Orient Anything V2: Unifying Orientation and Rotation Understanding
Authors: Zehan Wang
Ziang Zhang
Jiayang Xu
Jialei Wang
Tianyu Pang
Chao Du
Hengshuang Zhao
Zhou Zhao
Venue: NeurIPS 2025 spotlight ()
Keywords: Orientation Estimation
6DoF Pose Estimation
Primary area: applications

Abstract: This work presents Orient Anything V2, an enhanced foundation model for unified understanding of object 3D orientation and rotation from single or paired images. Building upon Orient Anything V1, which defines orientation via a single unique front face, V2 extends this capability to handle objects with diverse rotational symmetries and directly estimate relative rotations. These improvements are enabled by four key innovations: 1) Scalable 3D assets synthesized by generative models, ensuring broad category coverage and balanced data distribution; 2) An efficient, model-in-the-loop annotation system that robustly identifies 0 to N valid front faces for each object; 3) A symmetry-aware, periodic distribution fitting objective that captures all plausible front-facing orientations, effectively modeling object rotational symmetry; 4) A multi-frame architecture that directly predicts relative object rotations. Extensive experiments show that Orient Anything V2 achieves state-of-the-art zero-shot performance on orientation estimation, 6DoF pose estimation, and object symmetry recognition across 11 widely used benchmarks. The model demonstrates strong generalization, significantly broadening the applicability of orientation estimation in diverse downstream tasks.

Orient Anything V2: Unifying Orientation and Rotation Understanding
Zehan Wang Ziang Zhang" Jiayang Xu Jialei Wang',
Tianyu Pang", Chao Du Hengshuang Zhao?, Zhou Zhao
IZhejiang University; SShanhaii Al Lab; SSea Al Lab; "The University of Hong Kong https://orient:
htt.://rinnntanttniggg... anythingv2 github.io....
Strong Robustiesss
Absoluate Orientation
Relative
Labels
Rotational Symmetry
Rotation
Pscudo ensemble
Encoder
Diverse Pose
Assets
Reulistic Texture
Encoder
Eucoder
Genemtive model
Class
Full Coveruge
Names
Perfect Balance
Data: Scalable Data Engine
Model: Orient Anything V2
New Features: Symmetry & Rotation
Figure Overview of Orient Anything V2. We upgrade the foundation orientation estimation model from both Data and Model perspectives. It unifies the understanding of object orientation and rotation, achieving better estimation accuracy and gaining the New Features to handle rotational symmetry and relative rotation. Zoom in for the best view.
Abstract
This work presents Orient Anything V2, an enhanced foundation model for unified understanding of object 3D orientation and rotation from single or paired images. Building upon Orient Anything V1, which defines orientation via a single unique front face, V2 extends this capability to handle objects with diverse rotational symmetries and directly estimate relative rotations. These improvements are en- abled by four key innovations: 1) Scalable 3D assets synthesized by generative models, ensuring broad category coverage and balanced data distribution; An efficient, model-in-the -oothh-loop annotation system that robustly identifies 0 to N valid front faces for each object; 3) symmetry-aware, periodic distribution fitting objective that captures all plausible front-facing orientations, effectively modeling object rotational symmetry; 4) multi-frame architecture that directly predicts relative object rotations. Extensive experiments show that Orient Anything V2 achieves state-of-the-ats zero-shot performance on orientation estimation, 6DoF pose estimation, and object symmetry recognition across 11 widely used bench- marks. The model demonstrates strong generalization, significantly broadening the applicability of orientation estimation in diverse downstream tasks.
"Equal Contribution.
Project Leader.
*Correpponding Author.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Introduction
Estimating object orientation from images is a fundamental task of computer vision. 3D object orientation information plays crucial roles in robot manipulation [49, 37, 22], autonomous driv- ing [25], AR/VR [11, 30, 4, 57], and spatial-aware image understanding [21, 28, 60J and genera- tion [56, 35].
Orient Anything V1 [48] is a foundation model for estimating the object orientation aligned with an object's unique front face. While it exhibits strong robustness and accuracy absolute orientation estimation, it lacks an understanding of rotation (despite its intrinsic link to orientation). This deficiency results in difficulties handling numerous rotationally symmetric objects (simply classifying them as having no front face) and understanding object rotation relative to a specified reference frame. These limitations around rotation understanding restrict its utility in many downstream tasks.
In this work, we aim to develop an enhanced orientation estimation model, Orient Anything V2, with stronger generalization and deeper understanding of both object orientation and rotation. Our contributions include a scalable data engine and a more elegant model framework.
From the data perspective, Orient Anything VI uses advanced VLM [12, 31] to annotate real 3D assets from Objaverse [8, 7]. Building on this data-driven motivation, we leverage advanced 3D generation models [51, 61] to further speed up data scaling-up and improve the data coverage and balance. Additionally, we assemble pseudo labels predicted by the VI model across multi-view renderings and refine them through modellin-the-loeee highly cost-effective and flexible data scaling up, delivers robust annotation performance, and shows a strong understanding of rotationally symmetric objects. Our final dataset includes 600K assets, 12x larger than the existing orientation dataset, with significantly higher annotation quality, accurately identifying 0 to N valid front faces.
calibration. The proposed data engine enables
From the model perspective, we first propose symmetry-awe orientation distribution, explicitly teaching the model to capture and predict rotational symmetry. Moreover, our model supports multi- frame input directly predict relative rotations between frames. This design effectively bridges the knowledge transfer between absolute orientation and relative rotation, showing strong potential in reference- -nnwn scenarios.
Our experiments demonstrate the enhanced and novel capabilities of our model. achieves superior performance on zero-shot orientation estimation and sets new records on zero-shot rotation estimation (i.e., 6DoF pose estimation [49, 26]), while also accurately handling and predicting different rotational symmetries.
To summarize, we propose Orient Anything V2, which improves Orient Anything VI as follows:
We propose a data engine that cost-efficiently scales up 3D asset collection and robustly annotates the 0 to N valid front faces to capture different object rotational symmetries.
We introduce symmetry-awee aware distribution fitting as learning objective, allowing the model to directly predict all plausible object orientations.
We extend the model architecture to support multi-frame input, enabling it to directly estimate relative object rotations over the reference frame.
Our model demonstrates strong zero-shot generalization across absolute orientation estima- tion, relative rotation estimation, and object symmetry recognition.
2 Related Work
2.1 Object Rotational Symmetry
Rotational symmetry [36, 34] indicates that an object may retain its original shape after being rotated by certain angles. This property is commonly found across various objects. Understanding an object's rotational symmetry is critical for 3D object recognition and generation [24, 59], pose estimation [17, 6]. and robotic manipulation [39]. While some existing works [40] attempt to detect 3D rotational symmetry from single-view 2D images, they are constrained by limited training data and lack zero-shot generalization to open- open-world scenarios.

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OO
(a) Low-quality Texture
(b) Limited Pose&Shape
Figure 2: Real assets from Objavese suffer from (a) low-quality texture and (b) limited realism.
