CVPR 2024 Papers

6DoF Pose estimation has been gaining increased importance in vision for over a decade however it does not yet meet the reliability and accuracy standards for mass deployment in industrial robotics. To this effect we present the Industrial Plenoptic Dataset (IPD): the first dataset for the co-evaluation of cameras HDR and algorithms targeted at reliable high-accuracy industrial automation. Specifically we capture 2300 physical scenes of 20 industrial parts covering a 1mx1mx0.5m working volume resulting in over 100000 distinct object views. Each scene is captured with 13 well-calibrated multi-modal cameras including polarization and high-resolution structured light. In terms of lighting we capture each scene at 4 exposures and in 3 challenging lighting conditions ranging from 100 lux to 100000 lux. We also present validate and analyze robot consistency an evaluation method targeted at scalable high accuracy evaluation. We hope that vision systems that succeed on this dataset will have direct industry impact. The dataset and evaluation code are available at https://github.com/intrinsic-ai/ipd.

This paper proposes to correct the rolling shutter (RS) distorted images by estimating the distortion flow from the global shutter (GS) to RS directly. Existing methods usually perform correction using the undistortion flow from the RS to GS. They initially predict the flow from consecutive RS frames subsequently rescaling it as the displacement fields from the RS frame to the underlying GS image using time-dependent scaling factors. Following this RS-aware forward warping is employed to convert the RS image into its GS counterpart. Nevertheless this strategy is prone to two shortcomings. First the undistortion flow estimation is rendered inaccurate by merely linear scaling the flow due to the complex non-linear motion nature. Second RS-aware forward warping often results in unavoidable artifacts. To address these limitations we introduce a new framework that directly estimates the distortion flow and rectifies the RS image with the backward warping operation. More specifically we first propose a global correlation-based flow attention mechanism to estimate the initial distortion flow and GS feature jointly which are then refined by the following coarse-to-fine decoder layers. Additionally a multi-distortion flow prediction strategy is integrated to mitigate the issue of inaccurate flow estimation further. Experimental results validate the effectiveness of the proposed method which outperforms state-of-the-art approaches on various benchmarks while maintaining high efficiency. The project is available at https://github.com/ljzycmd/DFRSC.

Global translation estimation is a highly challenging step in the global structure from motion (SfM) algorithm. Many existing methods depend solely on relative translations leading to inaccuracies in low parallax scenes and degradation under collinear camera motion. While recent approaches aim to address these issues by incorporating feature tracks into objective functions they are often sensitive to outliers. In this paper we first revisit global translation estimation methods with feature tracks and categorize them into explicit and implicit methods. Then we highlight the superiority of the objective function based on the cross-product distance metric and propose a novel explicit global translation estimation framework that integrates both relative translations and feature tracks as input. To enhance the accuracy of input observations we re-estimate relative translations with the coplanarity constraint of the epipolar plane and propose a simple yet effective strategy to select reliable feature tracks. Finally the effectiveness of our approach is demonstrated through experiments on urban image sequences and unordered Internet images showcasing its superior accuracy and robustness compared to many state-of-the-art techniques.

Passive compact single-shot 3D sensing is useful in many application areas such as microscopy medical imaging surgical navigation and autonomous driving where form factor time and power constraints can exist. Obtaining RGB-D scene information over a short imaging distance in an ultra-compact form factor and in a passive snapshot manner is challenging. Dual-pixel (DP) sensors are a potential solution to achieve the same. DP sensors collect light rays from two different halves of the lens in two interleaved pixel arrays thus capturing two slightly different views of the scene like a stereo camera system. However imaging with a DP sensor implies that the defocus blur size is directly proportional to the disparity seen between the views. This creates a trade-off between disparity estimation vs. deblurring accuracy. To improve this trade-off effect we propose CADS (Coded Aperture Dual-Pixel Sensing) in which we use a coded aperture in the imaging lens along with a DP sensor. In our approach we jointly learn an optimal coded pattern and the reconstruction algorithm in an end-to-end optimization setting. Our resulting CADS imaging system demonstrates improvement of >1.5dB PSNR in all-in-focus (AIF) estimates and 5-6% in depth estimation quality over naive DP sensing for a wide range of aperture settings. Furthermore we build the proposed CADS prototypes for DSLR photography settings and in an endoscope and a dermoscope form factor. Our novel coded dual-pixel sensing approach demonstrates accurate RGB-D reconstruction results in simulations and real-world experiments in a passive snapshot and compact manner.

