility can be further improved by using faster samplers[3][4] and increasing parallelization in the second stage.\n    \n    **Table 1: Average runtime per stage of our method (NVIDIA RTX 4090)**\n    | Module                            | Stage                       | Average Runtime (seconds)   |\n    |-----------------------------------|-----------------------------|-----------------------------|\n    | Wrist Pose Estimation             | 1                           | 1.38                        |\n    | Diffusion-based IBS Generation    | 1                           | 1.39                        |\n    | IBS Ranking                       | 1                           | 0.71                        |\n    | Grasp Pose Optimization & Ranking | 2                           | 3.03                        |\n    | Total                             | -                           | 6.51                        |\n\n- **Comparison with Baselines:**\nWe also compare overall runtime with mainstream two-stage methods in Table 2. Our approach not only provides effective representations for complex real-world grasping but also achieves better runtime than baselines, even without inference-time optimizations.\n\n    **Table 2: Comparison with existing two-stage methods**\n    | Method              | Intermediate Representation | Partial Observation | Cluttered Scene | Runtime (seconds)           |\n    |---------------------|-----------------------------|---------------------|-----------------|-----------------------------|\n    | GraspTTA            | Contact Map                 | ✗                   | ✗               | 43.23                       |\n    | UniGrasp            | 3 Contact Points            | ✗                   | ✗               | 9.33                        |\n    | GenDexGrasp         | Contact Map                 | ✗                   | ✗               | 16.42                       |\n    | **CADGrasp (Ours)** | **Sparse IBS**              | **✓**               | **✓**           | **6.51**                    |\n\n\n> **Q3: Incomplete Ablation Studies: While the paper includes ablation studies, some design choices lack sufficient justification. For example, the choice of three channels for sparse IBS (surface occupancy, thumb contact, other finger contact) could benefit from more systematic analysis.**\n\n**A3:** We thank you for your suggestions regarding more comprehensive ablation studies. In response, we conducted additional experiments to analyze the interaction effects between different modules and the contribution of each design choice (see Table 3 below). The results show that decomposed modeling of thumb and other finger contacts with the object leads to a substantial improvement in grasp performance, confirming that the thumb plays a crucial role in dexterous grasping [5]. Additionally, both the IBS ranking and grasp pose ranking modules further enhance overall grasp performance. We will update Table 3 in the final version and provide further discussion in the manuscript.\n\n**Table 3: Ablation Study on Interaction Effects and Design Choices** \n| IBS Ranking | Grasp Pose Ranking | Decompose | SR (%) on Dense Scenes of GraspNet-1B |\n|-------------|--------------------|-----------|------------------|\n| ✗           | ✗                  | ✓         | 73.1             |\n| ✗           | ✓                  | ✗         | 53.8             |\n| ✗           | ✓                  | ✓         | 83.9             |\n| ✓           | ✗                  | ✗         | 26.9             |\n| ✓           | ✗                  | ✓         | 75.7             |\n| ✓           | ✓                  | ✗         | 56.1             |\n| ✓           | ✓                  | ✓         | 86.5             |\n\n> **Q4: Generalization Boundaries: While cross-embodiment results are impressive, what are the limits of this generalization? How sensitive is the method to significant differences in hand morphology, scale, or degrees of freedom? Could you provide an analysis of failure cases where the representation breaks down?**\n\n**A4:** We thank you for this insightful question. We agree that understanding the boundaries of cross-embodiment generalization is an important direction for future work. While our experiments demonstrate that the proposed sparse IBS representation enables a degree of cross-embodiment generalization, we do not over-claim this capability in the paper. As currently designed, the sparse IBS generation stage does not condition on the specific hand morphology, which means that generalization is primarily observed between dexterous hands with similar shape, scale, and degrees of freedom. For example, if the target hand is much smaller, when the fingertips satisfy the predicted contact constraints, the palm will collide with the object due to the shorter fingers. Similarly, hands with fewer degrees of freedom or very different structures cannot achieve the predicted contacts and will face kinematic infeasibility. Incorporating hand morphology and kinematics as part of the conditioning input for sparse IBS generation could potentially address these limitations and enable broader generalization—a direction we plan to explore in future work. We will discuss these boundaries and potential improvements more explicitly in the final version.