Our focus is on object orientation relative to semantic "front" face. The number of possible valid front-facing orientations an object possesses is determined by its rotational symmetry around its vertical axis. For example, 180-degree symmetry means there are two distinct valid front faces. Objects with continuous rotational symmetry (symmetric at any angle). like balls, are considered to have no meaningful direction. In this work, we broaden the applicability of orientation estimation models by enabling the prediction of an object's 's azimuthal symmetry from a single 2D image. Our model demonstrates impressive zero-shot rotational symmetry recognition performance.
2.2 Relative Rotation Estimation
Predicting an object's 's rotation in the query frame relative to the reference frame is a fundamental capability in 6DoF pose estimation [26, 14. 55] and is crucial for robotics applications. Early methods [23, 18, 9] focused on specific instances or object categories. More recent approaches like OnePose [44] and OnePose++ [15] estimate object rotation by solving 2D-3D correspondences across views. POPE [10] follows a similar idea and achieves zero-shot rotation estimation with a single reference frame with the help of SAM [19] and DINOv2 [33]. However, the reliance on pixel matching makes these methods prone to failure under large viewpoint changes.
In contrast, we propose purely implicit learning approach. Leveraging the inherent coupling between rotation and orientation, we extend the orientation estimation model to support multi-frame inputs, enabling direct zero-shot relative rotation prediction between arbitrary views.
2.3 Single-view Orientation Estimation
Estimating an object's 3D front-facing orientation (interpreted as its rotation relative to the canonical front view) from a single view, requires the model to have an inherent understanding of different objects' standard poses and front-facing appearances. Earlier works [53, 46, 42] are mainly limited small number of categories or specific domains. More recently, ImageNet3D [29] introduce large-scale dataset with manually annotated 3D orientations. Orient Anything [48] achieves robust orientation estimation for any objects in any scenes by leveraging an advanced automated annotation pipeline, improved learning objectives, and real-world knowledge from the pre-trained vision model.
In this work, further address several limitations of Orient Anything and upgrade the orientation estimation model from both data-driven (novel and scalable data engine) and model-driven (direct symmetry and rotation prediction) perspectives, resulting in Orient Anything V2.
Revisiting Orient Anything V1
Orient Anything V1 pioneers zero-shot object orientation estimation from single images. introduces a VLM--ased pipeline to annotate front faces of Objaverse 3D assets [8, 7], learns orientation estimation from their renderings via distribution fitting. It also provides confidence score indicate whether an object has a unique front face. To further advance the orientation prediction foundation model, we first dig into the potential limitations in training data and framework.
visadvan of Real 3D Assets 1) Imbalanced Category Distribution: Stemming from the
human biases asset creation, real 3D datasets, such as Objaverse [8, 7], suffer from significant class imbalance. Common categories like buildings and characters make up large proportion, while

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LLM
Image Generation
3D Mesh
Class Tag
Caption
Image
3D Mesh
Rewrite
Generation
Suats plank repr
Tounet armumblyy neggaiel
Cage
Computer
Heater
Farmer
Fence
Fightee
Aieeato Jrner
Worker
Figure 3:
Overview of 3D Asset Synthesis Pipeline. We begin with class tags and use series of advanced generative models to progressively generate high-quality 3D assets.
others, like uncommon animals, are severely underrepresented. 2) Inconsistent Data Quality: Current large-scale 3D datasets often lack high-quality assets with complete geometry and rich surface details. (Fig. 2 a) Moreover, many human-created meshes exhibit fixed poses, leading to a substantial domain gap from real-world object variations (Fig. 2 b).
Limitations of Object Rotation Understanding 1) Ignored Rolational Symmelries: Orient Any- thing VI defines orientation based on the single, unique front face, overlooking the different rotational symmetries (i.e., multiple valid "front" faces). For the many symmetric objects in real world, the model cannot effectively distinguish or identify their potential orientations. 2) Unsupported Relative Rotations: The relative rotation between two views and the front-facing orientation (essentially the rotation relative to the front view) are inherently coupled. However, estimating relative rotation through independent absolute orientation predictions suffers from significant enror accumulation, causing Orient Anything VI to often fail in relative rotation estimation.
4 Scalable Data Engine
4.1 3D Asset Synthesis
Motivated by the recent remarkable progress in generative models and the successful application of synthetic data in downstream tasks [45, 56], we explore whether symthetic 3D assets serve as scalable, high-quality data sources for orientation learning. To fully harness modern generative models, construct our asset synthesis pipeline structured process: Class Tag Caption Image + 3D Mesh as detailed below:
Step 1: Class Tag - Caption. To ensure broad category coverage and diversity, we follow SynCLR's approach [45], starting from ImageNet- [38] category tags, and use Qwen-2.5 [54] to generate rich captions that describe detailed object attributes and diverse poses. Step 2: Caption
Image. soxttt--mmae model. FLUX.I-DEE [20], to generate images following the
We use the sfate-of-the-ar captions. Besides, enhance captions with positional descriptors promote explicit 3D structure and upright pose. Step 3: Image + 3D mesh. We employ the leading open-source image-to-3D model, Hunyuan-3D-240 [61], to produce high-quality 3D meshes from the synthesized images.
Finally, we generate 600k 3D assets total, with approximately 30 items for each class tag in ImageNet-21K. These assets feature complete geometry, detailed textures, and balanced category coverage. In terms of scale, the new synthetic dataset is 12x larger than the filtered real dataset used in Orient Anything VI.
4.2 Robust Annotation
Orient Anything employs VLM annotate the unique canonical front view 3D assets. However, this approach is limited by the VLM's underdeveloped spatial perception ability and struggles to handle diverse rotational symmetries. address these challenges, we introduce more effective and robust system for annotating 3D asset orientations.

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Multi-view Rendering
model Annotating
Pseudo Label
Voting Standardize
Automatic Label
Manual
Final
Calibration
Label
VISS
Figure 4: Overview of Robust Annotation Pipeline. "Pseudo Label" visualizes the azimuth direction of pseudo labels and objects the horizontal plane. By fitting the pseudo labels to standard periodic distribution, we can robustly derive the orientation and symmetry label. Human calibration is only required for categories with symmetry inconsistencies.
Intra-asset Ensemble Annotation We first train improved orientation estimation model as automatic annotator, based on the Orient Anything VI paradigm and incorporating the additional real-world orientation dataset, ImageNet3D. Next, for each 3D asset, we employ this model to produce pseudo-labels for various renderings. Finally, we project these pseudo-labels, obtained from different viewpoints, back into canonical 3D world coordinate system.
As shown in Fig. the overall distribution of pseudo-labee labels on the horizon plane clearly indicates the object's possible orientations. To capture the main direction and rotational symmetries, we first arrange the discrete predicted azimuth angles over [0", 3609) into a probability distribution P psendo E 3300 This distribution is then fitted to a periodic Gaussian distribution using the least squares method:
(oslalt-p0 إ|ر
2
359 Σ
exp
(0, = arg min
pesudo (i)
2 7
,, i=0
where ō is the fitted variance, The phase [0 360") represents the main azimuth direction. The periodicity ā 21. N signifies (a00aadderree rotational symmetry, possessing valid front faces, while ā = 0 indicates no dominant orientation.