Inherent ambiguity in layout annotations poses significant challenges to developing accurate 360deg room layout estimation models. To address this issue we propose a novel Bi-Layout model capable of predicting two distinct layout types. One stops at ambiguous regions while the other extends to encompass all visible areas. Our model employs two global context embeddings where each embedding is designed to capture specific contextual information for each layout type. With our novel feature guidance module the image feature retrieves relevant context from these embeddings generating layout-aware features for precise bi-layout predictions. A unique property of our Bi-Layout model is its ability to inherently detect ambiguous regions by comparing the two predictions. To circumvent the need for manual correction of ambiguous annotations during testing we also introduce a new metric for disambiguating ground truth layouts. Our method demonstrates superior performance on benchmark datasets notably outperforming leading approaches. Specifically on the MatterportLayout dataset it improves 3DIoU from 81.70% to 82.57% across the full test set and notably from 54.80% to 59.97% in subsets with significant ambiguity.

Multi-view inverse rendering is the problem of estimating the scene parameters such as shapes materials or illuminations from a sequence of images captured under different viewpoints. Many approaches however assume single light bounce and thus fail to recover challenging scenarios like inter-reflections. On the other hand simply extending those methods to consider multi-bounced light requires more assumptions to alleviate the ambiguity. To address this problem we propose Neural Incident Stokes Fields (NeISF) a multi-view inverse rendering framework that reduces ambiguities using polarization cues. The primary motivation for using polarization cues is that it is the accumulation of multi-bounced light providing rich information about geometry and material. Based on this knowledge the proposed incident Stokes field efficiently models the accumulated polarization effect with the aid of an original physically-based differentiable polarimetric renderer. Lastly experimental results show that our method outperforms the existing works in synthetic and real scenarios.

We propose a single-shot approach to determining 6-DoF pose of an object with available 3D computer-aided design (CAD) model from a single RGB image. Our method dubbed MRC-Net comprises two stages. The first performs pose classification and renders the 3D object in the classified pose. The second stage performs regression to predict fine-grained residual pose within class. Connecting the two stages is a novel multi-scale residual correlation (MRC) layer that captures high-and-low level correspondences between the input image and rendering from first stage. MRC-Net employs a Siamese network with shared weights between both stages to learn embeddings for input and rendered images. To mitigate ambiguity when predicting discrete pose class labels on symmetric objects we use soft probabilistic labels to define pose class in the first stage. We demonstrate state-of-the-art accuracy outperforming all competing RGB-based methods on four challenging BOP benchmark datasets: T-LESS LM-O YCB-V and ITODD. Our method is non-iterative and requires no complex post-processing. Our code and pretrained models are available at https://github.com/amzn/mrc-net-6d-pose

We present a lightweight solution for estimating spatially-coherent indoor lighting from a single RGB image. Previous methods for estimating illumination using volumetric representations have overlooked the sparse distribution of light sources in space necessitating substantial memory and computational resources for achieving high-quality results. We introduce a unified voxel octree-based illumination estimation framework to produce 3D spatially-coherent lighting. Additionally a differentiable voxel octree cone tracing rendering layer is proposed to eliminate regular volumetric representation throughout the entire process and ensure the retention of features across different frequency domains. This reduction significantly decreases spatial usage and required floating-point operations without substantially compromising precision. Experimental results demonstrate that our approach achieves high-quality coherent estimation with minimal cost compared to previous methods.

The perception of 3D motion of surrounding traffic participants is crucial for driving safety. While existing works primarily focus on general large motions we contend that the instantaneous detection and quantification of subtle motions is equally important as they indicate the nuances in driving behavior that may be safety critical such as behaviors near a stop sign of parking positions. We delve into this under-explored task examining its unique challenges and developing our solution accompanied by a carefully designed benchmark. Specifically due to the lack of correspondences between consecutive frames of sparse Lidar point clouds static objects might appear to be moving - the so-called swimming effect. This intertwines with the true object motion thereby posing ambiguity in accurate estimation especially for subtle motion. To address this we propose to leverage local occupancy completion of object point clouds to densify the shape cue and mitigate the impact of swimming artifacts. The occupancy completion is learned in an end-to-end fashion together with the detection of moving objects and the estimation of their motion instantaneously as soon as objects start to move. Extensive experiments demonstrate superior performance compared to standard 3D motion estimation approaches particularly highlighting our method's specialized treatment of subtle motion.

Scene flow characterizes the 3D motion between two LiDAR scans captured by an autonomous vehicle at nearby timesteps. Prevalent methods consider scene flow as point-wise unconstrained flow vectors that can be learned by either large-scale training beforehand or time-consuming optimization at inference. However these methods do not take into account that objects in autonomous driving often move rigidly. We incorporate this rigid-motion assumption into our design where the goal is to associate objects over scans and then estimate the locally rigid transformations. We propose ICP-Flow a learning-free flow estimator. The core of our design is the conventional Iterative Closest Point (ICP) algorithm which aligns the objects over time and outputs the corresponding rigid transformations. Crucially to aid ICP we propose a histogram-based initialization that discovers the most likely translation thus providing a good starting point for ICP. The complete scene flow is then recovered from the rigid transformations. We outperform state-of-the-art baselines including supervised models on the Waymo dataset and perform competitively on Argoverse-v2 and nuScenes. Further we train a feedforward neural network supervised by the pseudo labels from our model and achieve top performance among all models capable of real-time inference. We validate the advantage of our model on scene flow estimation with longer temporal gaps up to 0.4 seconds where other models fail to deliver meaningful results.

