Source-linked AI summary
General Dynamic Scene Reconstruction from Multiple View Video
Armin Mustafa, Hansung Kim, Jean-Yves Guillemaut, Adrian Hilton
TL;DR
Dynamic-scene reconstruction from sparse moving cameras has often depended on controlled settings or prior background information. This paper automatically initializes object segmentation and reconstruction, then jointly refines dense shape and segmentation; tests report improved segmentation and reconstruction accuracy over prior methods.
Problem
Many approaches rely on controlled settings or prior scene information, limiting reconstruction of general dynamic scenes from moving cameras.
Source: §2.1 · p. 2Method
The method initializes dynamic-object segmentation and coarse reconstruction from sparse features, then jointly refines dense reconstruction and segmentation across wide-baseline views.
Source: §1 · pp. 1–2Results
Tests on challenging datasets demonstrate improved reconstruction and segmentation quality compared with state-of-the-art methods.
Source: §7 · p. 8Takeaways & Limitations
The approach supports unsupervised reconstruction of more general dynamic scenes without prior knowledge of scene appearance or structure.
Source: §7 · p. 8The method reconstructs nearby objects as one dynamic object, increasing computation, and does not handle textureless or crowded scenes.
Source: §6.3 · p. 8
Abstract
from arXiv · showhide
This paper introduces a general approach to dynamic scene reconstruction from multiple moving cameras without prior knowledge or limiting constraints on the scene structure, appearance, or illumination. Existing techniques for dynamic scene reconstruction from multiple wide-baseline camera views primarily focus on accurate reconstruction in controlled environments, where the cameras are fixed and calibrated and background is known. These approaches are not robust for general dynamic scenes captured with sparse moving cameras. Previous approaches for outdoor dynamic scene reconstruction assume prior knowledge of the static background appearance and structure. The primary contributions of this paper are twofold: an automatic method for initial coarse dynamic scene segmentation and reconstruction without prior knowledge of background appearance or structure; and a general robust approach for joint segmentation refinement and dense reconstruction of dynamic scenes from multiple wide-baseline static or moving cameras. Evaluation is performed on a variety of indoor and outdoor scenes with cluttered backgrounds and multiple dynamic non-rigid objects such as people. Comparison with state-of-the-art approaches demonstrates improved accuracy in both multiple view segmentation and dense reconstruction. The proposed approach also eliminates the requirement for prior knowledge of scene structure and appearance.
1. Introduction
The paper targets dynamic scene reconstruction from sparse, wide-baseline camera views without requiring prior background knowledge. It introduces automatic coarse object segmentation and reconstruction followed by robust joint refinement.
1. Introduction
- Prior work often relied on controlled settings, known backgrounds, static geometry, strong scene priors, or dense camera networks.Outdoor methods used known background images or prior reconstruction of the empty environment.Source: §1 · p. 1
- The approach reconstructs and segments dynamic objects from sparse synchronized videos without prior scene-structure or background-appearance knowledge.It automatically calibrates camera extrinsics, initializes objects from sparse feature matches, then jointly refines segmentation and dense reconstruction.Source: §1 · p. 1
- The approach is demonstrated on the Juggler dataset captured with moving handheld cameras.Source: §1 · p. 1, Figure 1 · p. 1
- The paper contributes automatic coarse initialization and robust spatio-temporal refinement integrating error-tolerant photo-consistency and edge information.Source: §1 · p. 2
2. Related work
Existing dynamic-scene methods often depend on static or controlled settings and prior information, limiting their use for cluttered scenes captured by moving cameras. This work instead initializes objects automatically and jointly refines segmentation and reconstruction.
