Stable and reliable 4D spatial understanding is fundamental for autonomous driving systems. While feedforward reconstruction networks can estimate camera motion and 3D structure in one pass, pose estimation over long videos remains challenged by computational cost, long-context ambiguity, and temporal instability. To address these challenges, we propose Feedforward Visual Odometry (FFVO), a pose-specialized adaptation of joint reconstruction architectures for efficient and temporally stable camera-pose estimation. FFVO uses (i) a compact camera-token representation for computationally efficient temporal aggregation, (ii) a hierarchical local-to-global temporal decoder that mitigates geometric ambiguity by separating short-range motion aggregation from sequence-level integration, and (iii) intermediate trajectory supervision that promotes temporal stability. Extensive evaluation on the Waymo Open Dataset (WOD), KITTI, and a large-scale proprietary benchmark demonstrates that our method performs favorably against existing feedforward approaches, and greatly reduces jitter and drift. These results support FFVO as an effective feedforward camera-pose decoder in long-horizon visual odometry settings.
Figures & tables
Figure 2: Feedforward reconstruction. As shown in (b), the state-of-the-art feedforward model π3 [ 40 ] is able to produce locally accurate geometry. However, it exhibits pose jitter that misaligns geometry across frames, leading to duplicated static structures (e.g., road signs and barriers) when given long-horizon videos as input, as shown in (c). This observation motivates us to develop a better pose regression branch to improve long term temporal consistency. As shown in (d), our reconstruction is more temporally coherent. Camera poses: yellow denotes the current frame, red denotes other frames. We remove dynamic agents and sky for better visualization.
Figure 3: Overview. A frozen π3 [ 40 ] backbone produces image tokens and geometry, while the trainable camera-token and local-to-global pose branches predict long-horizon odometry using relative-pose and intermediate local-trajectory supervision.
Figure 4: Camera tokenization. For each frame t , we partition the frozen image tokens Xt={xt(j)}j=1N into M coarse regions Ri and average-pool xt(j) within each region to initialize camera tokens Ct={ct(i)}i=1M , which serve as compact pose queries for temporal cross-attention.
Figure 5: Local-to-global temporal pose decoder with its overall architecture and key attention modules. (a) Overall architecture with two local-window stages and one global integration stage. (b) Frame self-attention within a single frame. (c) Temporal cross-attention where camera tokens query temporal context.
Sequences from the PDD test set with fast, long trajectories.
Figure 7: Qualitative comparison on KITTI Odometry Seq. 02. The blue and red boxes highlight local trajectory geometry: π3 -Long shows a local self-collision in the blue box, and both π3 -Long and DA3-Streaming produce overly-close/merged curves in the red box, while Ours-Long better preserves the separation and overall shape compared to GT.
Figure 8: Failure modes. Our model shows similar patterns of temporal instability and inaccuracy as other feedforward methods under degraded conditions: (top) high-speed motion exceeding 55 mph, (center) extreme weather, and (bottom) low-light environments.
Methods
Avg.
00
01
02
03
04
05
06
07
08
09
10
Classical
ORB-SLAM2 w/o LC [ 24 ] †
69.727
40.65
502.20
47.82
0.94
1.30
29.95
40.82
16.04
43.09
38.77
5.42
ORB-SLAM2 w/ LC [ 24 ] †
54.816
6.03
508.34
14.76
1.02
1.57
4.04
11.16
2.19
38.85
8.39
6.63
LDSO [ 10 ] †
22.425
9.32
11.68
31.98
2.85
1.22
5.10
13.55
2.96
129.02
21.64
17.36
Learning Based
DROID-VO [ 32 ] †
54.188
98.43
84.20
108.80
2.58
0.93
59.27
64.40
24.20
64.55
71.80
16.91
DROID-SLAM [ 32 ] †
100.278
92.10
344.60
107.61
2.38
1.00
118.50
62.47
21.78
161.60
72.32
118.70
DPVO [ 33 ] †
53.609
113.21
12.69
123.40
2.09
0.68
58.96
54.78
19.26
115.90
75.10
13.63
Table 3: KITTI Odometry eval results . Top-2 highlighting excludes classical methods. † : from [ 6 ] . ‡ : per-sequence values are reported where available.
Figure 9: Trajectory visualization on the KITTI Odometry Benchmark. Predictions and ground truth for sequences 00–10; all methods use 120-frame alignment chunks [ 6 ] .
