Active Learning of 3D Gaussian Splatting with Consistent Region Partition and Robust Pose Estimation
Authors: Ruiqi Li
Yiu-ming Cheung
Venue: ICLR 2026 Poster (2026)
Keywords: Active Learning
3D Gaussian Splatting
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: We propose a bottom-up active learning strategy for 3DGS.

Abstract: Radiance fields have been successful in reconstructing 3D assets for scenes presented in Virtual Reality and Augmented Reality (VR/AR). The general workflow of scanning objects with radiance field representation involves a heavy workload of capturing images depicting the object empirically by the user, and lacks feedback for the image collection stage. This would lead to potential repeated or deficient gathering of information, affecting the efficiency of the reconstruction workflow. In this paper, we therefore present an active learning algorithm for 3D Gaussian Splatting that guides the image capturing by estimating the pose of the most informative image. Specifically, our method first partitions the consistent regions in the model by analyzing the Gaussian attributes and visibility features. Then, we determine the informative region to explore by estimating the semantic feature variance of each Gaussian, which evaluates the quality of the Gaussian cloud from the semantic level features. Furthermore, we tackle the practical problem of noise in the pose of the collected image via a robust pose optimization method. Extensive experimental results on both synthetic and real-world scenes demonstrate the remarkable performance of our algorithm in active learning of the radiance field under both accurate and noisy pose conditions.

Published as a conference paper at ICLR 2026
ACTIVE LEARNING OF 3D GAUSSIAN SPLATTING WITH CONSISTENT REGION PARTITION AND ROBUST POSE ESTIMATION
Ruiqi Li, Yiu-ming Cheung
Department of Computer Science
Hong Kong Baptist University
(carqlí, ymc @comp www.. hk
ABSTRACT
Radiance fields have been successful in reconstructing 3D assets for scenes pre- sented in Virtual Reality and Augmented Reality (VR/AR). The general workflow of scanning objects with radiance field representation involves heavy workload of capturing images depicting the object empirically by the user, and lacks feed- back for the image collection stage. This would lead to potential repeated or deficient gathering of information, affecting the efficiency of the reconstruction workflow. In this paper, we therefore present an active learning algorithm for 3D Gaussian Splatting that guides the image capturing by estimating the pose of the most informative image. Specifically, our method first partitions the consistent regions in the model by analyzing the Gaussian attributes and visibility features. Then, we determine the informative region to explore by estimating the semantic feature variance of each Gaussian, which evaluates the quality of the Gaussian cloud from the semantic level features. Furthermore, we tackle the practical prob- lem of noise in the pose of the collected image via robust pose optimization method. Extensive experimental results both synthetic and real-world scenes demonstrate the remarkable performance our algorithm in active learning of the radiance field under both accurate and noisy pose conditions.
INTRODUCTION
3D Gaussian Splatting (3DGS) Kerbl et al.) (2023) has become popular in novel view synthesis of objects and scenes by learning a radiance field represented based on Gaussian ellipsoids. However, a major problem of 3DGS is that it requires large number of images for training, which are desired to be placed densely to cover the scene. Capturing such images would require extensive and potentially repeated human labor. More importantly, timely feedback missing, leading to a potential oversampling of images in some object parts and undersampling in some others at the same time. This incurs the misalignment of the captured images and the required images for reconstructing the radiance field.
the quality of the captured images is
practical solution to the above problem is the active learning of radiance field reconstruction, where image capturing and 3DGS model training are performed simultancously in an online man- ner and provide mutual guidance for each other. Such an active reconstruction algorithm delivers the user with the next best poses for capturing images, where they can provide the model with the most information that is missing in the current model. Existing research on the active reconstruction mostly focused on evaluating the quality of rendered images to choose the next pose, and developed heuristic metries such as predictive uncertainty or ray entropy Lee et al. (2022); Ran et al.) (2023). However, these methods cannot directly generate candidate poses first. Then, they evaluate the informativeness of the rendered images, which would ignore the occluded regions. Also. these metrics overlook the semantic features quality of object parts in the radiance field, which reflects the recognizability of object semantics and an impor-
poses, and require randomly sampling some
"Corresponding author is Yiu-ming (llemmm@@ymm@@Compppbbu........ Code available whtttps: //gi thub. www/csrel i/alearrlllllllaa 3dgs

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Published as a conference paper at ICLR 2026
Incomplcis 3065 Model
Mokey Part Gasroien Ellipsoids
Determine the Kolyy Fart
Move to the Estimated Next Pose
Generating Nost View Pose
Cannooulgne with bndt
Mov t0 cestir
Next Pose Estimation
AR Guided Next View Capturing
User Interface
An application scenario of our active reconstruction algorithm. Our algorithm can be deployed on the server end, providing the next best pose in a front-end smartphone showing as virtual cameras to guide the image capturing.
Figure 1:
tant indicator of the perceptual quality of the geometry and appearance of the reconstructed object. Moreover, previous works Jiang et al. (2023); Pan et al. (2022b) select the next views from pre- collected dataset, assuming that the perfect camera pose is provided. However, from practical perspective, the actual captured image might deviate from the desired best pose, leading to noise in the camera pose and poor reconstruction results.
In this paper, we develop practical active reconstruction algorithm that solves the above challenges. Our method directly analyze the noisy distribution of an incomplete 3DGS model under training. providing guidance for a progressive scanning of each object part, and finally composing complete, semantically recognizable object with distinct appearance features.
Furthermore, to handle the noisy pose raised by the image capturing with, e.g., a camera on hand- held device, design pose optimization algorithm that move the camera from the given next pose to the actual pose of collected image. Our algorithm runs in an online manner, providing the user with feedback on the noisy region the current model and guidance to the next pose.
Conceptually, we apply a bottom-up strategy to capture the under-reconstructed object parts in se- quence. Specifically, we propose an active reconstruction algorithm that can firstly divide the con- sistent part of the 3DGS models. These consistent parts share similar geometry, texture and visibility features. The divided consistent regions are captured by a of adjacent cameras, where they can provide the best coverage for that region. We use the variance of the semantic feature of each Gaus- sian in the model to evaluate the quality of each Gaussian and determine the noise and uncertain region. Also, our method can adaptively decide the number of new poses in each image capturing step according to the size the consistent region. Additionally, we employ a pose optimization with reprojection loss to optimize the actual pose of newly captured images, showing robustness under inaccurate poses.
Experiment results have demonstrated that the informativeness of the next best poses generated by our method outperforms those from the existing state-of tiitte-f--hee-rt the-art method, evaluated quantitatively by the quality of synthesized novel views on benchmarks of the radiance field active reconstruction with perfect pose using a limited number of images. We also validated that our robust pose estimation can improve 3DGS model quality by reconstructing real-world objects under large pose noise. The contributions of our work are summarized as follows:
We propose a bottom-up strategy for dividing and conquering the noisy consistent regions in reconstructing the radiance field of objects in an active manner.
We propose to evaluate the noisy level of Gaussians with semantic feature variance to choose the next pose.
We develop pose fine-tuning method based on reprojection visible Gaussian to provide depth supervision for optimizing the pose.
