While 3DGS has emerged as a high-fidelity scene representation, encoding rich, general-purpose features directly from its primitives remains under-explored. We address this gap by introducing Chorus, a multi-teacher pretraining framework that learns a holistic feed-forward 3D Gaussian Splatting (3DGS) scene encoder by distilling complementary signals from 2D foundation models. Chorus employs a shared 3D encoder and teacher-specific projectors to learn from language-aligned, generalist, and object-aware teachers, encouraging a shared embedding space that captures signals from high-level semantics to fine-grained structure.We evaluate Chorus on a wide range of tasks: open-vocabulary semantic and instance segmentation, linear and decoder probing, as well as data-efficient supervision. Besides 3DGS, we also test Chorus on several benchmarks that only support point clouds by pretraining a variant using only Gaussians’ centers, colors, estimated normals as inputs. Interestingly, this encoder shows strong transfer and outperforms the point clouds baseline while using $39.9\times$ fewer training scenes. Finally, we propose a render-and-distill adaptation that facilitates out-of-domain finetuning. Our code and model will be released upon publication.

One of the most exciting applications of vision models involve pixel-level reasoning.Despite the abundance of vision foundation models, we still lack representations that effectively embed spatio-temporal properties of visual scenes at the pixel level.Existing frameworks either train on image-based pretext tasks, which do not account for dynamic elements, or on video sequences for action-level reasoning, which does not scale to dense pixel-level prediction.We present a framework that learns pixel-accurate feature descriptors from videos, LILA.The core element of our training framework is linear in-context learning.LILA leverages spatio-temporal cue maps -- depth and motion -- estimated with off-the-shelf networks.Despite the noisy nature of those cues, LILA trains effectively on uncurated video datasets, embedding semantic and geometric properties in a temporally consistent manner.We demonstrate compelling empirical benefits of the learned representation across a diverse suite of vision tasks: video object segmentation, surface normal estimation and semantic segmentation.

Keypoint-based matching is a fundamental component of modern 3D vision systems, such as Structure-from-Motion (SfM) and SLAM. Most existing learning-based methods are trained on image pairs, a paradigm that fails to explicitly optimize for the long-term trackability of keypoints across sequences under challenging viewpoint and illumination changes. In this paper, we reframe keypoint detection as a sequential decision-making problem. We introduce TraqPoint, a novel, end-to-end Reinforcement Learning (RL) framework designed to optimize the Track-quality (Traq) of keypoints directly on image sequences. Our core innovation is a track-aware reward mechanism that jointly encourages the consistency and distinctiveness of keypoints across multiple views, guided by a policy gradient method. Extensive evaluations on sparse matching benchmarks, including relative pose estimation and 3D reconstruction, demonstrate that TraqPoint significantly outperforms some state-of-the-art keypoint detection and description methods.

We revisit the problem of estimating the fundamental matrix of a pair of perspective cameras, a cornerstone of geometric computer vision.As is well-known, linear solvers require at least 8 point correspondences, whereas nonlinear minimal solvers require just 7 in the uncalibrated case or 5 in the calibrated case.In this paper, we consider a special case of the 7-point problem where 5 of the points are configured to lie on two lines, which has previously been shown to have a unique solution.As a theoretical contribution, we offer an analysis of how this uniqueness manifests in the standard 7-point algorithm. On a practical level, we provide the first practical linear solver for the minimal problem associated to this special configuration.Additionally, we evaluate a heuristic 5-point fundamental matrix solver based on the construction of virtual midpoints.When combined with early non-minimal fitting, the runtime and accuracy of our solver is competitive with the state-of-the-art (SoTA) on multiple benchmarks.

Semantic Scene Completion (SSC) is crucial for 3D perception in mobile robotics, as it enables holistic scene understanding by jointly estimating dense volumetric occupancy and per-voxel semantics. Although SSC has been widely studied in terrestrial domains such as autonomous driving, aerial scenarios like autonomous flying remain largely unexplored, thereby limiting progress on downstream applications. Furthermore, LiDAR sensors represent the primary modality for SSC data generation, which poses challenges for most uncrewed aerial vehicles (UAVs) due to flight regulations, mass and energy constraints, and the sparsity of LiDAR-based point clouds from elevated viewpoints. To address these limitations, we introduce OccuFly, the first real-world, camera-based aerial SSC benchmark, captured at altitudes of 50m, 40m, and 30m during spring, summer, fall, and winter. OccuFly covers urban, industrial, and rural scenarios, provides 22 semantic classes, and the data format adheres to established conventions to facilitate seamless integration with existing research. Crucially, we propose a LiDAR-free data generation framework that is based on camera modality, which is ubiquitous on modern UAVs. By utilizing traditional 3D reconstruction, our framework automates label transfer by projecting annotated 2D masks into the reconstructed 3D point cloud, thereby minimizing manual 3D annotation effort. Finally, we benchmark several state-of-the-art SSC methods on OccuFly using standard metrics, and highlight challenges specific to aerial viewpoints, yielding a comprehensive aerial vision benchmark that fosters holistic aerial 3D scene understanding.

We present VGGT-Ω, a feed-forward model for 3D reconstruction that substantially advances the state of the art in accuracy, efficiency, and capability for both static and dynamic scenes. Prior models such as VGGT have shown that feed-forward 3D reconstruction can already be competitive with traditional optimization-based methods. Here, we further demonstrate that the accuracy and robustness of these models scale predictably with model capacity and data size. To enable training 3D reconstruction models at an unprecedented scale, we introduce a high-quality data annotation pipeline that handles dynamic scenes, a self-supervised learning protocol, and architectural changes that greatly reduce memory requirements. We significantly simplify VGGT’s architecture by replacing multiple dense prediction heads with loss-driven multitask learning, removing unstable DPT blocks, and introducing more efficient global attention via scene tokens. These changes allow us to efficiently train VGGT-Ω with 20$\times$ more supervised data and 100$\times$ more unsupervised data than prior work, while requiring only 30% of VGGT’s memory and running 1.6$\times$ faster at inference. As a result, VGGT-Ω establishes a new state of the art for 3D reconstruction on both static and dynamic scenes across a wide range of benchmarks, e.g., improving the camera estimation accuracy by 77% on the Sintel dataset. Models and code will be publicly released.