Ensembling multiple pseudo labels in the 3D world effectively suppresses outlier errors from single- view predictions, resulting in significantly more reliable annotations.
Inter-assets Consistency Calibration Building on the rotational symmetry and orientation anno- tations for individual assets, we further perform comman-n--the-lwee consistency calibration across assets. Specifically, since our 3D assets are generated based on object category tags, they are naturally grouped by category. We assume that objects of the same category should share the same type of rotational symmetry. Based on this assumption, we analyze the annotated rotational symmetries within each category. all assets within the same category demonstrate the same symmetries, we directly consider the annotations be correct. If inconsistencies are found, we manually review all assets in that category to re-annotate or filter out incorrect annotations.
As each asset is annotated independently, the cross-asset consistency check and manual calibration offer an orthogonal perspective that efficiently and effectively enhances annotation reliability. Statis- tically, across 21k source category tags, we observe only minor inconsistencies in around 15% of categories, each involving small number of assets. The finding further validates the accuracy and robustness of our ensemble annotation strategy.
5

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Orientation Token
Encoder
Joint Encoder
Rotation Token
Encoder
Figure 5: Framework of Orient Anything V2. One or two input frames are tokenized by DINOv2 and then jointly encoded using transformer blocks. We finally employ MLP heads to predict the orientation or rotation distributions from the encoded learnable tokens of each frame.
Framework
5.1 Symmetry-aware Distribution
Orient Anything V1 proposes an orientation distribution fitting task that guides the model to learn eircular Gaussian distributions azimuth, polar, and in-plane rotation angles, that preserve the similarity between neighhoring angles. Each angle is modeled with a unimodal target distribution centered on unique front-facing orientation. For symmetric objects with multiple front faces, the model additionally predicts a low orientation confidence to filter them out.
semantic
To recognize different types of rotational symmetry and enable general orientation prediction for objects with multiple front faces, we further introduce the symmetry-aware periodic distribution as the training target. As discussed in Sec. 4.2, ensemble annotation and consistency calibration approach enables accurate and robust labeling of 0 to N valid front-facing directions over the horizontal plane. To incorporate these annotations into prediction, directly model 0 to N valid front faces within the azimuth angle distribution. This design naturally replaces VI's extra orientation confidence design. Instead, different kinds of rotational symmetries captured directly from the predicted probability distribution. This more elegant framework enables the model to inherently share knowledge across all object categories.
For training, the target Pazi E R-360 for the azimuth angle, originally represented as circular Gaussian distribution, is adapted to be periodic:
cosfa (i-p)) Ø1) g2
exp
azi (i)),
(2)
2
where & and ä are the phase (azimuth angle) and periodicity (rotation symmetry) fitted from Sec, 4.2, is the variance hyper-parameter, and i 0" ....., 359 is the angle index. Target probability distributions for the polar angle P pol E TR 180 and in-plane rotation angle P Proc E 36000 are constructed using a similar method, but without the periodicity parameter.
During inference, the predicted angle distributions are fitted a standard distribution model using the least squares method, similar to Eq. 1. The resulting parameters (azimuth periodicity â azimuth angle & polar angle ē and rotation angle 8), directly indicate the object's á valid front faces (i.e., symmetric with 360/a a degree rotation) and their corresponding front-facing directions in 3D space.
5.2 Relative Rotation Estimation
To establish connection between absolute orientation and relative rotation, enabling knowledge sharing and transferring, we modify the network architecture to support dynamic inputs from one or multiple images.
As shown Fig. 5, we mainly follow VGGT [47], first using a visual encoder, DINOv2 [33], to encode each input image into K tokens, augmented with learnable tokens. The combined set of

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SUN-RGBD
ARKitScenes
Pascal3D+ Medy
Objectron
ImageNet3DI
Ori COCO
Model
Medj Acc30*f
Medj
Ace30*
Acc30*
Medj
Acc30*
Medj Acc30*f
Acct
OriAny.VI 33.94
48.5
77.58
35.8
22.90
55.0
30.67
49.6
13.34
71.3
72.4
OriAny.V2 26.00
55.4
36.48
43,2
15.02
72.7
22.62
56.4
15.26
65.2
86.4
Table I: Zero-shot Absolute Orientation Estimation. 1 ImageNet3D is used for training Orient Anything V2. To ensure fair comparison, the compared VI model is fine-tuned on ImageNet3D. Best results are highlighted in bold.
tokens from all frames then passed into a unified transformer block. The final learnable token corresponding to each frame is used for prediction. Specifically, the learnable token for the first frame initialized differently and is used to predict the absolute orientation using the symmetry-an distribution described in Sec. 5.1. Tokens from subsequent frames predict the object rotation relative to the first frame through a similar probability fitting task, but without considering symmetry.
5.3 Training Setting
Our model is initialized from VGGT, large berd-forward transformer with 1.2 billion parameters pre-trained on 3D geometry tasks. We repurpose its original "camera" token, designed to predict camera extrinsics, to predict object orientation and rotation. This leverages the inherent correlation between camera pose and object rotation. We train the model to fit target orientation (or rotation) distributions using Binary Cross-Entropy (BCE) loss for 20k iterations. A cosine learning rate scheduler is used with an initial rate of 1e-3. Input frames are resized to 518, and random patch masking is used for data augmentation simulate real-world occlusion. The effective batch size is set to 48, where 1-2 frames are randomly sampled for each training sample. The training dataset comprises the ImageNet3D training set and newly collected 600k synthetic assets. Furthermore, we observe that most objects exhibit only four types of rotational symmetry: {0, 1, 2, 2) Therefore, we restrict our training to consider only these four cases. Any fitted periodicity ā > is mapped to
6 Experiment
6.1 Zero-shot Orientation Estimation
Benchmark & Baselines Predicting the 3D orientation of objects from single image is our focus. We mainly compare with Orient Anything V1 [48] on ImageNet3D [29] test set and unseen test datasets. SUN-RGBD [41], ARKitScenes [3], Pascal3D+ [52]. Objectron [1] and the Ori COCO [48]. Since current testing datasets often provide only one ground truth orientation, even for symmetric objects, when Orient Anything V2 predicts multiple orientations, simply select the one closest to facing the camera as the prediction. The main evaluation metrics are the median 3D) angle error (Med[) and accuracy within 30 degrees (Acc30%t). For Ori DOC,, where 20 samples are collected for each class and annotated within 8 horizontal orientations, recognition accuracy (Acef) is used.
Main Results In Tab. 1, we present the comparative results on single view-based orientation estimation. Overall, Orient Anything V2 significantly improves upon V1, benefiting from diverse synthetic data and robust ensemble annotation. On the representative Ori_COCO benchmark, our method achieves 86.4% accuracy and performs well on categories where VI struggled, such as bicycles. Achieving state- of-the-art results on numerous real-world image datasets highlights our method's generalization ability.