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2 RELATED WORK
2.1 ACTIVE RECONSTRUCTION OF RADIANCE FIELD
Previous works investigated the active view selection problem of radiance fields with known actual poses of new images. Generally, they seleet the next view from candidate set of images already collected with poses estimated. Among them, FisherRFDiang et al. (2023) investigated the criterion for the next view, where they select the next image from a pre-collected set of images by maximiz- ing the expected information gain. Earlier view selection methods build upon Neural Radiance Field (NeRF). NeurAR Ran et al. (2023) and ActiveNeRF Pan et al. (2022b) add uncertainty output into the neural network and optimize with Negative Log Likelihood (NLL) loss, which is then used to choose the next view. Lee et al. Lee et al. (2022) proposed to leverage the entropy of density distri- bution along the ray as the uncertainty metric for that pixel. Xue et al. Xue et al. (2024) proposed an uncertainty estimation method for NeRF based on Bayesian neural network, and takes visibility into account in learning the uncertainty of rendered image. NeRF Director Xiao et al. (2024) pro- posed an evaluation framework for view selection. Other works JJin et al. (2023); Pan et al. (2023); Yan et al. (2023)] Lee et al. (2023); Zhha et al. (2022) proposed different uncertainty or information gain estimation methods for rendered images of NeRF, while they cannot be directly used for 3DGS.
Some previous works provided practical image capturing systems using cameras held by robot arms for the radiance field. ScanNeRF De Luigi et al. (2022) collects densely posed images from a pre- defined trajectory. Lee et al. [ee al. (2022) used wheeled robot and a five degrees of freedom manipulator to capture the estimated next best pose. However, they only consider the case when per- feet actual poses are provided. Nevertheless, our work considers more practical scenario, where the actual pose of the newly captured image deviates from the desired best pose, raised by eπror from the inaccurate pose of camera held by robot arm or a mobile device. Moreover, we utilize the explicit property of 3DGS, where we can generate directly informative poses without rendering and evaluating images. Thus, our method can run in an online manner and guide the users image capturing.
Apart from object-level view selection, some other methods are proposed for the active reconstruc- tion of large-scale scenes with neural implicit fields. These methods often take into account the path length when exploring the scene. Zeng et al. Zeng et al. (2022) proposed to reconstruct an implicit Truncated Signed Distance Function (TSDF) field, and leverage the neural network to approximate the information gain field in an implicit manner. Additionally, they deploy their method on a real drone. Active neural mapping and follow-up work Yan et al. (2023); Kuang et al.] (2024) introduce an active exploration and mapping of indoor scenes with implicit representation. NARUTO Feng let al. (2024) defined an uncertainty metric used for the exploration of indoor scenes with the TSDF
field. Additionally, some previous research K Kwon et al.] (2023); Chen et al. (2023): Adamkiewicz al. (2022) ;; |Mazaa et al. (2023); Jiang al. (2024); Chapllo et al. (2020); Jin al. (2024); Li al.
(2024) leveraged the radiance field or implicit field and designed algorithms for tasks such as active object finding, navigation, and embodied agents, where they consider not only the maximization of the reconstruction quality but also navigation to the target and path efficiency. Our work can be extended to such a scene-level scenario by estimating the semantic feature variance metric for each object in the scene.
2.2 NEXT BEST VIEW PLANNING
Apart from the radiance field, some algorithms were proposed to solve the view planning or sen- sor planning problem in object-level scanning for data structures such as voxels, point clouds or building maps of the scene. The majority of these algorithms leverage robots equipped with depth sensors or use multi-view stereo methods to acquire accurate depth. Tarabanis et al. Tarabanis et al. (1995) designed a sensor planning algorithm for robot vision system considering the detectability of feature points. GenNBV Chen et al. (024) formulated the NBV problem as a Markov Decision Process (MDP), and learned a generalizable reinforcement learning model for next best view pre- diction using a simulator with hundreds of models. Yan et al. Yan et al. (2023) trained a sequential prediction model that can predict the best pose considering the previous input image and the model under reconstruction. They use reward functions such as the intersection of the reconstructed model with the ground truth to lead the training of best pose prediction.
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X.C.R
Visibility Feature
Decoder
Semantic
Export 3DGS Model
Features Variance
Clustering
Encoder
Noxt Loop
Gaussian
Optimize 3DGS
Consistent Region Partitioning
Attributes
Determine Noisy Region
Optimize View
Draw Virtual Camera AR
Guide User Capture Image
Sampling Next View Poses
Determine View Number
Next View Acquisition
Figure 2: The above figure shows the pipeline of our active reconstruction algorithm. The next best pose estimation contains consistent region partitioning, noisy region determination and view sampling stages. The 3DGS model training, next pose estimation and guided image acquisition performed iteratively.
Another line of works designed geometry-based methods Zeng et (2020); Scott al. (2003); Lee et al. 2020); Scott et al. (2003); Mendoza et al. (2020); Daudelin & Campbell (201 Kriegel et al. (2012) 2011 for view planning object scanning with depth sensors, where accurate depth infor- mation is available. For example, Chen et al. Chen & Li (2005) established a uniform surface model by analyzing the surface curvatures of objects, which helps determine the pose that captures noisy parts. Mendoza et al. Mendoza et al. (2020) modeled the best pose prediction problem and built a 3D CNN that Iearns to predict the next best pose from a volume input. They trained dataset containing trajectories generated from 14 objects. SCVP et al. (2022a) mod- eled the view planning as set covering optimization problem. Different from the above works, our method is built upon the radiance field, which can generate dense reconstruction with photorealistic novel view renders with image input only.
classification
Some other works Song et al. (2022); Bircher al. (016); Caccamo al. (2016) Naazare et al. 2022 Sun et al. (020): Wang et al. (2019) designed algorithms for drones and field robots to actively explore scenes. Yamauchi Yamauchi (199)) proposed the concept of frontiers for active exploration, which are locations lies on the intersection of explored and unknown areas in the scene. SCONE Guédon et al. (2022) defined the surface coverage metric and proposed Monte Carlo tegration over the volumetric representation to estimate the best pose. MACRONS Guédon et al.) (2023) learned to predict a volume occupancy field and estimate the best view from previous ob- servations in self-supervised learning manner. Zhou et al. Zhou al. (2020) built exploration algorithm for drones that applies hierarchical planning strategy. Recent scene-level active mapping approaches such as Naruto Feng et al. (2024), ActiveGAMER Chen et al. (2025a), and ActiveSGM Chen et al. (2025b) have further advanced 3DGS view planning, while our object-level method is a complementary direction targeting reconstruction in mobile capture settings.
Theoretically, the active mesh reconstruction problem is related to the art gallery problem Rourke 1987): Marzal which investigates the minimum number of views cover the surface of known model. Some computational geometry algorithms Bonnet & Miltzow (2016) can solve the best view placement with known model geometry, whereas algorithm reconstructs unknown models actively.
METHOD
3.1 ACTIVE LEARNING PIPELINE
In the original pipeline of 3DGS, batch of K images is first captured. Then, the Structure from Motion (SfM) algorithm is performed to estimate the camera pose of each image. After that, a 3DGS model G is trained by projecting the Gaussians into the image plane and performing rasterization
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get the rendered image:
c= c= (1-ax,
where ý are the indices for Gaussians, C is the view-dependent color, determined by spherical harmonics, a is the density, and G is a Gaussian function centered at the position x: C(x) exp (-1/2- x -xxxxx). where the covariance matrix Σ determines the rotation and scaling of Gaus- sian. During training. we minimize the difference between the image and the ground truth with combination of L1 loss and structural similarity loss.