\n\n\n[1] Haoming Li, et al. Contact2Grasp: 3D Grasp Synthesis via Hand-Object Contact Constraint. IJCAI 2023.\n\n[2] Hao-Shu Fang, et al. AnyDexGrasp: Learning General Dexterous Grasping for Different Hands with Human-level Efficiency. ArXiv 2025.\n\n[3] Salimans T, et al. Progressive distillation for fast sampling of diffusion models. ICLR 2022.\n\n[4] Song Y, et al. Consistency models. ICML 2023.\n\n[5] Thomas Feix, et al. The grasp taxonomy of human grasp types. IEEE Transactions on human-machine systems, 2015."}, {"review_id": "wrsVsusVPV", "reviewer": "Reviewer_H1AQ", "summary": "CADGrasp tackles dexterous grasping in highly cluttered scenes by splitting the problem into two stages. Stage 1 is representation prediction. A conditional occupancy-diffusion model, guided voxel-wise by the canonicalised scene point cloud, predicts a sparse Interaction-Bisector Surface (IBS) that is both contact-aware and collision-aware. Stage 2 is for pose optimization. Several energy terms are minimised to recover a full 25-DoF hand pose that realises the predicted sparse IBS. Extensive experiments on 670 simulated cluttered scenes (≈1300 objects) and on five real scenes with 30 diverse household objects show higher success rates than regression and diffusion baselines.", "questions": "1. Could the authors report standard deviations over multiple random scene orderings?\n2. Could the authors provide average test time for Stage 1 sampling (per IBS candidate) and Stage 2 optimisation (per initialisation) on the hardware used. if possible, the authors are also encouraged to discuss feasibility for real-time grasp planning.\n3. Will the implementation and trained models be open-sourced upon acceptance, and if so under which licence?", "limitations": "The paper discusses the limitations of the work performed by the authors yet not directly addresses it.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**Strengths**\n1. IBS is a conceptually well-motivated intermediate representation. By predicting a sparse IBS that is decoupled from complete scene geometry, the method avoids the brittle requirement for full object reconstruction.\n2. CADGrasp outperforms state-of-the-art baselines across loose, random and dense clutter, and maintains competitive accuracy on unseen ShapeNet objects.\n3. The provided supplementary video organizes the contents clearly.\n4. The paper explicitly explores the benefit of each design choice.\n\n**Weakness**\n1. All success-rate tables should provide variance across random seed.\n2. The diffusion sampling plus gradient-based optimisation could be slow; yet runtimes per grasp are absent.\n3. Comparison to some recent contact-based two-stage graspers in clutter may be ommited, such as UniDexGrasp++ and UniDexGrasp in cluttered settings.\n4. Some typos are found. For example, in line 296, the \".\" should be \",\".", "was_revised": true, "final_justification": "I will maintain the original score.", "rebuttal": "We sincerely appreciate your constructive feedback and are excited that you find our work \"well-motivated\", \"competitive\", and \"clear\"! We want to clarify your concerns and questions in the following.\n> **Q1: All success-rate tables should provide variance across random seed.**\n\n**A1:** We thank you for your comment, and we agree that variance is important for evaluating the robustness of our method. As suggested, we conducted each experiment with 20 different random seeds and report the success rates together with the corresponding standard deviations in Table 1 below. The observed standard deviations are consistently small across different datasets and scene types, which indicates that our method exhibits stable performance across random initializations. Note that the reported variance captures both the inherent randomness of our method and the variability introduced by the simulation environment, further demonstrating the robustness of our approach.\n\n**Table 1: Success rates of CADGrasp on different datasets and scene types**\n| Dataset     | Scene Type   | Success Rate (%) ± Std. Dev. |\n|-------------|--------------|------------------------------|\n| GraspNet-1B | Dense        | 86.0 ± 2.2                   |\n| GraspNet-1B | Random       | 85.0 ± 3.1                   |\n| GraspNet-1B | Loose        | 80.1 ± 2.1                   |\n| ShapeNet    | Dense        | 78.6 ± 2.5                   |\n| ShapeNet    | Random       | 77.0 ± 3.8                   |\n| ShapeNet    | Loose        | 75.0 ± 4.0                   |\n\n\n\n> **Q2: The diffusion sampling plus gradient-based optimisation could be slow; yet runtimes per grasp are absent.