6.2 Zero-shot Rotation Estimation
Benchmark & Baselines We benchmark zero-shot 6DoF object pose estimation performance under a single reference view. Evaluation conducted on four widely used datasets: LINEMOD [16]. YCB-V Video [5], OnePose++ [15], and OnePose [44]. Objects are prepared using the cropping and matching, following [10]. Comparisons are made against three state-of-the-art zero-shot 6DoF object pose estimation methods: Gen6D [27], LoFTR [43], and POPE [10]. Standard metrics for relative object pose estimation are used: median error (Med) and accuracy within 15 and 30 (Acc15 15 and Acc30), computed for each sample pair.
7

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LINEMOD Medj Acc30f
YCB-Video
OnePose++
OnePose
Model
Acc15t Med, Ace30r
Acc15t Medy Acc30t
Acc15 Med,
Acc30t Acc15t
POPE's Sampling (Average rotation angle: 14.85")
Gen6D
44.86
36.4
9.6
54.48
23.2
7.7
35.43
41.1
15.8
17.78
89.3
38.9
LoFTR
33.04
56.2
32,4
19.54
68.6
47.8
9.01
89.1
70.3
4.35
96.3
91.8
POPE
15.73
77.0
48.3
13.94
80.1
54.4
6.27
89.6
72.8
2.16
96.2
91.1
OriAny. V2 7.82
98.07
89.7
6.07
91.6
86.4
6.18
99.7
96.6
6.76
99.7
95.7
Random Sample (Average rotation angle: 78.22")
POPE 98.03
10.3
4.3
41.88
40.9
27.2
88.21
25.6
19.8
45.73
45.1
37.3
OriAny. V2 28.83
51.6
28.3
15.78
61.2
48.7
12.83
85.5
58.8
11.72
86.7
63.4
Table 2: Zero-shot Relative Rotation Estimation (i.e., pose estimation with one reference view). We evaluate two strategies for sampling query-reference view pairs: (1) query-reference pairs provided by POPE [10], and (2) randomly sampled pairs. The average rotation angles between views for each sampling strategy are 14.85 and 78.22", respectively.
Main Results Tab. 2 includes zero-shot two-view relative rotation estimation results compared with state-of-the-ard pose estimation methods. With small relative rotations (using POPE's sampling), our model achieves the overall best performance across the four datasets. More importantly, our method's advantage significantly larger when the relative rotation between the query and reference frame is larger (using random sampling). The significant performance drop of the previous method stems from the reliance on explicit feature matching, which becomes unreliable with large rotations due to less view overlap and scarcer reliable matching points. In contrast, our approach understands images from different viewpoints by considering overall meaning rather than just detailed matching. This makes it more robust challenging large rotations.
6.3 Zero-shot Symmetry Recognition
Benchmark & Baselines We assess our method's zero-shot performance in predicting object
rotational symmetry in the horizontal plane. This evaluation the recent, large-scale 3D object datasets with rotational symmetry annotations: Omni6DPose [58], which contain 149 distinct object classes. To ensure their orientation definition aligns with our front-facing direction, select a subset of 3-5 assets per category and render 2 views per 3D asset for testing. This resulted in 838 testing sample. During inference, models receive single rendering and predict the four kinds of rotational symmetry predictions. As there are currently no dedicated zero-shot models for predicting object rotational symmetry from single view, we employ advanced VLMs (0wen..5VL-72B [2], GPT-4o [31], GPT-o3 [32], and 2emini-2.5-po [13]) as baselines. We evaluate their ability to predict horizontal plane rotational symmetry using a multiple-choice format, with recognition accuracy as the metric.
manually
Main Results We present a comparison of our method against various advanced general VLMs for identifying object horizontal rotational symmetry in Tab. 3. Our results indicate that recognizing object rotational symmetry is challenging problem even for the strongest VLMs, thereby limiting their ability fully understand the 3D spatial state from 2D images. In contrast, benefiting from high- high-quality annotations and unified learning objective, our model achieves 65% accuracy in distinguishing object rotational symmetry. Combining this strong symmetry recognition ability alongside the robust and accurate absolute orientation estimation performance demon- strated Sec. 1, our model can accurately infer multiple potential orientations from single image in real applications.
Omni6DPose
Acct
Random
25.0
Qwen2.5VL-72B
55.8
Gemini-2.5-pro
44.4
GPT-4o
62.5
GPT-o3
53.7
OriAny. V2
65.2
Table 3: Zero-shot horizontal rota- tional symmetry recognition.

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0 Front Face
Front Faces
2 Front Faces
4 Front Faces
Figure 6: Visualization of synthetic 3D assets and robust annotation.
Orientation Estimation
Rotation Estimation
Assets Type
Assets Number
Initialized Weights
Row
Objectron
Ori_COCO
LINEMOD
YCB-Video
Mcd.
Acc151
Acct
Mcd]
Acc151
Med.
Acc151
1
Real Synthetic
40K
VGGT
25.05
54.3
74.8
10.70
69.8
15.49
72.5
2
40K
VGGT
24.44
54.6
74.6
10.16
74.1
7.28
76.2
3
Synthetic
200K
VGGT
23.82
55.0
75.2
10.22
74.5
7.49
78.6
4
Synthetie
400K
VGGT
25.09
53.9
75.4
9.78
76.3
6.48
80.5
5
Synthetic
600K
VGGT
22.62
54.8
86.4
7.82
89.7
6.07
84.7
6
Synthetie
600K
DINOv2
26.70
52.6
79.0
15.11
49.1
13.78
52.6
Synthetic
600K
None
62.08
13.5
25.6
16.54
45.3
13.93
52.2
Table 4: Ablation study. For the rotation estimation, we employ POPE's sampling pairs.
6.4 Ablation Study
Quality of Synthetic 3D Assets Fig. 6 visualizes our synthetic dataset and the labelled orientation, qualitatively demonstrating the high quality of both the synthetic data and its annotations. Quantita- tively, Rows 1 and 2 of Tab. show the comparison of training with an equal amount of annotated real or synthetic 3D assets. We observe that both data sources yield comparable results for absolute orientation estimation. However, for rotation estimation (on LINEMOD and YCB-Video) training with synthetic assets provides a significant advantage. This may be because synthetic assets possess richer, more realistic textures, which are more crucial for understanding rotation.
Effect of Scaling Data In Rows 2, 3, 4, and of Tab. 4, we explore the impact of data scale on the performance of final orientation and rotation estimation. Overall, with the same training step, encountering more diverse data and 3D assets during training leads to better overall performance. Specifically, we find that rotation estimation is more sensitive data scale than orientation estimation. This may be because orientation relies on overall semantics and structure, while rotation estimation requires understanding diverse textures and fine-grain details to capture cross-view relationships.
Effect of Geometry Pre-training Tab. (Rows 5-7) presents our experiments of different model initialization strategies. Training without any pre-trained initialization yields the worst results. Initializing the separated visual encoder with DINOv2 introduces valuable high-quality semantic and object structure information, leading to substantial performance gains. We observe further improvements rotation estimation by using VGGT, pre-trained specifically on 3D geometric tasks, which boosts the model's comprehension of object geometry.