However, in our active reconstruction algorithm, the 3DGS model is learned in an online manner, where the image capturing and optimization take place in alternation. At the hth image capturing step, one or more images are captured from given poses:
{P) PE. Pi} A(g*).
where poses are chosen from the camera extrinsic space P E SE(3). The acquisition function A will analyze the model at the k image capturing step G and estimate the next poses P while determining the number of poses needed to be captured adaptively.
The objective of the acquisition function A is to provide the model with the most amount of infor- mation from images obtained at these poses, in order to minimize the effort of capturing images. In work, we first build an algorithm that can find under-reconstructed parts of the 3DGS model. Specifically, our method first divides consistent regions of the current 3DGS model G and evaluates which regions are the most noisy and uncertain parts, thus requiring more images cover. Then, method can provide the informative camera pose in, for example, an Augmented Reality application guide the user to collect images, shown in Figure I required number of images are collected.
These processes repeated until the
The images collected via the human-guided operation will inevitably have poses that deviate slightly from the ideal estimated view poses. To address this pose error and ensure high-quality reconstruc- tion, we utilize the estimated best poses as initializations for the actual image poses; then, we opti- mize the camera pose parameters of the newly collected images to ensure the pose accuracy of the new image used for reconstructing the 3D scene.
In practice, the actual pose of the images collected by the above guided operation performed by human users may deviate from the best poses. To solve this problem, we regard the estimated best poses as the initialization for actual pose, then, we optimize the pose of these new images together with the 3DGS model parameters.
3.2 INFORMATIVE VIEW ACQUISITION
In this section, introduce algorithm to determine the next poses for guiding the acquisition of new images. Generally, our method follows bottom-up strategy, where the objects are scanned by parts individually. First, we will introduce the concept of local consistent regions and the algorithm to partition them. Then, we generate new view samples on local consistent regions that are not covered well by the data already collected. We determine these regions by estimating the variance of the per-Gaussian semantic feature, which is extracted by self-supervised model trained on the large-scale dataset.
3.2.1 LOCAL CONSISTENT REGION DETERMINATION
The consistent regions are connected areas where the surface has smooth geometry, shares similar texture, and, most importantly, the complete region often visible as a whole from a camera pose. We refer to the last property the visibility feature, and will introduce an algorithm to determine it for 3DGS models. Once we determine the local consistent parts of the object, we employ a divide-and-conquer scheme to infer possible new observations added improve the reconstruction performance of the most uncertain part. Figure shows an example of the consistent region of a complex object composed of many parts.
We first estimate the visibility feature of the Gaussians. We refer to a region where the points share similar visibility features cisisility-onniistent region. An illustrative example is that thin plate
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has two sides; any camera that sees side can most of the surface that side, and cannot the other side. A visibility consistent region is mostly captured together. To determine the visibility feature, we first sampled a subset of M 3D Gaussians. then reconstructed the surface of the model with Alpha Shape Edelshrunner et al. 1983). After that, a set of N camera positions are sampled uniformly the surface of hemisphere around the object. We estimate visibility matrix T, where the (m,,1 entry of the matrix I" mm is binary indicator for the visibility of the m point from the th camera. point is visible to the camera if not occluded by any mesh triangle, and use the fragment buffer in mesh rasterization to determine visibility in the implementation. The m row of the visibility matrix is the visibility feature for m Gaussian. The visibility feature, concatenated with color and position constitutes the feature for each Gaussian:
ix/r, v/V3, R. m/V]],
where r is a predefined radius of the model, R is the rotation of Gaussian, represented by quaternion. Each of the four components in feature vectors normalized by its maximum value. Finally, we determine local consistent region by clustering the Gaussians with the above feature vector We use the K-Means Burkardi (2009) algorithm to form clusters of Gaussians that form consistent regions.
Compared to previous work using image-based metries for determining the next best poses, which is easily affected by image occlusion, our method determines the consistent region by visibility and is able to perform reconstruction without missing any informative region. Fig- ure shows that our method succeeds in distin- guishing the two sides of the thin cymbal with different visibility.
Upper Sides
Different Visibility
Lower
3.2.2 SEMANTIC
FEATURE VARIANCE SCORE
Consistent Regions of Drums
After partitioning the consistent regions, we have to determine which region is mostly noisy and under-reconstruuted, and more im- ages should be captured around it. Using deep features learned from large-scale datasets to evaluate the quality of data is common prac- tice in computer vision Zhhang et al. (2018).
Figure 3: Our regions partitioning algorithm can divide Gaussians based on their visibility feature, which helps to find occluded surfaces and gener- ate consistent next poses.
Therefore, we leverage the per-point semantic feature extracted from point cloud analysis model to directly determine the noisy level of each Gaussian.
Specifically, we perform surface reconstruction with the current Gaussian model using Alpha Shape. We extract the vertices of the surface mesh point cloud. Then, we extract the per-point deep feature of the point cloud using Point-MAE model|Pang et al. (0022). This feature extraction model leverages an autoencoder with Transformer architecture, which is pretrained in a self-supervised manner by mask modeling and finetuning on the per-point segmentation task. We perform T forward passes with different randomly sampled input points and dropout masks to get multiple instances of features. Then, we compute the norm of the average variance of the feature instances for each point get the semantic variance score Suem:
Seem = ||var[ 1
(4)
where fi is feature extractor used in tth forward pass with random dropout mask, x is one point. larger variance score of the semantic feature instances implies that the semantic property of the point is unstable. Since the Transformer model captures sequential features, the region around that point has a higher noise level and poor quality Gal & Ghahramani (2016); Lakshminarayanan et al. (017).. Additionally, we use the following metric Stotal to choose the consistent region needed to be covered in the next image capturing step:
1 Stotal A . S (x;) + Xa ming/ xxll
(5)

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Consottett roston
Rendereed
viow
Ground Truth
Gaussian
Eliipoods
Step
Step 2
Step 3
Step 4
Figure 4:
visualize process of the active reconstruction across multiple image capturing steps. The upper shows the partitioned consistent region at each step, where the purple cluster the noisy region chosen to be covered by the informative poses generated. The middle and lower rows show the by adding new views to cover the noisy regions.
part in the 3DGS model. The model quality improves gradually
where x = } is the center of points in the region, X the center for chosen consistent region at 4thh image capturing step. The latter term implies that larger distance with historical covered regions encouraged choosing the next region.
3.2.3 ADAPTIVE COVERAGE VIEW SAMPLE GENERATION
To generate the next best poses from a determined consistent region, we use the following sampling method. We choose the centering point in the consistent region and cast a line starting from that point along the normal direction. Then, we calculate the intersection point p of that point and bounding sphere with given radius.