2.1. Dynamic scene reconstruction
- Prior dynamic-scene reconstruction often required static backgrounds, closely spaced cameras, prior silhouettes, known backgrounds, or reconstructed background geometry.Cluttered scenes captured with moving cameras remained challenging for many approaches.Source: §2.1 · p. 2
2.2. Joint segmentation and reconstruction
- Appearance-similarity segmentation methods assume static backgrounds and distinct foreground colors, while other joint methods depend on training data, visual-hull initialization, or background plates.These assumptions constrain applicability to general non-rigid scenes.Source: §2.2 · p. 2
- The proposed approach automatically obtains a coarse dynamic-object reconstruction and refines it using geometry and appearance cues, including for moving-camera scenes with unknown background and structure.Source: §2.2 · p. 2
3. Overview
The framework turns synchronized wide-baseline videos into a 4D mesh sequence for each dynamic object. It combines automatic calibration and sparse initialization with per-view dense refinement, fusion, and texture mapping.
3. Overview
- The pipeline captures synchronized videos, calibrates cameras, and reconstructs a sparse point cloud from wide-baseline feature matches.Static-camera extrinsics use fundamental-matrix estimation and bundle adjustment; moving cameras are calibrated automatically.Source: §3 · p. 3
- Sparse-point clustering and optical-flow labeling initialize each dynamic object's segmentation and coarse reconstruction without prior scene appearance or structure.Source: §3 · p. 3
- Per-camera depth maps are jointly refined and fused into one model per dynamic object, then projectively textured for free-viewpoint rendering across the sequence.The proposed approach is reported to outperform previous approaches and does not require prior scene-structure knowledge.Source: §3 · p. 3
4. Initial dynamic object reconstruction
Initial reconstruction isolates dynamic objects from sparse scene information to provide coarse segmentation and shape estimates for later refinement. Clustering and optical flow supply labels, while depth tolerance accounts for uncertainty in the coarse estimates.
4. Initial dynamic object reconstruction
- Sparse point-cloud clusters are combined with optical-flow labels to initialize complete coarse segmentations and reconstructions, retaining object labels across the sequence.This initialization is intended to tolerate clutter, partial motion, and correspondence failure.Source: §4 · p. 3
4.1. Sparse point cloud clustering
- The method clusters the sparse 3D reconstruction using an octree grid and nearest-neighbor information, then uses optical flow to label dynamic clusters across time.For each cluster, it selects a camera view to maximize visibility; if an object does not move between frames, it retains the previous reconstruction.Source: §4.1 · p. 3, §4.2 · p. 4
4.2. Coarse scene reconstruction
- Back-projected sparse points are triangulated to estimate initial disparity within the object region, which is extrapolated by 5% of the average boundary-to-centroid distance.Source: §4.2 · p. 4
- Depth uncertainty is handled with volumes along camera rays, using thresholds of 1% of capture volume for extrapolated pixels and half that for the sparse-point region.This coarse volume avoids dependence on background plates or visual-hull estimates; the same parameters are used across datasets.Source: §4.2 · p. 4
5. Joint segmentation and reconstruction
The section refines coarse dynamic-object depth and segmentation by minimizing an energy that combines photo-consistency, depth smoothness, and contrast cues. It uses region-dependent depth labels and graph-cut optimization to produce view-dependent depth maps for later fusion.
