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4D Reconstruction with Events & RGB
Dulanga Weerakoon, Darshana Rathnayake, Van Minh Do, Hemanth Sabbella, Kaichen Zhou, Fangneng Zhan, Alok Prakash, Sanjay E. Sarma, Archan Misra, Paul Pu Liang
TL;DR
RGB-only 4D reconstruction can suffer from blur and temporal aliasing during fast motion, and event integration without a full redesign remains less explored. This preliminary study adds an event-voxel branch to MonST3R and reports lower reconstruction error across considered benchmarks, with the largest gains under high camera and object motion.
Problem
Whether event streams can be incorporated into existing RGB-based 4D reconstruction pipelines without full architectural redesign remains less explored.
Source: §1 · p. 2Method
The study adds an event-voxel input branch to MonST3R and concatenates it with RGB features before the otherwise unchanged pipeline.
Source: §1 · pp. 2–3Results
The minimal event-camera branch reduces both depth and pose estimation error across the benchmarks considered, with the largest gains under high camera and object motion.
Source: §5 · p. 7Takeaways & Limitations
Across camera pose and video depth benchmarks, combining RGB frames and event voxels achieves lower reconstruction error than either modality alone, consistent with complementary spatial and motion cues.
Source: §6 · p. 8The evaluated event streams are synthetically generated using V2E, so validating gains on real event-camera captures remains a next step before drawing real-world deployment conclusions.
Source: §5 · p. 8
Abstract
from arXiv · showhide
Reconstructing dynamic 3D scenes over time (4D reconstruction) from a single moving camera is central to robotics and AR/VR applications, yet state-of the-art feed-forward methods rely solely on RGB frames and therefore inherit the failure modes of conventional cameras, including motion blur and temporal aliasing under fast or non-uniform camera and object motion. Event cameras report per-pixel brightness changes asynchronously at microsecond resolution, offering a complementary signal that is largely immune to these artifacts, but whether this modality can be incorporated into existing RGB based 4D reconstruction pipelines without a full architectural redesign remains an open question. In this preliminary study, we investigate a minimal extension: we augment Monst3R, an existing RGB-only 4D reconstruction model, with an additional event-camera input branch, leaving the rest of the architecture unchanged. Across 3 benchmark datasets, this simple RGB-event fusion reduces Abs Rel by 3.4% on average relative to the RGB-only Monst3R baseline, and a sensitivity analysis shows that the improvement grows with the magnitude of camera and object motion, with the largest gains observed under high object motion conditions where RGB-only reconstruction degrades most. These early results suggest that even a lightweight, non-invasive fusion of event data can meaningfully improve 4D reconstruction robustness, motivating a more thorough architectural investigation in future work.
1 Introduction
RGB-only feed-forward 4D reconstruction remains vulnerable to blur and temporal aliasing during fast motion, while integrating events into existing pipelines without redesign is less explored. This study adds an event-voxel branch to MonST3R and reports improved reconstruction accuracy, especially under high object motion.
1 Introduction
- RGB-only feed-forward models inherit motion blur and temporal aliasing under fast or non-uniform camera and object motion.MonST3R recovers per-frame depth and camera pose directly from RGB video without per-scene optimization.Source: §1 · p. 2
- Whether event streams can enter existing RGB-based 4D reconstruction pipelines without full architectural redesign remains less explored.Events report per-pixel brightness changes asynchronously at microsecond resolution and capture motion information largely unaffected by blur or frame-rate limitations.Source: §1 · p. 2
- The proposed extension adds an event-voxel branch to MonST3R and concatenates its features with RGB features before the unchanged standard pipeline.The paper hypothesizes that RGB supplies spatial depth information while events supply motion cues, particularly under fast object and camera motion.Source: §1 · pp. 2–3
- Fusing event voxels and RGB achieves higher reconstruction accuracy than either modality alone, with the benefit particularly pronounced under high object motion.The study evaluates benchmark datasets for camera pose estimation and video depth estimation.Source: §1 · p. 2
- The work is preliminary and argues that substantially higher reconstruction accuracy requires more principled fusion for synchronous RGB and adaptive asynchronous event modalities.Source: §1 · p. 3
2 Related Work
Prior feed-forward 4D reconstruction methods use RGB video, while event-camera fusion has been explored more in related tasks than in 4D scene reconstruction. Recent event-augmented work targets illumination robustness, leaving motion-focused evaluation comparatively less direct.
Feedforward 4D reconstruction
- Feed-forward 4D reconstruction progressed from pairwise pointmaps and post-hoc alignment to unified pipelines and larger transformer models.DUSt3R and MonST3R predict pointmaps followed by alignment; CUT3R and Fast3R predict aligned pointmaps directly, while VGGT and PAGE4D scale training data and model capacity.Source: §2 · p. 3
Event cameras and RGB-event fusion
- Prior RGB-event fusion addresses deblurring, frame interpolation or extrapolation, and visual odometry or SLAM, while event-based 4D scene reconstruction remains comparatively underexplored.These related tasks use events to recover information lost in blurred or dropped RGB frames.Source: §2 · p. 3
Event-augmented 4D reconstruction
- EAG3R targets low-light robustness, leaving less direct evidence on event fusion for fast, non-rigid dynamics independent of illumination.Its central experiments vary illumination while holding the motion regime relatively constant; the present work evaluates varying camera and object motion.Source: §2 · pp. 3–4
3 Methodology
The method extends MonST3R to process RGB images with event voxel grids, using a lightweight input adapter while retaining the original prediction architecture. It generates event data from RGB sequences and trains on three datasets augmented with synthetic events.