We sample camera position from a von Mises-Fisher distribution v(u,, k), where the ls mean direction and is the concentration parameter. We choose that intersection point on the bounding sphere as the mean direction μ p. and use k V N,/Nau as the concentration parameter, where N, is the number of points in the consistent region. Nan is the number of total points. The number of poses sampled is also determined by this proportion of point number is 122 N,/Nou, where ?/ and 7/2 preset coefficients. After that, camera poses built using the lookat method where the targeting point the center consistent region. Since the von Mises-Fisher Fisher distribution also known the Gaussian distribution in the 3D direction, this design choice to ensure that the sampled cameras are looking at the consistent region to cover. Our method can determine the number of views capture and adaptively, according to the view needed for that consistent area, which further improves the efficiency of capturing the number of images needed.
As shown in Figure our algorithm successfully divides the local consistent region of the cymbals in the drums, which is noisy in the current model. After we identify the most under-reconstrruted consistent region using our method, we generate camera pose samples to cover that part. At the next image acquisition step, that part is well-reeconstructed.
3.3 ACCURATE ACTIVE RECONSTRUCTION WITH ROBUST POSE ESTIMATION
After estimating the poses for covering the next consistent region, we use the desired pose to guide the user capture the next poses, shown in Figure Once the image is collected, optimize

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Table 1: The quality of rendered images using active reconstruction under perfect pose.
Method
LPIPS
SSIM
LPIPS 4
NeurAR Ran etal.
18.94
0.774
0.241
23.71
0.819
0.172
NeRF Random
16.430
0.809
0.254
22.614
0.890
0.111
NeRF Purthest
19.24
0.735
0.227
26.00
0.812
0.144
NeRF + ActiveNeRE Pan et al. 2022b
20.010
0.832
0.204
26.240
0.856
0.124
NeRF + Bayes Gol et al.
2024
14,950
0.798
0.282
22.504
0.887
0.115
NVF
Plenoxel 2024
18.691
0.821
0.228
21.736
0.925
0.162
Fridovich
Jiang
20.670
0.205
0.876
3DGS Random
22.493
0.112
3DGS Furthest
18.782
0.764
0.201
22.346
0.814
0.143
3DGS ActiveNeRF Pan ot al
20226
22.979
0.876
0.111
26.610
0.905
0,081
3DGS FisherRI Jiag et al
2023
23.681
0.883
0.102
29.525
0.944
0.043
Ours
25.542
0.896
0.063
30.186
0.943
0.033
the 3DGS model using the new image. In practice, the actual poses of the captured image might differ from the given best view poses, considering that error is caused by the position of the handheld device and the actual desired pose. Thus, we further optimize the actual pose of the captured images.
Specifically, we first freeze the 3DGS attributes and optimize only the relative pose between the actual pose of the new image and the desired best pose:
T arg min Spose (TP,P). P).
(6)
We would like optimize the relative rigid transformation T E SE(3) between the estimated best pose and the actual new pose 7P. Numerically, we optimize the Lie algebra T = Log T to avoid an ill transformation matrix. We optimize the relative pose with the following objective function:
Cpose Crgb (CTP)),(P))
We use the photometric loss here to minimize the distance between the image rendered by the model c(TP) and the acquired new ground truth image e(P). During pose optimization, learning of Gaussian attributes, which can avoid the infection of deviated pose to the Gaussian model. After the relative pose optimization, continue the learning of the 3DGS model parame- ters. An overview of our pipeline is shown in Figure
freeze the
4 EXPERIMENTS
4.1 BACKGROUND
Implementation Details. We use 3D Gaussian Splatting as the reconstruction algorithm. Our active reconstruction algorithm follows the same optimizer and learning rate for each attribute of the 3DGS model. The image acquisition is performed every 300 steps, after which the model is reinitialized as random points. To cluster Gaussians and perform consistent region partition, we sample a subset of 10K Gaussians from the model, and use the K-Means algorithm and relative tolerance of 1 After all images are collected, the model is further trained for 10, 000 steps.
Baselines. We chose the following active reconstruction methods as a comparison with our method:
1. NeurAR chooses the most uncertain next view evaluated by the additional variance output. 2. ActiveNeRF, which acquires the image with the most posterior uncertainty. uses Laplace's approximation to estimate the expected information gain. 4. BayesRay quantifies the uncertainty of radiance field with Laplacian approximation, and the most uncertain image chosen. 5. Random denotes randomly selecting views, and Furthest denotes selecting the most distant view from existing ones. We choose NeRF, Plenoxels (annxees/Fridovicc--Ke et al. (2022) and 3DGS reconstruction backbones.
FisherRF, which
4.2 ACTIVE LEARNING WITH PERFECT POSE
We compare the reconstruction performance using 10 or 20 total views for all eight objects in the NeRF-Synthetic dataset Mildenhall et al. (2020), and report the averaged image quality. For
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30GS Random
pose optimization
Ours without
Ours pose optimizatio
Gundam
Phoenix
Pony
Teddy
Figure 5: The qualitative results of our active reconstruction with handheld device. We compare visual results of rendered novel views.
method, we first capture three initial images uniformly as initialization. Then, the best poses are estimated and corresponding images are rendered in Blender and used for training. From the results shown in Table [ our method surpassed largely the performance of previous methods, such as ActiveNeRF and FisehrRF, in all quantitative metrics. terms of the PSNR metric, our method outperforms FisherRF by 1.9.
The results using 20 total view settings are shown Table 1 Our method is superior to previous state-of-the-art method, FisherRF, in both PSNR and LPIPS and shows comparable results SSIM. We outperform FisherRF by 0.6 terms of the PSNR metric. However, the performance increase is not significant the 10 views. A plausible reason is that as the total number of images increases, the redundancy information in the given views also increases. We visualize the process of next view generation and consistent region partition of the drums scene in Figure 4
4.3 ACTIVE RECONSTRUCTION WITH NOISY POSE
We reconstruct four new real-world objects us- ing active reconstruction system, and com- pare the visual quality of the reconstructions with and without pose optimization. We col-
Table 2: Ablation study results of our method.
PSNR
SSIM
L.IPS
lect the next image by manually matching the
next best pose with the pose of the smartphone camera using the view synthesis results from the current 3DGS model. Therefore, the pose of the collected image roughly aligns with the desired pose, with some noise on the camera pose raised by manually capturing left for our algo- rithm to deal with. The qualitative results of active reconstruction with noisy pose are shown in Figure 5 Without fine-tuning the actual pose of new captures, the noise in pose lowers the visual quality of the model. For example, the wings of the phoenix toy fail to reconstruct and become white background. As comparison, our robust pose optimization technique significantly improves the quality of the overall reconstruction.
w/o r
21.271
0.863
0.137
w/ Distance
22.438
0.866
0.090
w/o Point-MAE
24.584
0.877
0.074
Full
25.542
0.063

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4.4 ABLATION STUDY
We compare the following variants of our method and baseline on 8 scenes from Blender dataset using 10 total images: 1. Remove the visibility feature I in Equation S when clustering local when region; 2. Remove the larger distance term in Equation when choosing the most uncertain region; 3. Replace Point-MAE feature with Fisher Information implemented in FisherRF to validate the effectiveness of semantic feature variance in estimating uncertainty. The results of view synthesis are shown in Table 2 and removing any component would lead to performance decrease, which validates the effectiveness of each individual component.
5 CONCLUSION
In this paper, we have proposed a practical system for online image acquisition and 3DGS recon- struction. Our method applies dlvide-and-conuue strategy, which captures the local consistent regions adaptively to ensure complete coverage of the object. Experiment results have demonstrated the superior performance of our method. Future work can be made to improve the efficiency of our method or adapt it to large-scale environments.
