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版本更新 · v3arXiv:2505.20858

Track-Free Dense Structure from Motion

Pak Hong Chui, Hector Andrade-Loarca, Daniel Cremers

原文摘要

Classical Bundle Adjustment (BA) relies on precise metric initialization and known camera intrinsics, and traditional Structure-from-Motion (SfM) pipelines struggle to exploit modern dense matchers because rigid track-building heuristics break down under their inherent noise. We present Track-Free Dense Structure from Motion (TFD-SfM), which jointly optimizes camera extrinsics, focal lengths, and geometry from a strict cold start. Our central idea is to relax the rigidities of classical SfM into soft, probabilistic decisions under Negative Log-Likelihood (NLL) objectives. Instead of forcing matched pixels onto a single shared 3D point, we model each match as a 3D Gaussian whose variance is optimized jointly with the geometry. Unreliable matches take on large variance and contribute little to the loss. This smooths the optimization landscape, so the solver converges even from identity cameras and uniform depths. A kinematic relative-pose parameterization stabilizes the cold start, and a dual-hypothesis mechanism resolves the mirror-symmetry ambiguity on object-centric scenes. Across DTU, Mip-NeRF~360, ETH3D, and IMC2021, TFD-SfM is competitive with both classical and learning-based SfM baselines and leads on ETH3D, recovering accurate camera poses, intrinsics, and dense geometry from unordered, uncalibrated image collections. Project~page: https://cphjsn.github.io/tfd-sfm/

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.12624

Towards Reconstructing and Recognizing Objects behind Fragmented Occlusion using Video

Roman Pflugfelder, Michael Lindenbaum, Daniel Cremers

原文摘要

Perceiving objects through fragmented occlusions, such as foliage, is a natural capability of the human visual system but remains a fundamental challenge for computer vision. This work addresses the problem of object reconstruction, or de-occlusion, in the presence of high-density, fragmented occlusions. We propose a reconstruction framework that operates on a sequence of grayscale images of a moving object, acquired by a single camera, either stationary or in motion. The algorithm estimates the object's relative positions across frames by jointly registering the images and optimizing the alignment parameters to maximize the contrast of their average, referred to as the integrated image. This integrated image serves as the reconstructed representation of the object. Experimental evaluations on both synthetic and real video sequences demonstrate the feasibility of accurate reconstruction under occlusion densities exceeding 90%. The proposed approach is competitive with synthetic aperture techniques, which are limited to static objects and require complex camera setup with careful calibration. Furthermore, we show that the integrated image enables reliable object classification under severe occlusion when used as input to standard, off-the-shelf classifiers.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07705

FlyingDrones: A Dataset and Benchmark for Optical Flow Estimation from UAV motion

Laurenz Ruzicka, Roman Pflugfelder, Daniel Cremers

原文摘要

Optical flow is an important cue for drone analysis in aerial surveillance, airspace monitoring, and long-range target tracking, where motion remains informative when appearance is weak. Estimating the motion of small Unmanned Aerial Vehicles, however, is difficult because distant targets occupy few pixels, have low contrast, and move against complex dynamic backgrounds. Standard optical-flow benchmarks emphasize large, object-centric motions and therefore do not characterize distant aerial targets well. We introduce FlyingDrones, a generated benchmark with dense ground-truth optical flow and segmentation masks derived from physically based rendering, and define resolution-normalized metrics (EPE Norm) that normalize flow by image dimensions. We evaluate 16 state-of-the-art model families on a motion- and size-stratified subset of 999 flow pairs at three resolutions. Every family recovers drone motion better than the zero-flow reference, but the gap between families widens with resolution: at 1920x1080, global-matching, recurrent, transformer, and short-window temporal models separate clearly from pyramid, classical, self-supervised, lightweight, and diffusion models. Within-family checkpoint spread is comparable to or larger than between-family spread, so the training curriculum is as important as the architecture. Two complementary studies test the benchmark beyond synthetic accuracy: a ground-truth-free motion-alignment evaluation on 21 cameras of real UAV footage shows that the synthetic ranking carries a moderate, significant signal about real-world performance (Spearman \r{ho} = 0.63 over 41 checkpoints) while the exact top ranks differ, and a fine-tuning study shows that 800 FlyingDrones pairs reduce held-out drone-region error by up to 37% and improve drone localization on the real footage for all four architectures tested.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2610.09411