**\n\n**A2:** We thank you for your comment. We provide the following clarifications and will include a discussion of this part in the final version. \n\nWe performed 50 inference runs on a single NVIDIA RTX 4090 GPU and report the average runtime for each stage of our method in Table 2 below. Although our method does not meet real-time requirements, we believe this is reasonable given its high performance and the potential of the proposed novel representation for cross-embodiment generalization. We also note that these reported runtimes are obtained without any engineering-level optimization—real-time feasibility can be further improved by using faster samplers [1][2] and by increasing parallelization in the second stage. Furthermore, as shown in Table 3, even without engineering acceleration, our method outperforms the speed of existing SOTA two-stage methods and provides effective representations for complex real-world grasping tasks.\n\n**Table 2: Average runtime per stage of our method (NVIDIA RTX 4090)**\n| Module                                               | Stage                       | Average Runtime (seconds)   |\n|------------------------------------------------------|-----------------------------|-----------------------------|\n| Wrist Pose Estimation                                | 1                           | 1.38                        |\n| Diffusion-based IBS Generation (5 Candidates)        | 1                           | 1.39                        |\n| IBS Ranking                                          | 1                           | 0.71                        |\n| Grasp Pose Optimization (5 Initialisation) & Ranking | 2                           | 3.03                        |\n| Total                                                | -                           | 6.51                        |\n\n**Table 3: Comparison with existing two-stage methods**\n| Method              | Intermediate Representation | Partial Observation | Cluttered Scene | Runtime (seconds)           |\n|---------------------|-----------------------------|---------------------|-----------------|-----------------------------|\n| GraspTTA            | Contact Map                 | ✗                   | ✗               | 43.23                       |\n| UniGrasp            | 3 Contact Points            | ✗                   | ✗               | 9.33                        |\n| GenDexGrasp         | Contact Map                 | ✗                   | ✗               | 16.42                       |\n| **CADGrasp (Ours)** | **Sparse IBS**              | **✓**               | **✓**           | **6.51**                    |\n\n\n> **Q3: Comparison to some recent contact-based two-stage graspers in clutter may be omitted, such as UniDexGrasp++ and UniDexGrasp in cluttered settings.**\n\n**A3:** We thank you for your comment. We would like to clarify the following points:\n- We would like to clarify that UniDexGrasp and UniDexGrasp++ focus on single-object grasping and do not support grasping in cluttered scenes. Furthermore, these approaches have not been extended to the dexterous grasping in the real-world setting that is the focus of our work.\n\n- Existing contact-based two-stage methods, which first predict a contact map and then optimize for a grasp pose, generally rely on two strong assumptions: (1) access to full point clouds, and (2) single-object grasping. Both assumptions are unrealistic in the context of general real-world grasping in cluttered scenes. Nevertheless, following the modifications suggested by DexGraspNet 2.0, we adapted these two-stage baselines (e.g., GraspTTA in Table 1 of the manuscript) for a fair comparison under our proposed setting. Our method still outperforms these baselines in both performance and speed. Please refer to Table 2 in A2 for detailed runtime results. This advantage largely stems from our proposed sparse IBS representation, which does not require a complete scene point cloud.\n\n> **Q4: Some typos are found. For example, in line 296, the \".\" should be \",\".**\n\n**A4:** We appreciate your careful reading of our manuscript. We will correct the typos in the final version.\n\n> **Q5: Will the implementation and trained models be open-sourced upon acceptance, and if so, under which licence?**\n\n**A5:** Yes, we plan to open-source the implementation and trained models upon acceptance to facilitate reproducibility and further research in this area. They will be released under the MIT License.\n\n[1] Salimans T, et al. Progressive distillation for fast sampling of diffusion models. ICLR 2022.\n\n[2] Song Y, et al. Consistency models. ICML 2023."}, {"review_id": "TKCMVLIOhr", "reviewer": "Reviewer_s4Sp", "summary": "This paper proposes CADGrasp, a diffusion-based framework for dexterous grasp synthesis that explicitly integrates contact awareness and collision avoidance. The method is trained on a large-scale synthetic dataset and evaluated on both simulated and real-world grasping tasks. CADGrasp outperforms baseline methods in simulation.", "questions": "1. Could you provide more comprehensive ablations, including “with IBS ranking-only”, “with decompose-only”, and “with grasp pose ranking-only” settings to clarify the joint impact of each component?\n2. Can the number of trials in real-world experiments be standardized across all methods to ensure fair comparison?\n3. How robust is CADGrasp to noisy supervision during training, especially in contact or collision labels?