ACKNOWLEDGEMENT
This work was supported by the Guangdong and Hong Kong Universities "1+1+1" Cross-Campus Research Collaboration Scheme with the grant: 2025A0505000004.
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A IMPLEMENTATION DETAILS
A.1 VISIBILITY FEATURE EXTRACTION
Firstly, we perform Alpha Shape Edelsbrunner et al. (1983) with alpha value equals 0.05 to recon- struct the triangle surface of the object from points in the exported Gaussians. The attributes of the original Gaussian are assigned to each vertex the reconstructed mesh by mapping each vertex with the attributes of the closest Gaussian.
We sample N 100 cameras randomly located on the surface sphere enclosing the object and looking the object center to determine the visibility matrix r. To perform visibility checking for each vertex in the mesh, we leverage the output of the Pytorch3d rasterizer Ravi et al. (2020). We first identify which faces the depth buffer to find the closest face to the viewer. Specifically, for the n camera, we map each pixel to the vertex that appears in the pixel. We iterate through the rendered pixel, marking the faces projected to each pixel with the minimum depth. Then, we find the vertex that belongs to the faces, which regarded as the visible vertex for the nh camera. We assign binary values indicating visible or not for each vertex mask in the n² column of the visibility matrix r. Finally, each m row of forms the visibility feature for that Gaussian point.
rendered at each pixel. Then, we use the depth information stored in
We compare quantitatively the image quality by the following three metries: Peak Signal to Noise Ratio (PSNR), Structural Similarity (SSIM) and Learned Perceptual image Patch Similarity (LPIPS) with a alexnet.
A.2 DEEP SEMANTIC FEATURE
We extract deep semantic features used in clustering the consistent regions for each Gaussian point.
We use a Point-MAE model Pang et al. (2022), where the encoder has 12 Transformer blocks. The model is pre-trained in masked auto-encoding manner the ShapeNet dataset Chang et al. (2015), and then finctuned on the ShapeNetPart dataset for the segmentation task. We use the feature extracted by the transformer encoder, which has 512 dimensions for each point.
To obtain the per-point semantic feature, we perform a total of T = 10 times forward pass. In each inference, randomly sample 90 percent of the points input, and dropout performed for the model parameters with a probability of 0.8 with different draws. Finally, we compute the norm of the variance of features get the semantic variance score.
Finally, we use K-Means clustering Burkardi (2009) with the combined and normalized features to find the clusters of points within the same consistent region. We use K-Means with a number of clusters equals 5, the maximum number of iterations equals 300, and the relative tolerance of the difference in the cluster centers equals 14
A.3 VIEW SAMPLING
In our active reconstruction with perfect poses on the Blender dataset experiments, we use a con- centration parameter ? = 0.1, and the coefficient for determining the number of generated cameras 1/2 1. real-world scenarios AR-based active reconstruction, users can adjust these parameters to fit the size and geometry complexity of the object.
A.4 3DGS TRAINING
For selecting the best poses during active reconstruction, we train the 3DGS using the images already collected from a random initialization every 500 steps. Finally, the model is trained for 10K steps. densification interval of 100 steps. We reset the opacity every 3000 For blender scenes, operation is always performed during active view selection, but stopped steps. The densification after the th step of the final model training.
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B ALGORITHM PSEUDOCODE
Algorithm Active Next Best View Selection
1: Input: Current 3DGS model Gk, acquisition history {Pi, pē-1}
2: Output: Next view poses ( P
3: Sample M Gaussians {g: J:=1 from G2
4: Sample N camera views {1z \N uniformly around object
5: Initialize visibility matrix {0, 1]MxN
6: for each view vl do
7: Render alpha shape mesh from Gg
8: for each Gaussian 9; do
Project Oj into view fij 10: if is visible in vy (not occluded via rasterization) then
9:
11:
[[ <
else
13: end if
[[i +
15: end for
end for
17: for each Gaussian yi do
18:
Construct feature vector 7/2 [2s/r, c// /3, R,, P:/VN]
end for
20: Cluster {7i} into consistent regions {
21: for each region R2 do
22: Extract surface mesh and sample points {zy}
23: for t T
Compute deep features ft(r,)) using Point-MAE with dropout 25: end for
24:
26: Compute semantic variance Seom (Ir₁) ([v)),
27: Compute region score:
28: Sycca = A [y Σ Spem (I₂) + A2 dist(Ry,
29: end for
30: Select region R" = arg max, Si
31: Determine center and surface normal of R
32: Sample view directions from von Mises-Fished distribution centered at bounding sphere inter- section
33:
Generate view poses P. using look-at orientation toward R center
34:
return [P], p!]
To facilitate reproducibility and clarify the operational details of our active view selection strategy, we provide pseudocode description of our method in Algorithm [ This algorithm outlines the full pipeline for estimating the next best camera poses our bottom-up active reconstruction framework.
C ADDITIONAL ABLATION STUDY
C.1 INVESTIGATING THE ROBUSTNESS OF CLUSTERING PARAMETERS
In separating local consistent regions, the clustering parameter is of great significance in the deter- mination of parts the object for generating the informative views. Intuitively, each cluster should contain part that is independent of other object parts. Specifically, we investigate the influence of the number of clusters on the overall reconstruction performance. We test with a total of 10 views on all 8 scenes from the Blender dataset, setting the number of clusters from {3, 5, 7). In practice, the number clusters is given by the user based on their observations of the geometrical complexity of the object to reconstruct. The results are shown in Table 3
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Table 3: Ablation study the number of clusters in consistent region partition.
Number of Cluster
PSNR
SSIM
LPIPS
24.913
0.889
0.074
5 (Default)
25.542
0,896
0.063
7
24.992
0.873
0.079
Table 4: Ablation study on the number of semantic feature samples.
Number of Samples
PSNR f
SSIM f
LPIPS
5
25.178
0.884
0.077
10 (Default)
25.542
0.896
0.063
15
25.976
0.901
0.061
Table 5: Ablation study the number of samples in the visibility feature calculation in consistent region partition.
Number of Sampled Camera N
PSNR 1
SSIM
LPIPS $
24.072
0.851
0.080
100 (Default)
25,542
0.896
0.063
200
25.497
0.895
0.061
The results demonstrate that the performance of our method varies slightly when using different numbers of clusters. This indicates that our method shows robustness to the setting of hyperparam- eters, demonstrating little performance variance against the variations of parameters.
C.2 INVESTIGATING THE NUMBER SEMANTIC FEATURE VARIANCE SAMPLES
In this ablation study, explore the effect of different sample sizes {5, 10, 15} estimating Se- mantic Feature Variance using the Point-MAE model. Our goal was to determine the optimal number of samples needed for accurate variance estimation and informative view estimation without exces- sive computational cost. We reconstruct all 8 objects from the Blender dataset with 10 total views. The results showed that using only 10 samples led balance between variance estimation and reconstruction performance.
As shown in Table 4 5 samples are insufficient capture the Semantic Feature Variance that exists in the noisy 3DGS model. Conversely, increasing the sample size to 15 did not significantly improve the accuracy of variance estimation compared using 10 samples. The computational cost grows linearly with the number of samples. Therefore, using 10 samples is both practical and efficient, optimizing the active learning performance without unnecessary inference of the model.