5.1. Problem statement
- Depth labels are sampled along each camera ray around the coarse estimate, with separate label sets for regions RI and RO and fewer labels in RI.The unknown label U handles occlusions and segmentation refinement.Source: §5.1 · p. 4
5.2. Proposed approach
- The energy combines photo-consistency, smoothness, and contrast terms to refine reconstruction and segmentation while estimating a view-dependent depth map for each object and camera.Pixels can be assigned the unknown label U to refine segmentation.Source: §5.2 · p. 4
5.2.1 Matching term
- The matching term uses NCC-based photo-consistency over a pixel’s 4-connected neighborhood and the k most photo-consistent camera pairs.The unknown label receives a fixed cost; the NCC cost uses a 15 × 15 window and noise variance 0.3.Source: §5.2.1 · p. 5
5.2.2 Contrast term
- The contrast term uses bilateral filtering to preserve image edges, and removes regions with low photo-consistency and weak edges to help estimate object boundaries.The formulation modifies a segmentation-prior and contrast-likelihood interpretation to preserve edges.Source: §5.2.2 · p. 5
5.2.3 Smoothness term
- The smoothness term penalizes neighboring depth-label differences, reducing surface noise and peaks when photo-consistency is insufficient.The penalty is capped at dmax, set to 50 times the depth-sampling step.Source: §5.2.3 · p. 5
5.3. Optimization of Reconstruction and Segmentation
- Graph-cut α-expansion obtains a local optimum, typically converging in four or five iterations, and the view-dependent depths are merged by Poisson surface reconstruction.Label iterations use DI for pixels in RI and DO for pixels in RO.Source: §5.3 · p. 5
6. Results and Evaluation
Evaluation spans indoor and outdoor datasets with simple or cluttered backgrounds, including sequences captured by handheld moving cameras. The proposed approach is compared with Guillemaut for segmentation and with Guillemaut and Furukawa for reconstruction.
6. Results and Evaluation
- Evaluation uses indoor and outdoor datasets with simple or cluttered backgrounds and handheld-camera sequences, comparing segmentation with Guillemaut and reconstruction with Guillemaut and Furukawa.Comparisons use the same calibration and coarse reconstruction initialization.Source: §6 · p. 5, §6.2 · p. 7
6.1.1 Qualitative results
- For the segmentation examples, the proposed approach is more accurate than Guillemaut despite erroneous coarse initialization, using photo-consistency and foreground-edge affinity to refine boundaries.Remaining artifacts relative to ground truth occur around shadows and occlusions.Source: Figure 5 · p. 6, §6.1.1 · p. 7
6.2.1 Qualitative results
- The proposed approach has more consistent depth maps than Guillemaut in the Figure 5 examples.Source: Figure 5 · p. 6, §6.2.1 · p. 7
- The proposed meshes are visibly more accurate than the compared methods, especially outdoors; Furukawa yields few points on only a small part of the Magician object.Figure 6 compares meshes across datasets, while Figure 7 shows the Magician comparison.Source: Figure 6 · p. 7, Table 2 · p. 8
6.1.2 Quantitative evaluation
- Across datasets, the proposed approach has higher HitRatio and OverlapRatio and lower BkgRatio than Guillemaut, with generally larger gains on complex scenes.The ratios are averaged over each sequence; higher hit and overlap ratios and lower background ratio indicate better segmentation.Source: §6.1.2 · p. 7, Table 2 · p. 8
6.2.2 Quantitative evaluation
- Reconstruction accuracy is assessed qualitatively because the datasets lack 3D ground-truth models; the proposed method is about 25% faster than Guillemaut but slightly slower than Furukawa.Table 3 reports run-time per frame.Source: §6.2.2 · p. 8, Table 3 · p. 8
6.3. Limitations and Future work
- The approach reconstructs and segments multiple close objects as one dynamic object, increasing computational time; textureless and crowded scenes challenge its sparse-point and clustering initialization.The paper identifies these cases for future work.Source: §6.3 · p. 8
7. Conclusion
The paper concludes that its approach segments and reconstructs dynamic objects without prior knowledge of background appearance or structure. It reports improved reconstruction and segmentation quality over state-of-the-art methods on challenging datasets.
7. Conclusion
- The approach automatically initializes multiple dynamic objects, then jointly refines their segmentation and reconstruction using view-dependent graph cuts.The refinement combines photo-consistency and contrast cues from wide-baseline images.Source: §7 · p. 8
- The method requires no prior knowledge of background appearance or structure for dynamic-scene segmentation and reconstruction.Source: §7 · p. 8
- Tests on challenging datasets show improved reconstruction and segmentation quality compared with state-of-the-art methods.Source: §7 · p. 8