\n4.Is CADGrasp feasible without object meshes during inference? If not, how practical is this in real-world grasping scenarios?", "limitations": "1. The ablation study is limited. It only removes individual components (e.g., IBS ranking or decompose), without evaluating combinations, making it difficult to assess interaction effects between modules.\n2. Real-world evaluations in Table 3 show only marginal improvement over baselines, with inconsistent numbers of trials across methods, which affects the fairness and reliability of the comparison.\n3. The paper lacks deeper theoretical analysis or visualization to explain why contact and collision conditioning improves grasp synthesis.", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths \n1. The paper proposes a novel diffusion-based grasp generation framework that incorporates both contact and collision awareness, addressing a critical limitation in existing dexterous grasping methods.\n2. The formulation is technically sound, and the system demonstrates generalization to unseen and real-world scenes without additional fine-tuning.\n3. The authors provide simulation and real-robot experiments, showing applicability to practical settings.\n\nWeaknesses\n1. The ablation study is limited. It only removes individual components (e.g., IBS ranking or decompose), without evaluating combinations, making it difficult to assess interaction effects between modules.\n2. Real-world evaluations in Table 3 show only marginal improvement over baselines, with inconsistent numbers of trials across methods, which affects the fairness and reliability of the comparison.\n3. The paper lacks deeper theoretical analysis or visualization to explain why contact and collision conditioning improves grasp synthesis.", "was_revised": true, "final_justification": "The authors' rebuttals adequately addressed my concerns. I suggest borderline accept of the paper.", "rebuttal": "We sincerely appreciate your constructive feedback and are excited that you find our work \"novel\", \"addressing a critical limitation in existing dexterous grasping methods\", and \"technically sound\"! We want to clarify your concerns and questions in the following.\n\n> **Q1: The ablation study is limited. It only removes individual components (e.g., IBS ranking or decompose), without evaluating combinations, making it difficult to assess interaction effects between modules.**\n\n**A1:** We thank you for your suggestions regarding more comprehensive ablation studies. In response, we conducted additional experiments to analyze the interaction effects between different modules and the contribution of each design choice (see Table 1 below). The results show that decomposed modeling of thumb and other finger contacts with the object leads to a substantial improvement in grasp performance, confirming that the thumb plays a crucial role in dexterous grasping [1]. Additionally, both the IBS ranking and grasp pose ranking modules further enhance overall grasp performance. We will update Table 3 in the final version and provide further discussion in the manuscript.\n\n**Table 1: Ablation Study on Interaction Effects and Design Choices** \n| IBS Ranking | Grasp Pose Ranking | Decompose | SR (%) on Dense Scenes of GraspNet-1B |\n|-------------|--------------------|-----------|------------------|\n| ✗           | ✗                  | ✓         | 73.1             |\n| ✗           | ✓                  | ✗         | 53.8             |\n| ✗           | ✓                  | ✓         | 83.9             |\n| ✓           | ✗                  | ✗         | 26.9             |\n| ✓           | ✗                  | ✓         | 75.7             |\n| ✓           | ✓                  | ✗         | 56.1             |\n| ✓           | ✓                  | ✓         | 86.5             |\n\n\n> **Q2: Real-world evaluations in Table 3 show only marginal improvement over baselines, with inconsistent numbers of trials across methods, which affects the fairness and reliability of the comparison.**\n\n**A2:** We apologize for any confusion. As described in the Real-world Setup section of the manuscript, we follow the cleared-table grasping evaluation protocol used in GS-Net [2]. For each scene, the policy continues grasping until two consecutive failures occur. As some baselines tend to reach this failure criterion earlier, the total number of grasp attempts appears inconsistent across methods in Table 3. This actually reflects the greater robustness of our method. We will clarify this evaluation protocol and the reason for varying trial numbers in more detail in the final version for better clarity.\n\n> **Q3: The paper lacks a deeper theoretical analysis or visualization to explain why contact and collision conditioning improve grasp synthesis.