C.3 INVESTIGATING THE SAMPLES IN ESTIMATING THE VISIBILITY MATRIX
In this ablation study, we investigate the sampled view in visibility matrix estimation. Specifically, we test different numbers of sampled cameras N that are used to calculate the visibility feature. We report the results from all objects the Blender dataset, reconstructed with N 50, 100, 200. The computational cost of visibility determination grows linearly with the number of sampled cameras N. The quality of the visibility matrix would influence the determination of the local consistent region, therefore further influencing the performance of reconstruction. Our results are shown in T
The quantitative results show that our visibility feature estimation requires 100 sampled cameras to evaluate the visibility of the objects from all possible viewing angles, conveying the features of occlusion between different parts to the local consistent region partitioning algorithm. Using only 50 samples can lead to performance degradation, and using 200 samples increases the cost with no significant gain.
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Table 6: The per-scene quantitative results of rendered images using active reconstruction under perfect pose with 10 total views.
Scene
PSNR f
SSIM 1
LPIPS
chair
24.712
0.917
0.049
drums
21.840
0.895
0.059
ficus
27.934
0.946
0.028
hotdog
28.642
0.937
0.049
lego
23.230
0.859
0.079
materials
29.196
0.882
0.065
mic
29.304
0.965
0.023
ship 24.475
0.767
0.151
Average
25.542
0.896
0.063
Table 7: The per-scene quantitative results of rendered images using active reconstruction under perfect pose with 20 total views.
Scene
PSNR t
SSIM 1
LPIPS
chair
31.378
0.966
0.016
drums
24.155
0.931
0.040
ficus
33.642
0.980
0.010
hotdog
33.116
0.965
0.027
lego
29.028
0.933
0.033
materials
28.808
0.941
0.026
mic
33.292
0.982
0.012
ship 28.072
0.850
0.096
Average
30.186
0.943
0.033
D JUSTIFICATION FOR MAJOR DESIGN CHOICE
D. BOTTOM-UP STRATEGY RENDERED IMAGE BASED METHODS
Our method introduces novel bottom-up strategy that directly generates informative camera poses, making it the first approach capable of estimating the next view pose without requiring candidate sampling. This design choice marks a significant departure from prior methods Jiang et al.) (2023); Pan al. (2022b); Ran al. (2023) that rely rendering from pre-defined set of sampled views and then evaluating them using uncertainty or information gain metrics. Those approaches suffer from inefficiencies and often miss occluded structurally ambiguous regions. In contrast, our method leverages the structure of the 3DGS model to identify under-reconstruuted parts and generate poses that target them precisely. This strategy only streamlines the active view selection pipeline but also enables real-tima tiletime guidance during image acquisition, which is crucial in practical scenarios involving handheld or mobile devices.
Additionally, traditional active view selection often relies sampling a set of candidate views, rendering images from these viewpoints, and scoring them using informativeness heuristics such as entropy or uncertainty. However, this process is both computationally expensive and vulnerable to occlusion artifacts, often overlooking poorly reconstructed or unseen regions. contrast, our bottom-up strategy directly analyzes the geometric and visibility characteristics of the model to identify areas to capture. By estimating iisibility-consisee regions and their semantic uncertainty, generate next-best views without requiring rendering post-hoc evaluation. This direct approach not only accelerates the acquisition process but also increases coverage efficiency and robustness in real-world scenarios where perfect pose alignment cannot be guaranteed.
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Table 8:
The per-scene quantitative results of AR-based active reconstruction with pose optimiza-
tion.
Scene
PSNR t
SSIM t
LPIPS
gundam
30.363
0.963
0.029
phoenix
19.206
0.873
0.152
hourse
26.959
0.903
0.077
teddy
28.438
0.898
0.073
Average
26.241
0.909
0.083
Table 9: The per-scene quantitative results of AR-based active reconstruction without pose opti- mization.
Scene
PSNR
SSIM t
LPIPS +
gundam
24.962
0.878
0.080
phoenix
17.755
0.855
0.199
hourse
20.871
0.743
0.174
teddy
24.256
0.846
0.136
Average
21.961
0.831
0.147
D.2 SEMANTIC FEATURE VARIANCE
To identify noisy incomplete regions in the 3DGS model, we propose semantic feature variance metric computed per Gaussian point. This metric, derived from multiple stochastic passes through pretrained Point-MAE encoder, quantifies the uncertainty of semantic representation and reflects the overall distortion of each Gaussian. Methodologically, our approach build upon the technique proposed by Kendall Gal (2017) for estimating epistemic uncertainty. They utilize Monte Carlo dropout to approximate the posterior distribution of model weights and estimate uncertainty via the variance the model's 'ss predictive distribution across multiple stochastic forward passes. Similarly, we implement stochastic dropout within the Transformer blocks of our Point-MAE encoder. How- ever, instead measuring variance the final output, we compute the variance the semantic feature space. This adaptation is crucial for our reconstruction task: high variance in this feature embedding indicates that the model is uncertain about that region, and thus requires further visual coverage. Therefore, targeting regions with high semantic feature variance is principled strategy to improve the model. In spirit, this is analogous to using LPIPS Zhang et al. (2018) as perceptual metric to evaluate image quality, but applied at the semantic feature level in 3D space. This ap- proach offers significant advantages over simpler EDGGSnative cues, such as RGB/opacity variance and Fisher Information, which are local and low-level indicators. Unlike these heuristics, our metric allows us to directly assess the high-level semantic quality and informativeness of the 3D Gaussian cloud representation, providing a principled, robust, and geometry- aware way determine which parts of the model require further refinement.
E ADDITIONAL RESULTS
E. RUNNING TIME AND STANDARD DEVIATION OF RESULTS
The running time for local consistent region partition, calculating the variance of semantic feature and view generation is respectively 4.13, 3.7 and 0.01 seconds. All the view synthesis results of our method reported in the paper are based on 3 runs. In both tables, the Standard Deviation across runs less than 0.1 for PSNR and 0.01 for SSIM and LPIPS.
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Table 10: The per-scene quantitative results the Objaverse dataset.
Method
FisherRF
Ours
Scene
PSNR
SSIM
LPIPS 4
PSNR
SSIM t
LPIPS 4
statue
38.365
0.985
0.008
42.929
0.993
0.004
warrior
33,851
0.962
0.021
28.046
0.912
0.056
bucket
23.372
0.856
0.085
39.639
0.983
0.012
rose
37.960
0.986
0.007
43.752
0.994
0.003
lion 25,472
0.860
0.081
31.303
0.923
0.054
bacanal
27.928
0.940
0.028
36.161
0.983
0.008
rhino 37.969
0.972
0.023
43.423
0.987
0.013
turtle 36.316
0.971
0.016
41.318
0.987
0.007
Average 32.654
0.942
0.034
38.321
0.970
0.020
E.2 PER-SCENE RESULTS ON BLENDER DATASET
We provide the per-scene quantitative results for the active reconstruction with perfect pose exper- iments on 8 scenes from the Blender dataset. The results are shown Table and Table 7 The cha i scene gains the most significant performance improvements by increasing the view number from 10 to 20, with the PSNR metric increasing by 6.67. We also provide the visualization results of rendered novel views in Figure and Figure
E.3 QUANTITATIVE RESULTS OF ACTIVE RECONSTRUCTION WITH NOISY POSE
In active reconstruction with 4 real-world scenes with noisy pose, we acquire a total of 30 ~ 40 im- ages for each scene, according their reconstructed model quality. We chose every images as the test set, and provide the quantitative results in the four objects captured by our active reconstruction application, Table and T From the average results, we can see that our pose optimization increases the performance of rendered novel views largely. We provide the training video of the active reconstruction gundam scene in the supplementary files.