3 Methodology
- Each event stream records pixel coordinates, timestamps, and polarity, with polarity indicating a brightness increase or decrease; events are accumulated into temporal-bin voxel grids.The voxel grids are paired with RGB images as network inputs.Source: §3 · p. 4
3.1 Model Architecture
- A single convolution projects concatenated 8-channel RGB and event inputs to the pretrained encoder’s 3-channel input, while the shared Siamese ViT encoder and original MonST3R decoder and prediction heads remain unchanged.Each view combines a 3-channel RGB image with a 5-channel event voxel grid.Source: §3.1 · p. 4, §3 · p. 4
3.2 Dataset Preparation
- V2E generates event streams between consecutive RGB frames by interpolating frames to increase temporal resolution, modeling brightness changes, and accumulating events into voxel grids.The datasets used provide RGB sequences only.Source: §3.2 · p. 5
3.3 Training Details
- Training uses PointOdyssey, TartanAir, and Spring augmented with synthetic event data, with remaining configurations matching the original MonST3R implementation.Source: §3.3 · p. 5
4 Results
RGB and event-voxel fusion achieves the lowest reported errors in camera-pose and video-depth comparisons. Its advantage persists across camera-motion levels and is larger under high object motion.
4 Results
- RGB + event voxels achieves the lowest camera-pose error on all three Sintel metrics and the lowest video-depth error on both metrics across Sintel, Bonn, and KITTI.The comparisons use four input variants for camera pose and report video-depth error on three datasets.Source: Camera Pose Estimation · p. 5, Table 1 · p. 6, Table 2 · p. 6
- RGB and event-voxel fusion achieves lower Log RMSE across all three camera-motion levels in the PointOdyssey test subset.The predicted trajectories are easier to estimate under smooth, uniform motion; non-uniform motion accumulates higher error over time.Source: Event+RGB against Camera Motion · p. 6, Figure 2 · p. 7
- Under high object motion, fusion achieves a 0.327 lower Log RMSE; it also achieves lower Log RMSE under low object motion.The PointOdyssey test set is divided into two object-motion levels using ground-truth depth variation.Source: Event+RGB against Object Motion · pp. 6–7
- Visualizations of RGB + event-voxel reconstructions show scene geometry remaining intact across the displayed examples.Source: Visualization of RGB+Event Voxels… · p. 7
5 Discussion & Future Work
A minimal event branch reduces depth and pose estimation error, with the largest gains under high camera and object motion. The discussion identifies limits in event encoding and fusion, the MonST3R base model, and synthetic event data.
5 Discussion & Future Work
- The minimal event-camera branch reduces depth and pose estimation error across the considered benchmarks, with the largest gains under high camera and object motion.The design concatenates event voxels with RGB features and makes no other architectural changes.Source: §5 · p. 7
- The event representation uses dense voxel grids processed by the RGB backbone, while concatenation treats RGB and event features uniformly regardless of local motion.The discussion suggests event-specific encoders and fusion mechanisms such as cross-attention or gating as possible alternatives.Source: §5 · p. 7
- RGB + Ev. Vox. achieves lower Log RMSE than RGB alone across motion levels, with the gap widening under high object motion.The sensitivity analysis uses a PointOdyssey test subset; camera motion is grouped into three levels and object motion into two.Source: Figure 3 · p. 8
- MonST3R is no longer the most accurate feed-forward 4D reconstruction model, so whether event fusion generalizes to newer base models remains untested.The authors identify applying the extension to VGGT or PAGE4D as a future direction.Source: §5 · p. 8
- The evaluated datasets lack native event streams, so the study uses V2E-simulated events and calls for validation on real event-camera captures.The authors identify real-event validation as a next step before drawing conclusions about real-world deployment.Source: §5 · p. 8
6 Conclusion
This preliminary study combines RGB frames and event voxels through a minimalist design that concatenates them before MonST3R. Across multiple camera-pose and video-depth datasets, the fusion achieves lower reconstruction error than either modality alone, while more principled multimodal fusion remains future work.
6 Conclusion
- The minimalist design concatenates event voxels with RGB frames before passing them through retrained MonST3R.Across multiple camera-pose and video-depth datasets, combining the modalities achieves lower reconstruction error than either alone.Source: §6 · p. 8
- The qualitative reconstructions show scene geometry remaining intact with the RGB + Ev. Vox. variant and MonST3R as the base model.The figure presents reconstructions from pairs of RGB frames and corresponding event voxels.Source: Figure 4 · p. 9
- More principled multimodal fusion approaches combining events and RGB are left for future work.The study describes itself as preliminary.Source: §6 · p. 9