**\n\n**A3:** We thank you for pointing out the importance of deeper theoretical analysis and visualization of our sparse IBS representation and the effect of contact and collision conditioning. We provide further clarification from two perspectives:\n- **Compared to one-stage direct grasp pose prediction:** Directly mapping 3D point cloud observations to grasp poses is challenging due to the highly non-linear nature of the mapping from 3D point cloud space to pose space, resulting in low sample efficiency[3][4]. Furthermore, such representations are usually embodiment-specific, limiting cross-embodiment generalization. Our sparse IBS representation is spatially aligned with the 3D point cloud, improving sample efficiency and enabling our two-stage approach to achieve cross-embodiment generalization, as shown in our experiments.\n- **Compared to two-stage methods that optimize from predicted contact map:** Approaches using the contact map as intermediate representations generally assume that complete point cloud observations are available, which is unrealistic in real-world settings with partial observation. Our proposed sparse IBS representation, in contrast, does not rely on this assumption. As shown in both the quantitative experiments in the manuscript and qualitative visualizations in the supplementary material, our method achieves robust dexterous grasp pose prediction using only single-view partial point cloud inputs, making it suitable for complex, real-world tasks.\n\nWe appreciate the suggestion and agree that further theoretical insights would strengthen our work. We will include additional theoretical analysis and visualizations in the final version.\n\n> **Q4: How robust is CADGrasp to noisy supervision during training, especially in contact or collision labels?**\n\n**A4:** We appreciate the reviewer’s question. In our training process, the contact and collision labels are not manually annotated but are generated automatically in simulation. Specifically, force-closure grasps are optimized in simulation, and both the IBS surface and contact ground-truth are computed programmatically. This approach ensures label consistency and reduces the likelihood of significant supervision noise.\n\n> **Q5: Is CADGrasp feasible without object meshes during inference? If not, how practical is this in real-world grasping scenarios?**\n\n**A5:** Unlike contact map-based two-stage methods, our model does not require object CAD models or complete point cloud observations as input, thanks to our proposed sparse IBS representation. CADGrasp only relies on single-view partial point clouds, enabling dexterous grasping in complex real-world scenarios using partial observations. This design ensures that our method can be directly deployed in real-world environments without any special modifications or assumptions.\n\n[1] Thomas Feix, et al. The grasp taxonomy of human grasp types. IEEE Transactions on human-machine systems, 2015.\n\n[2] Chenxi Wang, et al. Graspness discovery in clutters for fast and accurate grasp detection. ICCV 2021.\n\n[3] Haoming Li, et al. Contact2Grasp: 3D Grasp Synthesis via Hand-Object Contact Constraint. IJCAI 2023.\n\n[4] Hao-Shu Fang, et al. AnyDexGrasp: Learning General Dexterous Grasping for Different Hands with Human-level Efficiency. ArXiv 2025."}, {"review_id": "DNUgwRVgXf", "reviewer": "Reviewer_EAK1", "summary": "The paper proposes CADGrasp, a two-stage algorithm to empower contact- and collision-aware dexterous grasping in cluttered scenes from a single-view point cloud. The first stage predicts an IBS surface in voxels and the second stage performs an energy-based optimization for grasp poses. The method has demonstrated strong performance against baselines in cluttered scenes both in simulation and real-world experiments and across two hand embodiments.", "questions": "1. Do you use simple motion planning to execute the pre-grasp?\n2. What is the inference speed of the method?\n3. \"Additionally, the current second-stage optimization is time-consuming due to the use of the DDPM for sampling IBS.\" In my understanding, the second stage optimizes the hand pose given a fixed IBS surface. Why is the DDPM used for sampling IBS in the second-stage optimization?\n4. How does the voxel resolution for IBS surface prediction affect the overall performance? Clearly, there is a trade-off between the precision and the computational cost.", "limitations": "1. As mentioned by the authors, the optimization is time-consuming.\n2. Voxel resolution can largely limit the precision of the IBS surface prediction. And computational cost grows cubically when we increase the resolution.\n3. Small and thin objects are particularly subject to the voxelization.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1. The method effectively models the grasps in cluttered scenes with only a single-view point cloud that avoids collision with non-target objects and captures the contact information.\n2. The method is object- and hand-agnostic so that it can generalize to different objects and hand embodiments.\n3. The two stage design is modular and interpretable.\n4. The high performance in real-world experiments provides solid evidence for the effectiveness of the method.\n\nWeaknesses:\n1. As mentioned by the authors, the optimization is time-consuming.