Method
ATE (m) ↓
RPE t (m) ↓
RPE r (deg) ↓
π3 -finetuned
15.989
8.452
0.596
+ camera tokens (cross-attn., M=14 )
11.776 (+26.35%)
5.509 (+34.82%)
0.379 (+36.41%)
+ auxiliary supervision
11.195 (+29.98%)
5.354 (+36.65%)
0.373 (+37.42%)
+ local-window temporal attention
7.070 (+55.78%)
1.090 (+87.10%)
0.289 (+51.51%)
+ increase camera tokens to M=24
7.060 (+55.84%)
1.070 (+87.34%)
0.281 (+52.85%)
Table 4: Ablation study on PDD. All our proposed components contribute meaningfully to the final performance. Green number indicates relative improvement to π3 -finetuned.
We introduce OpenVO, a novel framework for Open-world Visual Odometry (VO) with temporal awareness under limited input conditions. OpenVO effectively estimates real-world-scale ego-motion from monocular dashcam footage with varying observation rates and uncalibrated cameras, enabling robust trajectory dataset construction from rare driving events recorded in dashcam. Existing VO methods are trained on fixed observation frequency (e.g., 10Hz or 12Hz), completely overlooking temporal dynamics information. Many prior methods also require calibrated cameras with known intrinsic parameters. Consequently, their performance degrades when (1) deployed under unseen observation frequencies or (2) applied to uncalibrated cameras. These significantly limit their generalizability to many downstream tasks, such as extracting trajectories from dashcam footage. To address these challenges, OpenVO (1) explicitly encodes temporal dynamics information within a two-frame pose regression framework and (2) leverages 3D geometric priors derived from foundation models. We validate our method on three major autonomous-driving benchmarks - KITTI, nuScenes, and Argoverse 2 - achieving more than 20 performance improvement over state-of-the-art approaches. Under varying observation rate settings, our method is significantly more robust, achieving 46%-92% lower errors across all metrics. These results demonstrate the versatility of OpenVO for real-world 3D reconstruction and diverse downstream applications.
Deep learning-based visual odometry (VO) has achieved significant progress, yet most existing methods focus on a monocular approach, which suffers from scale ambiguity. Stereo VO provides real metric by its nature, but remains less studied in deep learning VO due to its high computational cost and modeling complexity. Recent advances in stereo matching and optical flow estimation have made dense visual correspondence increasingly accurate and reliable, but their complementary geometric information has not been fully exploited for VO. In this paper, we present SFVO, a correspondence-driven stereo VO framework that directly builds upon pretrained stereo matching and optical flow models. SFVO exploits pretrained stereo matching and optical flow models to estimate stereo and temporal correspondences. Instead of learning pose directly from images, SFVO maps learned correspondences into geometric constraints and predicts which points are trustworthy. To improve the reliability of visual correspondence-based geometric constraints, we introduce decoupled confidence maps for rotation and translation. This design better aligns the characteristics of visual correspondence and 6-DoF transformations. Extensive experiments on outdoor and indoor datasets demonstrate that SFVO achieves robust and accurate pose estimation with strong generalization capability. The code will be released.
Kai Zhang, Guoyang Zhao, Jun Ma
Robotics and Autonomous Systems Thrust, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou 511453, China · Cheng Kar-Shun Robotics Institute, The Hong Kong University of Science and Technology, Hong Kong SAR, China
Monocular visual odometry (VO) is a fundamental computer vision problem with applications in autonomous navigation, augmented reality and more. While deep learning-based methods have recently shown superior accuracy compared to traditional geometric pipelines, particularly in environments where handcrafted features struggle due to poor structure or lighting conditions, most rely on deterministic regression, which lacks the uncertainty awareness required for robust applications. We propose PoseFM, the first framework to reformulate monocular frame-to-frame VO as a generative task using Flow Matching (FM). By leveraging FM, we model camera motion as a distribution rather than a point estimate, learning to transform noise into realistic pose predictions via continuous-time ODEs. This approach provides a principled mechanism for uncertainty estimation and enables robust motion inference under challenging visual conditions. In our evaluations, PoseFM achieves strong performance on TartanAir, KITTI and TUM-RGBD benchmarks, achieving the lowest absolute trajectory error (ATE) on some of the trajectories and overall being competitive with the best frame-to-frame monocular VO methods. Code and model checkpoints will be made available at https://github.com/helsinki-sda-group/posefm.
Dominik Kuczkowski, Laura Ruotsalainen
Department of Computer Science University of Helsinki Finland