7 Conclusion
We present Orient Anything V2, an advanced model for unified object orientation and rotation understanding. Through introducing the scalable data engine, symmetry-awar distribution learning target, and a mqulti-frame framework, our model enables: 1) Stronger single-view absolute orientation estimation. 2) Advanced two-frame object relative pose rotation estimation. 3) Powerful object horizontal rotational symmetry recognition. In practice, the model can simultaneously and accurately predict multiple valid front faces of objects, making well-suited for diverse objects and real-world application scenarios.

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Limitation While our models exhibit strong generalization to diverse in-the-wild objects in real images, we find that the inherent ambiguity of monocular images leads to less accurate predictions in views with very low information or severe occlusion. Furthermore, the current framework supports a maximum of two input frames. Extending the model to handle more frames will be an important direction for supporting video understanding applications.
Acknowledgements
This work was supported in part by National Key R&D Program China (No. 20222D0162000) and National Natural Science Foundation of China (No. 62222211, U24A20326, 624B2128, 62422606 and 62201484)
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A More Visualizations of Images in The Wild
In 78,9, 10, 11, 12, 13, we present more visualizations of images from various domains containing different objects. In these images, our model shows strong abilities in single-view absolute orientation estimation, powerful object horizontal rotational symmetry recognition and two-frame object relative pose rotation estimation, further highlighting the impressive zero-shot capability of Orient Anything V2,
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Figure 7: Relative pose rotation estimation for images in the wild.
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Figure 8: Orientation estimation and Rotational symmetry recognition results on objects has no front direction.
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Figure 9: Orientation estimation and Rotational symmetry recognition results on objects has one front direction. Part 1.
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Figure 10: Orientation estimation and Rotational symmetry recognition results on objects has one front direction. Part 2.
Figure 11: Orientation estimation and Rotational symmetry recognition results on objects has two front direction. Part 1.
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Figure 12: Orientation estimation and Rotational symmetry recognition results on objects has two
front direction. Part 2.
Figure 13: Orientation estimation and Rotational symmetry recognition results objects has four front direction.
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Reviews:
[{"review_id": "WJ8gDVxJ2t", "reviewer": "Reviewer_13NV", "summary": "This paper proposes a new model for single-view and multi-view viewpoint estimation and symmetry detection. The model builds on the existing Orient Anything model and extends that approach through proposing a novel data engine that leverage generative and VLM models. The data engine uses LLMs to generate detailed object descriptions for a large number of object types, text2mesh models to generate the 3D meshes, and VLMs to identify the front view (through a process of multiview generation, voting, and human calibration at the end). The proposed model starts with the VGGT model and trains it with the generated assets and annotation to estimate the object orientation and possible symmetries. The proposed approach achieves impressive performance on several datasets. The paper also details several ablations to evaluate impact of scale, weight initialization, and asset type.", "questions": "- How well does Orient Anything V1, VGGT, and RelPose perform on relative rotation estimation? While I think all 3 comparisons are important, VGGT seems to be the crucial one give the results shown in Table 4. \n- The paper proposes a novel data engine and the resulted generated dataset. However, it's unclear from the text of the paper whether there are plans to release the data engine (code to generate this data) or the generated artifacts. The checklist asks the reader to refer to Sec 6, but it was unclear from Sec 6 what the plans are. Could you please clarify this? As the guidelines indicate, this is not a requirement but it does impact the contribution of the work to the community. \n\n\nThe following questions are less important and more of curiosities about how the model would perform under different edge cases. I think discussing them would improve the paper. \n- The paper appears to make an assumption regarding rotation symmetries only existing around the vertical (or up) vector (L66). This assumption doesn't seem to always hold; eg, a strafish's front view is likely facing the star shape, resulting in the rotation being relative to the front vector. I am curious how you think the model would react to something like this? Similarly for a wheel or a log, both of which will have rotation symmetries around right/left or front/back vectors.\n- Another example I am curious about is a sphere which would have 0 front faces (as well as no well defined up vector). This is discussed briefly in L70 as having no meaningful direction, but I am curious how the model would perform on such a case.", "limitations": "Yes.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**Strengths**\n- The paper is very well written. I especially appreciated the nuance and care taken in explaining the challenges with rotation symmetry and what examples don't make sense, as well as the limitations of prior work. I also appreciated the discussion of the limitations of real data and what generative models can provide. \n- The data engine is very well motivated and explained. The figures were especially helpful to understand what's going on. The manual verification and calibration also seems like a lot of work (L162-166). \n- The experimental section is nicely presented and the ablations done make a lot of sense. \n\n**Weaknesses**\n- The paper mentioned that Orient Anything V1 struggled in the relative rotation estimation task (L108, L114), yet, it's not evaluated on the relative rotation estimation task.\n- Similar to the previous point, VGGT (which is the basis for the proposed model) and RelPose [ref1] both seem like relevant comparisons for the relative pose estimation task. VGGT is incredibly relevant given that the results in Table 4 frame as a major contributor to the model's performance. \n- The paper makes several claims about limitations in the literature that ignore existing work on rotation symmetry and relative pose estimation especially regarding lack of open-world scenarios and limitation to category-specific methods. There exists several papers that tackled those problems in the past. For example, Ref2 predicted a probability distribution for single image rotation that capture rotation symmetries. Similarly, Ref1 predicted a distribution for pairwise views that also accounted for rotation symmetries. In terms of relative rotation estimation, Ref1 has been applied in more open-world settings. Furthermore, Ref3-4 were earlier works for relative pose estimation that did not make category/object specific assumptions. \n\n\nReferences:\n- [Ref1] Zhang et al. \"Relpose: Predicting probabilistic relative rotation for single objects in the wild.\" European Conference on Computer Vision. Cham: Springer Nature Switzerland, 2022.\n- [Ref2] Murphy et al. \"Implicit-PDF: Non-Parametric Representation of Probability Distributions on the Rotation Manifold.\" International Conference on Machine Learning. PMLR, 2021.\n- [Ref3] El Banani et al. \"Novel Object Viewpoint Estimation Through Reconstruction Alignment.\" 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2020.\n- [Ref4] Park et al. \"Latentfusion: End-to-end differentiable reconstruction and rendering for unseen object pose estimation.\" Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2020.", "was_revised": true, "final_justification": "After looking at the reviews and the authors' responses, I would recommend the acceptance of this work. This paper addresses an interesting problem, proposes a holistic solution that combines improvements in representing the problem as well as a scalable data engine. The paper also analyzes the various components of the pipeline to understand the contribution of different model/data-engine components and design choices. As a result, I think this work would be valuable.", "rebuttal": "Thank you for your supportive review and suggestions. Below we respond to the comments in **Weaknesses (W)** and **Questions (Q)**.\n\n---\n\n## W1 & W2 & Q1: More baselines in relative rotation estimation tasks\n\nWe report the median error (Med↓) of more baselines, Orient Anything v1, VGGT, and two versions of Relpose (co3dv1 and co3dv2), on the relative rotation prediction benchmark as follows:\n\n| Model              | LINEMOD | YCB-Video | OnePose++ | OnePose |\n|--------------------|---------|-----------|-----------|---------|\n| **POPE's Sampling**|         |           |           |         |\n| Gen6D              | 44.86   | 54.48     | 35.43     | 17.78   |\n| LoFTR              | 33.04   | 19.54     | 9.01      | 4.35    |\n| POPE               | 15.73   | 13.94     | **6.27**  | **2.16**|\n| VGGT               | 16.64   | 10.97     | 17.76     | 14.08   |\n| Relpose-co3dv1     | 16.74   | 13.53     | 14.54     | 11.37   |\n| Relpose-co3dv2     | 14.27   | 9.76      | 12.99     | 9.52    |\n| OriAny. V1         | 22.67   | 16.27     | 13.32     | 22.30 |\n| OriAny. V2         | **9.45**| **6.53**  | 7.62      | 8.53    |\n| **Random Sampling**|         |           |           |         |\n| POPE               | 98.03   | 41.88     | 88.21     | 45.73   |\n| VGGT               | 83.96   | 20.75     | 52.18     | 40.30   |\n| Relpose-co3dv1     | 100.05  | 43.17     | 81.35     | 76.67   |\n| Relpose-co3dv2     | 95.49   | 31.21     | 83.21     | 47.48   |\n| OriAny. V1         | 86.63   | 43.30     | 26.88     | 75.19   |\n| OriAny. V2         | **32.08**| **13.78**| **12.94** | **16.29**|\n\n---\n\n## W3: Lack of discussion on existing work on rotation symmetry and relative pose estimation\n\nThank you for your suggestion. We will revise our wording and add discussions of these papers in the updated version. In particular, Relpose [Ref1] demonstrates better generalization to open-world scenarios, and we further include it as one of the baselines for the relative pose estimation task. The comparison results are provided in **our response to your W1 & W2 & Q1 above**.\n\n---\n\n## Q2: Open-sourcing the data engine and resulted dataset\n\nWe will do our best to open-source our work. Each part of our data engine is based on open-source models or methods, and we will organize and release our data engine pipeline. However, for the resulted datasets, the situation is more complex: our 3D object assets were generated using Hunyuan-3d-2.0, and we need to further confirm the license with the Hunyuan team before deciding whether to release large-scale generated 3D assets.\n\n---\n\n## Q3: Only considering vertical rotation symmetries\n\nTo simplify the problem, we only considered vertical rotation symmetry, which covers most cases. However, as you pointed out, some objects may exhibit rotational symmetry along the left-right or front-back axes. We believe that multi-view rendering and prediction voting along these axes may help capture such symmetries.\n\n---\n\n## Q4: How does the model perform for spheres\n\nThank you for raising this point. We tested several sphere cases and observed some interesting phenomena. For spheres placed on a plane (with background), the model predicts a pitch angle relative to the supporting plane (i.e., the object's up vector is perpendicular to the plane). For spheres without a background, the model does indeed predict a random 'up' direction, which makes sense because any angle is valid. This aligns with human perception and observation.\n\n---"}, {"review_id": "YYMWIXlZZH", "reviewer": "Reviewer_k3h2", "summary": "This paper focuses on the problem of estimating object orientation from real world images. It builds on Orient Anything V1 and makes multiple improvements to the data collection and training pipeline to enhance its capabilities.\n\nFirst, they use recent 3D generative models to collect a diverse dataset of objects much beyond the previous work on Orient Anything V1. Additionally, they also use some heuristics to annotate multiple front faces on the horizontal plane for objects which are symmetric. \n\nThey then train a model to predict multiple plausible orientations of the object using this dataset, which is a significant advance compared to Orient Anything V1 which predicted only one possible orientation. Further, unlike Orient Anything V1 the same model is also capable of estimating the relative rotation between a pair of frames.", "questions": "1. Can the method be extended to work on scenes with multiple objects? It would be interesting to see some results on this and whether the model would generalize. The test datasets contain only one object per scene.\n\n2. I’m also curious if the method can work on arbitrary object classes which don’t exist in the training set. Is there a way to evaluate this?", "limitations": "yes", "rating": 6, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 4, "significance": 4, "originality": 4, "strengths_and_weaknesses": "**Strengths:**\n\n1. The paper demonstrates impressive results across multiple tasks. For single-image absolute orientation estimation, the model outperforms Orient Anything V1 by a large margin. It also introduces two additional tasks—relative rotation estimation and horizontal-plane symmetry detection—and shows superior performance compared to strong baselines.\n\n2. I found Orient Anything V1 to be quite limited in applicability due to its inability to handle symmetric objects or those with no clear front face (as illustrated by the skateboard example in Figure 1). This work addresses that limitation effectively by modeling all plausible front faces for such objects.\n\n3. A particularly interesting contribution is the emergence of multiple front-face detection via simple statistical analysis of the predicted orientation distribution from a pretrained VLM trained on absolute orientation tasks. Despite the ill-posed nature of the problem, analyzing predicted pose distributions for each class over the dataset allows the model to detect multiple plausible front faces. This is a significant and impactful result, as there has been no clear method in the field for automatically identifying all valid orientations of arbitrary objects.\n\n4. The paper also employs some clever design choices, such as pretraining VGGT and leveraging the camera pose token—which contains related information—for orientation prediction. This results in a highly capable and versatile model.\n\n**Weaknesses:**\n\n1. In Figure 1, the tasks depicted in the third column are difficult to follow. Specifically, the overlay of the two coordinate systems in the third row is hard to interpret, and it is unclear that different tasks are being illustrated.\n\n2. Lines 23–24 are confusing; it’s not clear what it means for a model to have an intrinsic link to orientation yet not understand orientation. This could be clarified.\n\n3. Line 46—“and sets new records”—sounds overly celebratory. A more neutral phrasing would be more appropriate.", "was_revised": true, "final_justification": "My questions were sufficiently addressed during the discussion, so I will keep my original rating.", "rebuttal": "Thank you for your supportive review and suggestions. Below we respond to the comments in **Weaknesses (W)** and **Questions (Q)**.\n\n---\n\n## W1: Tasks depicted in the third column of Figure 1 are difficult to follow\n\nOur intention was to use semi-transparent arrows to indicate the orientation of the previous frame and annotate the predicted relative rotation. However, due to lack of explanation and insufficient distinction between arrows, this may have caused confusion. In future versions, we will add clarifications and use more distinguishable arrow styles (e.g., lighter colors) to differentiate them.\n\n---\n\n## W2: Confusing Lines 23-24\n\nWe intended to convey that while Orient Anything can predict absolute orientation, it does not fully capture relative rotation, despite the intrinsic link between object orientation and rotation. We will modify and polish these statements to eliminate confusion.