Fast Sparse View Guided NeRF Update for Object Reconfigurations

Neural Radiance Field (NeRF), as an implicit 3D scene representation, lacks inherent ability to accommodate changes made to the initial static scene. If objects are reconfigured, it is difficult to update the NeRF to reflect the new state of the scene without time-consuming data re-capturing and NeRF re-training. To address this limitation, we develop the first update method for NeRFs to physical changes. Our method takes only sparse new images (e.g. 4) of the altered scene as extra inputs and update the pre-trained NeRF in around 1 to 2 minutes. Particularly, we develop a pipeline to identify scene changes and update the NeRF accordingly. Our core idea is the use of a second helper NeRF to learn the local geometry and appearance changes, which sidesteps the optimization difficulties in direct NeRF fine-tuning. The interpolation power of the helper NeRF is the key to accurately reconstruct the un-occluded objects regions under sparse view supervision. Our method imposes no constraints on NeRF pre-training, and requires no extra user input or explicit semantic priors. It is an order of magnitude faster than re-training NeRF from scratch while maintaining on-par and even superior performance.

A Quantitative Evaluation of Score Distillation Sampling Based Text-to-3D

The development of generative models that create 3D content from a text prompt has made considerable strides thanks to the use of the score distillation sampling (SDS) method on pre-trained diffusion models for image generation. However, the SDS method is also the source of several artifacts, such as the Janus problem, the misalignment between the text prompt and the generated 3D model, and 3D model inaccuracies. While existing methods heavily rely on the qualitative assessment of these artifacts through visual inspection of a limited set of samples, in this work we propose more objective quantitative evaluation metrics, which we cross-validate via human ratings, and show analysis of the failure cases of the SDS technique. We demonstrate the effectiveness of this analysis by designing a novel computationally efficient baseline model that achieves state-of-the-art performance on the proposed metrics while addressing all the above-mentioned artifacts.

Towards Visual Foundational Models of Physical Scenes

We describe a first step towards learning general-purpose visual representations of physical scenes using only image prediction as a training criterion. To do so, we first define "physical scene" and show that, even though different agents may maintain different representations of the same scene, the underlying physical scene that can be inferred is unique. Then, we show that NeRFs cannot represent the physical scene, as they lack extrapolation mechanisms. Those, however, could be provided by Diffusion Models, at least in theory. To test this hypothesis empirically, NeRFs can be combined with Diffusion Models, a process we refer to as NeRF Diffusion, used as unsupervised representations of the physical scene. Our analysis is limited to visual data, without external grounding mechanisms that can be provided by independent sensory modalities.

Fast Direct Stereo Visual SLAM

We propose a novel approach for fast and accurate stereo visual Simultaneous Localization and Mapping (SLAM) independent of feature detection and matching. We extend monocular Direct Sparse Odometry (DSO) to a stereo system by optimizing the scale of the 3D points to minimize photometric error for the stereo configuration, which yields a computationally efficient and robust method compared to conventional stereo matching. We further extend it to a full SLAM system with loop closure to reduce accumulated errors. With the assumption of forward camera motion, we imitate a LiDAR scan using the 3D points obtained from the visual odometry and adapt a LiDAR descriptor for place recognition to facilitate more efficient detection of loop closures. Afterward, we estimate the relative pose using direct alignment by minimizing the photometric error for potential loop closures. Optionally, further improvement over direct alignment is achieved by using the Iterative Closest Point (ICP) algorithm. Lastly, we optimize a pose graph to improve SLAM accuracy globally. By avoiding feature detection or matching in our SLAM system, we ensure high computational efficiency and robustness. Thorough experimental validations on public datasets demonstrate its effectiveness compared to the state-of-the-art approaches.

Continuous-Time Spline Visual-Inertial Odometry

We propose a continuous-time spline-based formulation for visual-inertial odometry (VIO). Specifically, we model the poses as a cubic spline, whose temporal derivatives are used to synthesize linear acceleration and angular velocity, which are compared to the measurements from the inertial measurement unit (IMU) for optimal state estimation. The spline boundary conditions create constraints between the camera and the IMU, with which we formulate VIO as a constrained nonlinear optimization problem. Continuous-time pose representation makes it possible to address many VIO challenges, e.g., rolling shutter distortion and sensors that may lack synchronization. We conduct experiments on two publicly available datasets that demonstrate the state-of-the-art accuracy and real-time computational efficiency of our method.