\n2. Voxel resolution can largely limit the precision of the IBS surface prediction. And computational cost grows cubically when we increase the resolution.", "was_revised": true, "final_justification": "The authors' response has addressed my concerns and questions. I would like to change my score to \"Accept\".", "rebuttal": "We sincerely appreciate your constructive feedback and are excited that you find our work \"effective\", \"interpretable\", and \"solid\"! We want to clarify your concerns and questions in the following.\n\n> **Q1: As mentioned by the authors, the optimization is time-consuming. What is the inference speed of the method?**\n\n**A1:** We thank you for your comment. We provide the following clarifications and will include a discussion of this part in the final version. \n\nThank you for raising this important point regarding the inference time of our method. In our experiments, the average inference runtime per sample on a single NVIDIA RTX 4090 GPU is summarized in Table 2. While our approach currently does not achieve real-time performance, we emphasize that these results are obtained with unoptimized code and no engineering-level acceleration. There is significant potential to reduce inference time using more efficient sampling strategies [1][2] and further parallelization. Importantly, even in its current implementation, our method achieves faster inference compared to prior state-of-the-art two-stage approaches (see Table 3), while also delivering strong grasping performance in challenging real-world scenarios. We will elaborate on the potential for further speedup and optimization in the final version.\n\n**Table 1: Average runtime per stage of our method (NVIDIA RTX 4090)**\n| Module                            | Stage                       | Average Runtime (seconds)   |\n|-----------------------------------|-----------------------------|-----------------------------|\n| Wrist Pose Estimation             | 1                           | 1.38                        |\n| Diffusion-based IBS Generation    | 1                           | 1.39                        |\n| IBS Ranking                       | 1                           | 0.71                        |\n| Grasp Pose Optimization & Ranking | 2                           | 3.03                        |\n| Total                             | -                           | 6.51                        |\n\n**Table 2: Comparison with existing two-stage methods**\n| Method              | Intermediate Representation | Partial Observation | Cluttered Scene | Runtime (seconds)           |\n|---------------------|-----------------------------|---------------------|-----------------|-----------------------------|\n| GraspTTA            | Contact Map                 | ✗                   | ✗               | 43.23                       |\n| UniGrasp            | 3 Contact Points            | ✗                   | ✗               | 9.33                        |\n| GenDexGrasp         | Contact Map                 | ✗                   | ✗               | 16.42                       |\n| **CADGrasp (Ours)** | **Sparse IBS**              | **✓**               | **✓**           | **6.51**                    |\n\n> **Q2: Voxel resolution can largely limit the precision of the IBS surface prediction. And computational cost grows cubically when we increase the resolution.**\n\n**A2:** We appreciate your comment and agree that voxel resolution is a key hyperparameter that impacts both the precision of IBS surface prediction and the associated computational cost. In our work, we select a voxel size of 5mm to balance accuracy and efficiency. This resolution is chosen because it is sufficient to capture contact surfaces for most objects in practical applications, while still maintaining manageable memory and runtime requirements (see Table 1 in A1 for runtime details). To further investigate this trade-off, we conducted ablation experiments on the GraspNet-1B Loose setting, as shown in Table 3 below. Finer resolutions lead to substantially higher memory consumption, while offering only marginal improvements in success rate. Conversely, coarser resolutions reduce memory usage but result in clear performance drops. We will add a more detailed discussion of this design choice in the final version of the paper.\n\n**Table 3: Ablation Study on Voxel Resolution**\n| Voxel Size (mm)  | Memory Usage (GB)  | SR (%) on Dense Scenes of GraspNet-1B |\n|------------------|--------------------|---------------------------------------|\n| 2.5              | 2.12               | 81.2                                  |\n| 5                | 0.84               | 81.0                                  |\n| 10               | 0.36               | 72.4                                  |\n\n> **Q3: Do you use simple motion planning to execute the pre-grasp?**\n\n**A3:** We thank the reviewer for this question. In our experiments, all methods employ the same motion planning algorithm to execute both pre-grasp and grasp poses. Developing goal-conditioned reinforcement learning algorithms for planning based on grasp poses is a promising direction for future work.