\n\n---\n\n## W3: Overly celebratory Line 46\n\nThank you for your suggestion. In future versions, we will use more neutral phrasing, such as \"robust performance\" and \"precise results.\"\n\n---\n\n## Q1: Extend to multiple objects\n\nAs discussed in Orient Anything V1, by equipping with object detection and segmentation models, we can isolate each object in multi-object scenes and perform orientation prediction separately. This simple pipeline yields strong results.\n\n---\n\n## Q2: Application to arbitrary object classes not present in the training set\n\nWe tested on some imaginary objects (e.g., fantasy animals or vehicles) that do not exist in our training set. Our model is able to make reasonable predictions based on common sense, such as object parts or proportions.\n\nAdditionally, SynCLR [1], which also synthesizes text-image paired data from ImageNet21K class tags using LLMs and image generation models, demonstrates strong generalization to arbitrary categories. This may be due to the inherent diversity of image generation models. Our method contains similar category coverage as SynCLR, and thus can be expected to generalize similarly to arbitrary categories.\n\n[1] Tian Y, Fan L, Chen K, et al. Learning vision from models rivals learning vision from data. (CVPR 2024)\n\n---"}, {"review_id": "ugIHkzkLaE", "reviewer": "Reviewer_krXG", "summary": "This paper presets Orient Anything V2 - a model for estimating the absolute 3D orientation (the pose of an object defined by its front face) and relative rotation (3D transform between pose in two images) of objects in images. The main distinctions to Orient Anything V1 are the enabling direct relative pose estimation and handling (rather than ignoring) objects with symmetry. \n\nTo achieve this, the paper makes two significant contributions. First, a data engine is developed to generate a large amount of high quality 3D assets (ImageNet21K class label -> object description from LLM -> image generated by generative model -> 3D mesh generated by Hunyan 3D). These assets are then pseudo-labeled using the V1 model and these predictions are aggregated to determine symmetry and the number of front faces with a final manual consistency check. Given this large set of labeled assets, a transformer-based model is trained to jointly predict an orientation distribution relative to a world coordinate frame, and relative rotation distribution. The proposed approach achievers state-of-the-art absolute orientation estimation, relative rotation estimation, and symmetry recognition.", "questions": "- What is the effect of using synthesized 3D data rather than Objeverse? Is the 3D engine worth the effort?\n- With respect to the sim2real domain gap, could the authors describe scenarios where the proposed approach may have critical failures?\n- Could the authors describe, in as much detail as possible, how the world coordinate frame is chosen and why (is it the center of mass of the object, 3D bounding box center?) and how this is handled at inference time (for an arbitrary 2D image, how can we know were the point is in 3D for the coordinate system that we learned to predict in?)? What is the process to draw the arrows on the in-the-wild images?", "limitations": "The authors adequately address the weaknesses.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**Strenghts**\n\nQuality\n- Extensive ablation studies - the paper ablates data scaling, model initialization, which are the main design components. As expected, rotation estimation benefits more from higher data scale.\n- Extensive evaluation and strong performance - to justify the claim of referring to their model as a foundation model, the authors demonstrate its zero-shot generalization on orientation estimation, relative pose estimation, and symmetry recognition. Successfully unifying these tasks and obtaining strong performance is a significant achievement. Compared with prior work, the model has strong performance even on views with a large angle gap between the frame pairs.\n- Model design - The architecture is simple and effective. Following the design of VGG-T and predicting a distribution over possible poses while handling periodicity for symmetry is elegant.\n\nClarity\n- The paper is well written with high quality figures and easy to follow. Some technical and implementation details are missing that should be included (see weaknesses)\n\nOriginality\n- Data engine - it is exciting to see that a combination of language models and image and 3D generative models can be thoughtfully combined to synthesize a dataset that is actually useful. Looking toward the future, leveraging generative models for data synthesis will be essential, and this work will inform future works attempting to do this.\n\nSignificance\n- The significance is clear -- this work unifies three pose understanding tasks in a single model with good zero-shot performance. There are clear downstream applications in robotics or 3D content generation.\n\n**Weaknesses**\n\nQuality\n- Understanding the effect of the data engine would greatly strengthen the paper. How much would performance reduce if instead of the data engine, Objaverse was used off-the-shelf?\n- Given the significant reliance on synthetic data, this warrants an explicit discussion on the visual domain gap between synthetic data and test data, as well as what can be effective mitigation strategies.\n\nClarity\n- How is the world coordinate system defined and does that affect generalization? This is an important detail that's not discussed in the main text or supplement. Given this omission, it should also be clarified how the origin points of the arrows indicating the various axes are determined for in the wild data visualizations and how these arrows are projected from 2D to 3D.\n\nMinor\nL287 - Fine grain -> Fine grained", "was_revised": true, "final_justification": "I initially had a positive opinion of this work and asked for some clarifications that the authors addressed very well (justifying the data engine empirically, and clarifying how the visualizations in the paper are actually generated). I don't have concerns for this paper and maintain my positive rating.", "rebuttal": "Thank you for your supportive review and suggestions. Below we respond to the comments in **Weaknesses (W)** and **Questions (Q)**.\n\n---\n\n## W1 & Q1: Effect of Data Engine\n\nBelow are comparison results using the same number of object assets from different sources (our data engine vs. Objaverse):\n\n| Assets Type | Assets Number | Objectron | Objectron | Ori_COCO | LINEMOD   | LINEMOD  | YCB-Video | YCB-Video |\n| ----------- | ------------- | --------- | --------- | -------- | --------- | -------- | --------- | --------- |\n|             |               | Med↓      | Acc15↑    | Acc↑     | Med↓      | Acc15↑   | Med↓      | Acc15↑    |\n| Objaverse   | 40K           | 25.05     | 54.3      | **74.8** | 10.70     | 69.8     | 15.49     | 72.5      |\n| Ours        | 40K           | **24.44** | **54.6**  | 74.6     | **10.16** | **74.1** | **7.28**  | **76.2**  |\n\nThe comparison shows that even without considering scalability, our data engine has significant advantages in asset quality and annotation accuracy.\n\n---\n\n## W2 & Q2: Domain gap between synthetic and real data\n\nWe have adopted two strategies to improve domain transfer:\n\n1. We mixed annotated real images (i.e., ImageNet3D) into our training data.\n2. We initialized our models with strong base models pre-trained on large-scale real data, which has been shown to significantly improve synthetic-to-real transfer in works such as Orient Anything v1, Depth Anything v2[1], and Marigold[2].\n\nAdditionally, more domain gap mitigation methods can be explored. For example, we found that using advanced depth-conditioned image generation models can redraw rendered images into photorealistic ones while preserving object structure, which could be a promising direction for addressing domain gaps in the future.\n\n[1] Yang L, Kang B, Huang Z, et al. Depth anything: Unleashing the power of large-scale unlabeled data. (CVPR 2024)\n\n[2] Ke B, Obukhov A, Huang S, et al. Repurposing diffusion-based image generators for monocular depth estimation. (CVPR 2024)\n\n---\n\n## W3 & Q3: World coordinate system\n\nDuring rendering, we align the center of each 3D object's bounding box to the origin, so by definition, our orientation is aligned with the object's 3D box center.