E.4 RESULTS ON THE OBJAVERSE DATASETS
Apart from the standard object-level radiance field reconstruction dataset, the Blender dataset, further validate the effectiveness and generalization ability of our method on more objects from high-quality dataset 3D Models scanning from real-world objects, the Objaverse dataset Deitke et al. (003).. Specifically, we select and reconstruct 8 objects from the Objaverse dataset, contain- ing objects like statue, bucket, and so on, with variance geometry and scale. Regardless of different object classes, perform active learning for the Objaverse dataset with the same hyper- parameters as the Blender datasets for both methods. We compare the results of our method and the FisherRF Jiang et al. (2023) both quantitatively and qualitatively. In Table I0 compare the quality novel views reconstructed by both methods. Our method outperforms FisherRF image quality metrics for most objects. For the averaged results, our method superior to FisherRF, with an increase in PSNR 5.7. This indicates that the superior performance of our method is general- izable variant objects. We also visualize the reconstructed novel views of both methods, together with the ground truth in Figure g and Figure
E.5 ESTIMATED NEXT BEST VIEWS DISTRIBUTION VISUALIZATION
To understand the feature of the next views generated by our method, we visualize the distribution of all views selected reconstruct the object, in the 10 total views case, together with the object to reconstruct. The distribution views shown Figure I0 and Figure II] For each object, estimated view is distributed evenly around the object, capturing the object from an informative
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viewing pose. The quality of generated views is stable across different types of objects, showing robustness against the shape appearance of different objects.
F LIMITATIONS AND FUTURE WORKS
Our method is designed for object-level active 3DGS reconstruction but has several limitations. inherits weaknesses from the 3D Gaussian Splatting (3DGS) backbone, particularly under transpar- ent or reflective surfaces where geometry is poorly captured. While our pose refinement improves robustness, the system requires clear captured image whose pose is near the estimated next view without significant motion blur. Additionally, the current design targets single-object scenes and re- lies fixed semantic encoders, which may limit generalization in cluttered or open environments.
In the future, we could extend our method to scene-level reconstruction by incorporating semantic segmentation and multi-object reasoning. We could also improve the robustmess of our pose estima- tion in dealing with the large initial drift by integrating learning-ber based estimators, such as DUSt3R and its variants Wang al. (025): Leroy et al. (2024); Wang et al. (2024); Yang et al. (2024). Moreover, adapting the semantic encoder with other point cloud understanding networks Zhao et al. (202T): Park et al. (2022) or introducing class-aware view selection policies could help generalize across object categories or specific downstream tasks.
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Drums
Chair
Ficus
Lego
Hotdog
Supazce
Materials
Mic
Ship
Ground Truth
FisherRF
Ours
Figure 6: The per-seene qualitative results of rendered images using active reconstruction under perfect pose with 10 total views.
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Drums
Chair
Lego
Hotdog
Materials
Ship
Ground Truth
FisherRF
Ours
Figure 7: The per-seene qualitative results of rendered images using active reconstruction under perfect pose with 20 total views.
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Bucket
Bacanal
Rhino
Turtle
Ground Truth
FisherRF
Ours
Figure 8: The per-scene qualitative results of rendered images using active reconstruction using 20 total views on the scenes from the Objaverse dataset.
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Warrior
Rose
Statue
Lion
Ground Truth
FisherRF
Ours
Figure 9: The per-scene qualitative results of rendered images using active reconstruction using 20 total views on the scenes from the Objaverse dataset.
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Ship
Drums
Ficus
Chair
Figure 10: The distribution of all estimated next best views for scenes from the Blender dataset. The total number of views 10.
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Lego
Hotdog
Materials
Mic
Figure 11: The distribution of all estimated next best views for scenes from the Blender dataset. The total number of views 10.
27


Reviews:
[{"review_id": "ASRx6s2oVa", "reviewer": "Reviewer_fPM1", "summary": "This paper presents a novel active learning framework for 3D Gaussian Splatting (3DGS) that guides view acquisition during reconstruction. The proposed method introduces a bottom-up strategy that partitions the model into locally consistent regions based on Gaussian attributes and visibility features, identifies noisy or under-reconstructed areas via a semantic feature variance metric, and generates the next best views accordingly. To further enhance robustness in real-world capture scenarios, the authors incorporate a pose optimization module that refines noisy camera poses using reprojection constraints. Extensive experiments on both synthetic and real-world datasets demonstrate that the approach achieves state-of-the-art reconstruction quality under both accurate and noisy pose settings, validated through comprehensive quantitative comparisons and ablation studies.", "questions": "1. Appendix A.2.2 says K-Means uses “number of clusters equals 10,” yet Table 3 states “5 (Default)” and also reports results for 3/5/7 clusters. Which value was used for the main results, and how sensitive are gains to this choice?\n\n2. You define \\hat{x} = x_j/J as “the center of points,” which seems incorrect dimensionally—should this be \\hat{x} = $\\frac{1}{J}\\sum_{j=1}^{J} x_j$?\n\n3. You “first capture three initial images uniformly.” Do all baselines start from exactly the same three initial views and total budget (including those three)", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "Quality and Clarity: The paper is clearly written and well-structured, with a logical flow from motivation to methodology and experiments. The technical explanations are concise yet sufficiently detailed, making the method easy to follow. Figures effectively illustrate key components such as consistent region partitioning and pose optimization, which enhances overall readability and technical transparency.\n\nSignificance: The experimental results demonstrate state-of-the-art performance across both synthetic and real-world datasets, validating the robustness and effectiveness of the proposed method. The paper’s combination of semantic feature variance for uncertainty estimation and robust pose refinement addresses important limitations of prior active reconstruction approaches, showing strong potential impact for practical 3DGS-based active learning systems in robotics and AR/VR applications.", "weaknesses": "Incomplete coverage of recent literature: While the paper provides a solid overview of classical active reconstruction and next-best-view methods, it omits several recent and highly relevant works such as Naruto: Neural Active Reconstruction from Uncertain Target Observations, ActiveGAMER: Active Gaussian Mapping through Efficient Rendering, and Understanding while Exploring: Semantics-driven Active Mapping. These methods also explore uncertainty-driven or semantic-guided active mapping. A comparative discussion or experimental inclusion of these approaches would better contextualize the paper’s contributions and clarify its novelty.\n\nLack of empirical validation for efficiency claims: The paper repeatedly highlights the proposed bottom-up strategy’s efficiency in generating informative views without candidate sampling, yet no quantitative results support this claim. Metrics such as runtime per iteration, total reconstruction time, or convergence rate compared with baselines like FisherRF or ActiveNeRF would be valuable for substantiating the claimed computational advantage.