No Pose, No Problem: Surprisingly Simple 3D Gaussian Splats from Sparse Unposed Images

We introduce NoPoSplat, a feed-forward model capable of reconstructing 3D scenes parameterized by 3D Gaussians from \textit{unposed} sparse multi-view images. Our model, trained exclusively with photometric loss, achieves real-time 3D Gaussian reconstruction during inference. To eliminate the need for accurate pose input during reconstruction, we anchor one input view's local camera coordinates as the canonical space and train the network to predict Gaussian primitives for all views within this space. This approach obviates the need to transform Gaussian primitives from local coordinates into a global coordinate system, thus avoiding errors associated with per-frame Gaussians and pose estimation. To resolve scale ambiguity, we design and compare various intrinsic embedding methods, ultimately opting to convert camera intrinsics into a token embedding and concatenate it with image tokens as input to the model, enabling accurate scene scale prediction. We utilize the reconstructed 3D Gaussians for novel view synthesis and pose estimation tasks and propose a two-stage coarse-to-fine pipeline for accurate pose estimation. Experimental results demonstrate that our pose-free approach can achieve superior novel view synthesis quality compared to pose-required methods, particularly in scenarios with limited input image overlap. For pose estimation, our method, trained without ground truth depth or explicit matching loss, significantly outperforms the state-of-the-art methods with substantial improvements. This work makes significant advances in pose-free generalizable 3D reconstruction and demonstrates its applicability to real-world scenarios. Code and trained models are available at https://noposplat.github.io/.

Joint Feature Learning and Relation Modeling for Tracking: A One-Stream Framework

The current popular two-stream, two-stage tracking framework extracts the template and the search region features separately and then performs relation modeling, thus the extracted features lack the awareness of the target and have limited target-background discriminability. To tackle the above issue, we propose a novel one-stream tracking (OSTrack) framework that unifies feature learning and relation modeling by bridging the template-search image pairs with bidirectional information flows. In this way, discriminative target-oriented features can be dynamically extracted by mutual guidance. Since no extra heavy relation modeling module is needed and the implementation is highly parallelized, the proposed tracker runs at a fast speed. To further improve the inference efficiency, an in-network candidate early elimination module is proposed based on the strong similarity prior calculated in the one-stream framework. As a unified framework, OSTrack achieves state-of-the-art performance on multiple benchmarks, in particular, it shows impressive results on the one-shot tracking benchmark GOT-10k, i.e., achieving 73.7% AO, improving the existing best result (SwinTrack) by 4.3%. Besides, our method maintains a good performance-speed trade-off and shows faster convergence. The code and models will be available at https://github.com/botaoye/OSTrack.

Geometry-aware data augmentation for monocular 3D object detection

This paper focuses on monocular 3D object detection, one of the essential modules in autonomous driving systems. A key challenge is that the depth recovery problem is ill-posed in monocular data. In this work, we first conduct a thorough analysis to reveal how existing methods fail to robustly estimate depth when different geometry shifts occur. In particular, through a series of image-based and instance-based manipulations for current detectors, we illustrate existing detectors are vulnerable in capturing the consistent relationships between depth and both object apparent sizes and positions. To alleviate this issue and improve the robustness of detectors, we convert the aforementioned manipulations into four corresponding 3D-aware data augmentation techniques. At the image-level, we randomly manipulate the camera system, including its focal length, receptive field and location, to generate new training images with geometric shifts. At the instance level, we crop the foreground objects and randomly paste them to other scenes to generate new training instances. All the proposed augmentation techniques share the virtue that geometry relationships in objects are preserved while their geometry is manipulated. In light of the proposed data augmentation methods, not only the instability of depth recovery is effectively alleviated, but also the final 3D detection performance is significantly improved. This leads to superior improvements on the KITTI and nuScenes monocular 3D detection benchmarks with state-of-the-art results.