\n\nHowever, our model only outputs three Euler angles and does not predict the 3D box center. For visualization, we currently overlay the orientation arrow at the center of the object's 2D bounding box for quick illustration. More precise alignment with the origin would require combining advanced depth prediction or monocular 3D box detection models to estimate the object's 3D box center.\n\n---"}, {"review_id": "cpnIMdE5d5", "reviewer": "Reviewer_tGZ7", "summary": "This paper presents Orient Anything V2, an upgraded foundation model for understanding 3D object orientation and rotation from single or paired images. Compared to its previous version, V2 claims: a new pipeline for generating high-quality synthetic 3D assets, a robust method to identify 0 to N valid front-facing directions per object, a symmetry-aware learning objective that directly models rotational symmetry, and a multi-frame architecture that supports relative rotation estimation. The proposed model achieves strong generalization across multiple real-world benchmarks for orientation estimation, 3D pose estimation, and rotational symmetry recognition.", "questions": "What is the best performance of Orient Anything V2 without using the VGGT pre-trained weights? Is it still comparable to the baseline?", "limitations": "Similar to its predecessor, Orient Anything V2 does not estimate object translation, which is a critical component for many real-world applications, such as robotics and AR/VR.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths:\n- The problem addressed in this paper, orientation estimation of arbitrary objects, is well motivated.\n- The paper is overall clear and easy to follow.\n\nWeaknesses:\n- The paper overlooks several important related works that address similar tasks (pose estimation from a single image and orientation distribution prediction) such as RelPose [1] and NOPE [2].\n- In Table 1 and its accompanying analysis, the authors claim that “Orient Anything V2 significantly improves upon V1, benefiting from diverse synthetic data and robust ensemble annotation“. This suggests that the improvement primarily stems from the dataset, rather than from the method design changes. This is an important point, as the model architecture and training losses appear very similar between V1 and V2. If true, the contribution of this work would lie mostly on the data side.\n- In Table 4, the performance drops significantly when the model is not initialized with VGGT pre-trained weights, even falling below the baseline. This raises concerns about how much of the performance gain can truly be attributed to the proposed method itself.", "was_revised": true, "final_justification": "I’ve read through all the reviews and the rebuttal carefully. Overall, I found the authors well responded to the points raised, and I appreciate the addition of new experiments (e.g., initializing with DINOv2 and showing the effect of the proposed data engine, etc). Therefore, I raised my score to accept.\n\nI recommend the authors to carefully consider the comments and suggestions provided by the reviewers such as missing references/experiments, and address them for the final camera-ready version", "rebuttal": "Thank you for your valuable review and suggestions. Below we respond to the comments in **Weaknesses (W)** and **Questions (Q)**.\n\n---\n\n## W1: Overlooks several important related works\n\nThank you for your reminder. We will add discussions of these works and revise some of our statements in the updated version. While these works achieve a certain degree of open-world capability, their generalization to real-world scenarios remains limited by training scale and model capacity. We provide additional comparisons of median error (Med↓) below:\n(*since NOPE did not release open-source checkpoints, we failed to reproduce its results during rebuttal*)\n\n| Model               | LINEMOD   | YCB-Video | OnePose++ | OnePose   |\n| ------------------- | --------- | --------- | --------- | --------- |\n| **POPE's Sampling** |           |           |           |           |\n| POPE                | 15.73     | 13.94     | **6.27**  | **2.16**  |\n| VGGT                | 16.64     | 10.97     | 17.76     | 14.08     |\n| Relpose-co3dv1      | 16.74     | 13.53     | 14.54     | 11.37     |\n| Relpose-co3dv2      | 14.27     | 9.76      | 12.99     | 9.52      |\n| OriAny. V1          | 22.67     | 16.27     | 13.32     | 22.30     |\n| OriAny. V2          | **9.45**  | **6.53**  | 7.62      | 8.53      |\n| **Random Sampling** |           |           |           |           |\n| POPE                | 98.03     | 41.88     | 88.21     | 45.73     |\n| VGGT                | 83.96     | 20.75     | 52.18     | 40.30     |\n| Relpose-co3dv1      | 100.05    | 43.17     | 81.35     | 76.67     |\n| Relpose-co3dv2      | 95.49     | 31.21     | 83.21     | 47.48     |\n| OriAny. V1          | 86.63     | 43.30     | 26.88     | 75.19     |\n| OriAny. V2          | **32.08** | **13.78** | **12.94** | **16.29** |\n\n---\n\n## W2: Source of improvement\n\nOur improvements are demonstrated in three aspects:\n1. Absolute orientation estimation (Table 1 and Sec 6.1),\n2. Relative rotation estimation (Table 2 and Sec 3.2),\n3. Rotation symmetry recognition (Table 3 and Sec 3.3).\n\nThe dataset mainly contributed to the absolute orientation estimation in Table 1. However, the improvements in relative rotation estimation and rotation symmetry recognition are due to the updated model architecture (supporting multi-frame input) and learning objectives (periodic angular distributions).\n\nTherefore, across all three dimensions, both our new data engine and framework design are the main contributions.\n\n---\n\n## W3 & Q1: Usage of VGGT pre-training\n\nFirstly, regarding the baselines: **Orient Anything v1 is initialized with DINOv2, while POPE uses pre-trained DINOv2 and SAM**. Under a fair comparison setting with DINOv2 initialization, our method shows comprehensive advantages, as shown below:\n\n| Method                              | Objectron | Objectron | Ori_COCO | LINEMOD   | LINEMOD   | YCB-Video | YCB-Video |\n| ----------------------------------- | --------- | --------- | -------- | --------- | --------- | --------- | --------- |\n|                                     | Med↓      | Acc15↑    | Acc↑     | Med↓      | Acc15↑    | Med↓      | Acc15↑    |\n| POPE                                | -         | -         | -        | 15.73     | 48.30     | 13.94     | **54.4**  |\n| OriAny. V1                          | 30.67     | 49.6      | 74.8     | 22.67     | 31.10     | 16.27     | 45.80     |\n| OriAny. V2(DINOv2 initialized only) | **26.70** | **52.6**  | **79.0** | **15.11** | **49.07** | **13.78** | 52.65     |\n\nSecondly, we believe that strong pre-training is the foundation for developing powerful models. For instance, VGGT itself is initialized with DINOv2, Depth Anything v2 heavily relies on DINOv2, and Segment Anything uses MAE and CLIP pre-training. We believe it is normal for performance to drop without pre-training, and it's valuable to demonstrate how far a model can go when built upon existing research.\n\n---\n\n## Limitation 1: Does not estimate object translation\n\nObject translation in images relates to 2D bounding box and depth, which correspond to object detection and depth estimation models, respectively. Given the strong generalization and modularity of these methods, our work focuses solely on object orientation estimation. In practical applications such as robotics and AR/VR, one can modularly combine object detection, depth estimation, and our orientation estimation model to obtain the object's absolute pose, including both 3D translation and rotation.\n\n---"}]

Author rebuttal:

Meta-review: Following the discussion phase, all reviewers recommended acceptance (1 Strong Accept, 3 Accepts), noting that the paper is interesting, well-written, novel, thoroughly evaluated, and presents impressive results. The rebuttal addressed many of the reviewers’ concerns—for example, by clarifying technical details, committing to expand the related work discussion, and adding new experiments on initialization. As a result, the ACs have decided to accept the paper. Please take the reviewers’ feedback into account when preparing the camera-ready version.
Decision: Accept (spotlight)