Learning Rolling Shutter Correction from Real Data without Camera Motion Assumption

The rolling shutter mechanism in modern cameras generates distortions as the images are formed on the sensor through a row-by-row readout process; this is highly undesirable for photography and vision-based algorithms (e.g., structure-from-motion and visual SLAM). In this paper, we propose a deep neural network to predict depth and camera poses for single-frame rolling shutter correction. Compared to the state-of-the-art, the proposed method has no assumptions on camera motion. It is enabled by training on real images captured by rolling shutter cameras instead of synthetic ones generated with certain motion assumption. Consequently, the proposed method performs better for real rolling shutter images. This makes it possible for numerous vision-based algorithms to use imagery captured using rolling shutter cameras and produce highly accurate results. Our evaluations on the TUM rolling shutter dataset using DSO and COLMAP validate the accuracy and robustness of the proposed method.

Design and Experiments with LoCO AUV: A Low Cost Open-Source Autonomous Underwater Vehicle

In this paper we present LoCO AUV, a Low-Cost, Open Autonomous Underwater Vehicle. LoCO is a general-purpose, single-person-deployable, vision-guided AUV, rated to a depth of 100 meters. We discuss the open and expandable design of this underwater robot, as well as the design of a simulator in Gazebo. Additionally, we explore the platform's preliminary local motion control and state estimation abilities, which enable it to perform maneuvers autonomously. In order to demonstrate its usefulness for a variety of tasks, we implement a variety of our previously presented human-robot interaction capabilities on LoCO, including gestural control, diver following, and robot communication via motion. Finally, we discuss the practical concerns of deployment and our experiences in using this robot in pools, lakes, and the ocean. All design details, instructions on assembly, and code will be released under a permissive, open-source license.

Place Recognition for Stereo VisualOdometry using LiDAR Descriptors

Place recognition is a core component in SLAM, and in most visual SLAM systems, it is based on the similarity between 2D images. However, the 3D points generated by visual odometry, and the structure information embedded within, are not exploited. In this paper, we adapt place recognition methods for 3D point clouds into stereo visual odometry. Stereo visual odometry generates 3D point clouds with a consistent scale. Thus, we are able to use global LiDAR descriptors for 3D point clouds to determine the similarity between places. 3D point clouds are more reliable than 2D visual cues (e.g., 2D features) against environmental changes such as varying illumination and can benefit visual SLAM systems in long-term deployment scenarios. Extensive evaluation on a public dataset (Oxford RobotCar) demonstrates the accuracy and efficiency of using 3D point clouds for place recognition over 2D methods.

Place Recognition for Stereo VisualOdometry using LiDAR descriptors

Place recognition is a core component in SLAM, and in most visual SLAM systems, it is based on the similarity between 2D images. However, the 3D points generated by visual odometry, and the structure information embedded within, are not exploited. In this paper, we adapt place recognition methods for 3D point clouds into stereo visual odometry. Stereo visual odometry generates 3D point clouds with a consistent scale. Thus, we are able to use global LiDAR descriptors for 3D point clouds to determine the similarity between places. 3D point clouds are more reliable than 2D visual cues (e.g., 2D features) against environmental changes such as varying illumination and can benefit visual SLAM systems in long-term deployment scenarios. Extensive evaluation on a public dataset (Oxford RobotCar) demonstrates the accuracy and efficiency of using 3D point clouds for place recognition over 2D methods.

Extending Monocular Visual Odometry to Stereo Camera System by Scale Optimization

This paper proposes a novel approach for extending monocular visual odometry to a stereo camera system. The proposed method uses an additional camera to accurately estimate and optimize the scale of the monocular visual odometry, rather than triangulating 3D points from stereo matching. Specifically, the 3D points generated by the monocular visual odometry are projected onto the other camera of the stereo pair, and the scale is recovered and optimized by directly minimizing the photometric error. In particular, it is computationally efficient, adding minimal overhead to the stereo vision system compared to straightforward stereo matching, and is robust to repetitive texture. Additionally, direct scale optimization enables stereo visual odometry to be purely based on direct method. Extensive evaluation on public datasets (e.g., KITTI), and outdoor environments (both terrestrial and underwater) demonstrates the accuracy and efficiency of a stereo visual odometry approach extended by scale optimization, as well as the robustness in environments with challenging texture.