\n\nAbsence of discussion on limitations and failure cases: The paper does not provide any analysis of scenarios where the method might fail or underperform, such as highly transparent or reflective surfaces, cluttered environments, or extreme pose noise. A brief reflection on these limitations—along with potential directions for improvement—would enhance the paper’s credibility and balance.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "gyla1NGWW7", "reviewer": "Reviewer_dCpe", "summary": "This paper tackles active view selection for 3D scene reconstruction with 3DGS, addressing how current radiance field workflows capture many images empirically without guidance, causing redundant or insufficient coverage. The authors propose a bottom-up active learning algorithm that partitions the scene's Gaussian point cloud into spatially coherent regions based on attributes and visibility, then estimates semantic feature variance per Gaussian to identify the most informative regions to explore next. This semantic variance acts as a high-level uncertainty measure revealing model incompleteness. The method also incorporates robust pose optimization to refine noisy camera poses from sources like SLAM or AR sensors. Extensive experiments on synthetic and real-world datasets show the approach achieves faster, higher-quality reconstruction than existing methods, contributing a novel region-based selection framework, a semantic-level uncertainty metric for 3DGS, and improved pose noise robustness that collectively enhance radiance field acquisition efficiency.", "questions": "1. Figure 3 shows the local consistent region determination, and I wonder about the input view distribution of this demo.\n2. I have noticed that “The image acquisition is performed every 300 steps, after which the model is reinitialized as random points.” Why is this design? Why not continue training?\n3. After collecting all the images, the model undergoes further training for 10,000 steps. Do other models follow the same process, considering that 3DGS is sensitive to the number of training iterations?\n4. What specific “semantic-level features” are used to compute the Gaussian feature variance, and how robust are they across different scenes?\n5. Additionally, the initialization pose is an important consideration. Since this paper focuses on practical applications, the initial pose should be addressed. In sparse settings, where acquiring an accurate pose is challenging, many methods utilize neural matching approaches like VGGT, MASt3R, or DUSt3R. It would be interesting to explore whether these methods can be integrated into the pipeline, similar to GaussianObject. These methods should also be cited.\n6. The authors claim that \"The mesh with after consistent region partition can be found in folder 'partition_results'. \", but none of this folder is provided. \n7. How is the consistent region partition determined in practice, and how sensitive is the method to this partitioning?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 4, "strengths": "- A hard-but-important problem tackled with a clean, region-wise idea: cluster Gaussians by visibility to get consistent parts, then use semantic feature variance to pinpoint what’s under-reconstructed—more direct than image-space heuristics and easy to plug into 3DGS. \n- The pose-refinement loop makes handheld/AR capture practical rather than brittle. Results on NeRF-Synthetic are strong in low-view regimes, and ablations show each module matters. \n- Reproducibility is treated seriously: the authors provide NeRF-Synthetic results and partition meshes (colored by region) so readers can inspect the visibility-based separation, and they commit to open-sourcing the full code, which is great for the community.", "weaknesses": "- Although plausible, the semantic-feature variance remains a heuristic; it’s not rigorously tied to reconstruction error nor carefully pitted against simpler 3DGS-native cues (*e.g.*, RGB/opacity variance or Fisher information). \n- Region partitioning introduces knobs (cluster count/thresholds) whose stability is only lightly explored; a small robustness sweep and an adaptive stopping rule would usefully de-risk it. \n- The pipeline is heavy—visibility sampling, multiple feature passes, clustering, then pose refinement—yet there’s no wall-clock or memory breakdown per NBV iteration, so speed/latency remains unclear for both human-in-the-loop capture and embodied agents (*e.g.*, AR round-trip, pose-refinement iterations, and how N/T/K trade time vs. quality). \n- The design is tailored to 3DGS; portability to NeRF/voxel backbones without explicit splats is non-obvious. \n- Pose refinement’s convergence basin and failure modes (large drift, low-texture regions) are not quantified, leaving robustness bounds somewhat ambiguous. \n- To ensure a fair evaluation of performance, the results for the 12 objects from the Objaverse dataset should be reported. It would also be helpful for the authors to provide a video showing the method's performance across 3 to 20 views, along with comparisons to other methods.\n- Minor: Line 75 \"e.g.\" should be italicized, meaning it should appear as *e.g.* . Some descriptions are ambiguous, making them hard to understand. For instance, Line 230-234. The cite of Alpha shape should be placed on Line 267 rather than Line 292.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "M9tYk0KUUq", "reviewer": "Reviewer_PDuY", "summary": "This paper proposes an *active learning* framework for 3D Gaussian Splatting (3DGS) that jointly performs image acquisition and model training based on the following key findings.\n\n- Consistent Region Partition: the 3DGS is clustered into local visibility-consistent regions using K-Means on a combined feature of position, color, rotation, and visibility.\n- Semantic Feature Variance Score: the region are estimated via variance in Point-MAE semantic features across samples, guiding next-view selection.\n- Pose Optimization: a robust photometric pose refinement desired best capture poses and actual capture poses in real-world.\n\nExperiments on NeRF-Synthetic, real-world handheld captures, and Objaverse show quantitative improvement and demonstrate robustness to pose noise.", "questions": "Please see the suggestions and comments from the Weaknesses section above.\n\nAdditionally:\n\n- In Figure 4, could you explicitly mark the newly added views at each iteration to help visualize coverage progression?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- Idea for online 3DGS training and the capture loop is well motivated and demonstrated via real-world experiment\n- Utilizing point-wise metrics for active learning seems novel compared to demonstrated existing works using image-based metrics.\n- Comprehensive evaluation on synthetic and real data demonstrates robustness to pose noise.", "weaknesses": "1. **Overlap with prior sampling methods.**\n    \n    The von Mises-Fisher camera sampling still relies on probabilistic view selection similar to random hemisphere sampling; a clear distinction from ActiveNeRF or FisherRF seems missing.\n    \n2. **Heuristic semantic variance metric.**\n    \n    The use of dropout variance from Point-MAE features is empirical. An analysis of dropout ratio or random sampling rate effects would be great (Table 4 only partially covers this).\n    \n    Moreover, testing other point-cloud encoders[1][2] could clarify whether improvements generalize beyond Point-MAE.\n    \n3. **Pose optimization noise propagation.**\n    \n    Because $\\mathcal{L}_{pose}$ in Eq.(7) uses rendered images for photometric alignment, any render artifact can bias pose updates. An experiment quantifying pose error after refinement would clarify robustness.\n    \n4. **Lack of qualitative comparison on NeRF-Synthetic dataset**\n    \n    Additonal qualitative comparison on NeRF-Synthetic would strengthen claims of better inconsistent-region handling.\n    \n5. **Re-initialization strategy unclear.**\n    \n    The active loop re-initializes the 3DGS every 300 steps, but it is unclear how densification and opacity reset interact with semantic variance tracking. Ablation on this would be helpful.\n\n[1] Zhao, Hengshuang, et al. \"Point transformer.\" *Proceedings of the IEEE/CVF international conference on computer vision*. 2021.\n\n[2] Park, Chunghyun, et al. \"Fast point transformer.\" *Proceedings of the IEEE/CVF conference on computer vision and pattern recognition*. 2022.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)