20 accepted papers
We study the task of language-conditioned pick and place in clutter, where a robot should grasp a target object in open clutter and move it to a specified place. Some approaches learn end-to-end policies with features from vision foundation models, requiring large datasets. Others combine foundation…
Simulation-based data synthesis has emerged as a powerful paradigm for enhancing real-world robotic manipulation. However, existing synthetic datasets remain insufficient for robust bimanual manipulation due to two key challenges: (1) the lack of an autonomous self-correcting mechanism to resolve ex…
Real-world time series inherently exhibit significant non-stationarity, posing substantial challenges for forecasting. To address this issue, this paper proposes a novel prediction framework, TimeStacker, designed to overcome the limitations of existing models in capturing the characteristics of non…
Multi-view depth estimation has achieved impressive performance over various benchmarks. However almost all current multi-view systems rely on given ideal camera poses which are unavailable in many real-world scenarios such as autonomous driving. In this work we propose a new robustness benchmark to…
Image matching is a fundamental computer vision problem. While learning-based methods achieve state-of-the-art performance on existing benchmarks, they generalize poorly to in-the-wild images. Such methods typically need to train separate models for different scene types (e.g., indoor vs. outdoor) a…
"∗ Equal contributionWe introduce GeoWizard, a new generative foundation model designed for estimating geometric attributes, , depth and normals, from single images. While significant research has already been conducted in this area, the progress has been substantially limited by the low diversity a…
Multi-camera setups find widespread use across various applications, such as autonomous driving, as they greatly expand sensing capabilities. Despite the fast development of Neural radiance field (NeRF) techniques and their wide applications in both indoor and outdoor scenes, applying NeRF to multi…
Multi-frame depth estimation generally achieves high accuracy relying on the multi-view geometric consistency. When applied in dynamic scenes, e.g., autonomous driving, this consistency is usually violated in the dynamic areas, leading to corrupted estimations. Many multi-frame methods handle dynami…
Reconstructing accurate 3D scenes from images is a long-standing vision task. Due to the ill-posedness of the single-image reconstruction problem, most well-established methods are built upon multi-view geometry. State-of-the-art (SOTA) monocular metric depth estimation methods can only handle a sin…
Sampling and interpolation of continuous graph signals have been extensively studied, in order to reconstruct or estimate the entire graph signal from the signal values on a subset of vertices. Whereas in a lot of real-world scenarios, only the signs of signals are available. For example, a rating s…
Many methods have been proposed to process stereo matching for rolling shutter image pairs, they treat all pixels from an image pair in an identical way and require additional estimation approaches to estimate motion states of the camera. However, pixels from a rolling shutter image pair naturally h
We propose a learning-based method <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> that solves monocular stereo and can be extended to fuse depth information from multiple target frames. Given two unconstrained images from a monocular camera wit
Many approaches have been proposed to estimate camera poses by directly minimizing photometric error. However, due to the non-convex property of direct alignment, proper initialization is still required for these methods. Many robust norms (e.g. Huber norm) have been proposed to deal with the outlie…
ImageNet-pretrained networks have been widely used in transfer learning for monocular depth estimation. These pretrained networks are trained with classification losses for which only semantic information is exploited while spatial information is ignored. However, both semantic and spatial informati…
In this letter, we propose a novel motion planning framework for quadrotor teach-and-repeat applications. Instead of controlling the drone to precisely follow the teaching path, our method converts an arbitrary jerky human-piloted trajectory to a topologically equivalent one, which is guaranteed to
In this paper, we propose a novel dense surfel mapping system that scales well in different environments with only CPU computation. Using a sparse SLAM system to estimate camera poses, the proposed mapping system can fuse intensity images and depth images into a globally consistent model. The system…
State-of-the-art monocular dense mapping methods usually divide the image sequence into several separate multi-view stereo problems thus have limited utilization of the information in multi-baseline observations and sequential depth estimations. In this paper, two core contributions are proposed to…
This paper presents a probabilistic approach for online dense reconstruction using a single monocular camera moving through the environment. Compared to spatial stereo, depth estimation from motion stereo is challenging due to insufficient parallaxes, visual scale changes, pose errors, etc. We utili…
In this paper, we propose a novel mapping method for robotic navigation. High-quality dense depth maps are estimated and fused into 3D reconstructions in real-time using a single localized moving camera. The quadtree structure of the intensity image is used to reduce the computation burden by estima…
We focus on a replanning scenario for quadrotors where considering time efficiency, non-static initial state and dynamical feasibility is of great significance. We propose a real-time B-spline based kinodynamic (RBK) search algorithm, which transforms a position-only shortest path search (such as A…