\n\n> **Q4: The paper lacks a deeper theoretical analysis or visualization to explain why contact and collision conditioning improve grasp synthesis.**\n\n**A4:** We thank you for pointing out the importance of deeper theoretical analysis and visualization of our sparse IBS representation and the effect of contact and collision conditioning. We provide further clarification from two perspectives:\n- **Compared to one-stage direct grasp pose prediction:** Directly mapping 3D point cloud observations to grasp poses is challenging due to the highly non-linear nature of the mapping from 3D point cloud space to pose space, resulting in low sample efficiency[3][4]. Furthermore, such representations are usually embodiment-specific, limiting cross-embodiment generalization. Our sparse IBS representation is spatially aligned with the 3D point cloud, improving sample efficiency and enabling our two-stage approach to achieve cross-embodiment generalization, as shown in our experiments.\n- **Compared to two-stage methods that optimize from predicted contact map:** Approaches using contact map as intermediate representations generally assume that complete point cloud observations are available, which is unrealistic in real-world settings with partial observation. Our proposed sparse IBS representation, in contrast, does not rely on this assumption. As shown in both the quantitative experiments in the manuscript and qualitative visualizations in the supplementary material, our method achieves robust dexterous grasp pose prediction using only single-view partial point cloud inputs, making it suitable for complex, real-world tasks.\n\nWe appreciate the suggestion and agree that further theoretical insights would strengthen our work. We will include additional theoretical analysis and visualizations in the final version.\n\n> **Q5: \"Additionally, the current second-stage optimization is time-consuming due to the use of the DDPM for sampling IBS.\" In my understanding, the second stage optimizes the hand pose given a fixed IBS surface. Why is the DDPM used for sampling IBS in the second-stage optimization?**\n\n**A5:** We apologize for the confusion caused by this statement. This was a misstatement in the manuscript, and the intended statement should be: “The Diffusion-based IBS prediction in the first stage is time-consuming.” Thank you for catching this error. We will correct this in the final version.\n\n> **Q6: Small and thin objects are particularly subject to the voxelization.**\n\n**A6:** We appreciate your comment. As shown in Table 3 of A2, increasing the voxel resolution yields only marginal improvements in grasp success rate. However, we agree that object size has a substantial impact on grasping performance. In Table 4 below, we report the success rate of our method on the GraspNet-1B (Loose) setting as a function of object volume, along with the proportion of objects in each size category. The results indicate that, within a reasonable range, the grasp success rate for small objects is indeed lower compared to larger objects. Combined with the findings in Table 3 of A2, we attribute this to the inherently higher demands for precision and strategy when grasping small and thin objects, rather than to insufficient voxel resolution. We will add further discussion of this point in the final version.\n\n**Table 4: Success Rate on Small and Thin Objects**\n| Volume Range (m³) | Success Rate (%) | Proportion (%) |\n|-------------------|------------------|----------------|\n| (0, 0.00025)      | 77.0             | 33.0           |\n| [0.00025, 0.0005) | 78.8             | 36.9           |\n| [0.0005, 0.001)   | 82.6             | 22.3           |\n| [0.001, 0.0015)   | 82.3             | 6.0            |\n| [0.0015, +∞)      | 91.5             | 1.9            |\n\n\n[1] Salimans T, et al. Progressive distillation for fast sampling of diffusion models. ICLR 2022.\n\n[2] Song Y, et al. Consistency models. ICML 2023.\n\n[3] Haoming Li, et al. Contact2Grasp: 3D Grasp Synthesis via Hand-Object Contact Constraint. IJCAI 2023.\n\n[4] Hao-Shu Fang, et al. AnyDexGrasp: Learning General Dexterous Grasping for Different Hands with Human-level Efficiency. ArXiv 2025."}]

Author rebuttal:

Meta-review: This paper proposes CADGrasp, a two-stage framework for dexterous grasping in cluttered scenes that introduces sparse Interaction Bisector Surface (IBS) as a novel intermediate representation to decouple contact and collision awareness from complete scene geometry. Its key strengths include: (1) the innovative sparse IBS representation that enables robust optimization without complete object geometry, (2) strong cross-embodiment generalization without retraining, and (3) exceptional real-world performance (93.3% success rate) across diverse clutter configurations. 

The authors did a great rebuttal, and all reviewers are satisfied. The AC concurs with reviewers that the representation learning contribution is significant and the real-world validation compelling, and thus the final decision is accept. The authors are encouraged to include the additional feedback provided during the rebuttal and discussion period.
Decision: Accept (poster)