Shared Geometry As A Rosetta Stone: Cross-Modal Alignment Without Paired Data

Dominik Schnaus, Thomas Dagès, Daniel Cremers, Xi Wang, Phillip Isola

原文摘要

Multimodal representations enable zero-shot classification and retrieval, but aligning independently trained models usually requires large amounts of paired data. Yet, the Platonic Representation Hypothesis suggests that models trained on different modalities may converge spontaneously toward a shared representation geometry. But then, do we even need paired examples for cross-modal alignment? Remarkably, we show that paired examples are unnecessary for coarse cross-modal alignment. Our simple Wasserstein Procrustes method with a coarse geometric initialization (GeoWarP) aligns two disjoint embedding sets by estimating a single orthogonal map without seeing any pairs. Across datasets, modalities, and unimodal models, we show that we can consistently align independently trained representations without pairs, and standard geometric alignment metrics accurately predict when this is possible. Nevertheless, we can naturally benefit from paired examples. In the very few-pair regime, our method substantially outperforms existing ones, while staying competitive with pair-based methods with more added examples. Finally, we demonstrate that the resulting alignments can enable text-to-image generation without paired examples. These results show that independently trained models often share enough geometry to establish cross-modal correspondence with little or no paired data.

cs.LG · cs.AI · cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03228

AnchorComplete: Recovering Complete Scene Meshes from Partial Point Clouds

Weirong Chen, Ismail El-Yamany, Ganlin Zhang, Chuanxia Zheng, Andrea Vedaldi, Daniel Cremers

原文摘要

Most image-based 3D reconstructors primarily recover visible surfaces, producing partial point clouds with large holes and leaving unobserved geometry unresolved. To address this limitation, we introduce AnchorComplete, a framework that reconstructs complete scene meshes from partial point clouds while preserving observed geometry and supporting scenes of varying scales. Building on TRELLIS.2, our method combines partial-point conditioning, which integrates global scene tokens with local spatial evidence to anchor generation to the input, and metric-scale coarse-to-fine generation, which progressively refines scene geometry within fixed-size windows while maintaining global structural consistency. Experiments on Replica and SCRREAM demonstrate that AnchorComplete consistently outperforms strong baselines in complete-scene reconstruction, particularly under sparse-view settings.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01937

Spade-SLAM: Sparse Correspondence Anchors for Dense Self-Supervised Visual SLAM

Julian Philipp Schmitt, Weirong Chen, Ganlin Zhang, Felix Wimbauer, Daniel Cremers

原文摘要

We introduce Spade-SLAM, a self-supervised dense visual SLAM framework that substantially narrows the long-standing performance gap to supervised methods in a zero-shot setting. By demonstrating that high-fidelity tracking can be achieved without explicit ground-truth pose and depth labels, our approach offers a highly effective and label-efficient alternative to fully supervised pretraining. Our core contribution is a complementary sparse-to-dense training regime: While a warping-based photometric loss provides a dense training signal, we stabilize the optimization and suppress drift in ambiguous regions using a sparse correspondence loss derived from image feature matches. Extensive experiments demonstrate that Spade-SLAM significantly improves robustness and accuracy compared to existing self-supervised baselines, closely rivaling SLAM systems trained with explicit pose and depth supervision. The code will be made publicly available at https://github.com/JulianPSchmitt/Spade-SLAM.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.39167

EgoContact: Evaluating Physical Intelligence with Egocentric Video

Adam Deryło, Alexey Gavryushin, Daniel Cremers, Marc Pollefeys, Xi Wang

原文摘要

Egocentric video has emerged as one of the most scalable sources of data for learning generalizable manipulation priors. Yet, despite its growing use in pretraining robotic policies, it has been underutilized as a validation tool for physical intelligence, a key component of which is understanding where to make contact with an object to achieve an intended interaction. We present EgoContact, a large-scale egocentric dataset for evaluating the generalizability and precision of learned manipulation priors through future contact-point prediction. EgoContact contains 22.5K manually verified interaction events, each with a pre-contact observation, semantic intent, and pixel-precise contact locations. We find that current methods often struggle to predict precise interaction contacts and sometimes fail to localize the target object in diverse, in-the-wild scenes, even when allowed multiple predictions. Furthermore, we demonstrate the usefulness of EgoContact's annotated interactions for robotic manipulation: simply using EgoContact as the memory bank of an existing affordance-transfer policy improves grasp success from 14.2% to 31.2% in simulation and from 16% to 58% on a real robot.

cs.RO · cs.CV

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arXiv ↗PDF ↗