Human hands are highly articulated and versatile at handling objects. Jointly estimating the 3D poses of a hand and the object it manipulates from a monocular camera is challenging due to frequent occlusions. Thus existing methods often rely on intermediate 3D shape representations to increase performance. These representations are typically explicit such as 3D point clouds or meshes and thus provide information in the direct surroundings of the intermediate hand pose estimate. To address this we introduce HOISDF a Signed Distance Field (SDF) guided hand-object pose estimation network which jointly exploits hand and object SDFs to provide a global implicit representation over the complete reconstruction volume. Specifically the role of the SDFs is threefold: equip the visual encoder with implicit shape information help to encode hand-object interactions and guide the hand and object pose regression via SDF-based sampling and by augmenting the feature representations. We show that HOISDF achieves state-of-the-art results on hand-object pose estimation benchmarks (DexYCB and HO3Dv2). Code is available https://github.com/amathislab/HOISDF.

Gated cameras flood-illuminate a scene and capture the time-gated impulse response of a scene. By employing nanosecond-scale gates existing sensors are capable of capturing mega-pixel gated images delivering dense depth improving on today's LiDAR sensors in spatial resolution and depth precision. Although gated depth estimation methods deliver a million of depth estimates per frame their resolution is still an order below existing RGB imaging methods. In this work we combine high-resolution stereo HDR RCCB cameras with gated imaging allowing us to exploit depth cues from active gating multi-view RGB and multi-view NIR sensing -- multi-view and gated cues across the entire spectrum. The resulting capture system consists only of low-cost CMOS sensors and flood-illumination. We propose a novel stereo-depth estimation method that is capable of exploiting these multi-modal multi-view depth cues including the active illumination that is measured by the RCCB camera when removing the IR-cut filter. The proposed method achieves accurate depth at long ranges outperforming the next best existing method by 39% for ranges of 100 to 220 m in MAE on accumulated LiDAR ground-truth. Our code models and datasets are available here (https://light.princeton.edu/gatedrccbstereo/).

Addressing pose ambiguity in 6D object pose estimation from single RGB images presents a significant challenge particularly due to object symmetries or occlusions. In response we introduce a novel score-based diffusion method applied to the SE(3) group marking the first application of diffusion models to SE(3) within the image domain specifically tailored for pose estimation tasks. Extensive evaluations demonstrate the method's efficacy in handling pose ambiguity mitigating perspective-induced ambiguity and showcasing the robustness of our surrogate Stein score formulation on SE(3). This formulation not only improves the convergence of denoising process but also enhances computational efficiency. Thus we pioneer a promising strategy for 6D object pose estimation.

Visual navigation is to let the agent reach the target according to the continuous visual input. In most previous works visual navigation is usually assumed to be done in a static and ideal environment: the target is always reachable with no need to alter the environment. However the "messy" environments are more general and practical in our daily lives where the agent may get blocked by obstacles. Thus Interactive Navigation (InterNav) is introduced to navigate to the objects in more realistic "messy" environments according to the object interaction. Prior work on InterNav learns short-term interaction through extensive trials with reinforcement learning. However interaction does not guarantee efficient navigation that is planning obstacle interactions that make shorter paths and consume less effort is also crucial. In this paper we introduce an effect-oriented affordance map to enable long-term interactive navigation extending the existing map-based navigation framework to the domain of dynamic environment. We train a set of affordance functions predicting available interactions and the time cost of removing obstacles which informatively support an interactive modular system to address interaction and long-term planning. Experiments on the ProcTHOR simulator demonstrate the capability of our affordance-driven system in long-term navigation in complex dynamic environments.

Extracting planes from a 3D scene is useful for downstream tasks in robotics and augmented reality. In this paper we tackle the problem of estimating the planar surfaces in a scene from posed images. Our first finding is that a surprisingly competitive baseline results from combining popular clustering algorithms with recent improvements in 3D geometry estimation. However such purely geometric methods are understandably oblivious to plane semantics which are crucial to discerning distinct planes. To overcome this limitation we propose a method that predicts multi-view consistent plane embeddings that complement geometry when clustering points into planes. We show through extensive evaluation on the ScanNetV2 dataset that our new method outperforms existing approaches and our strong geometric baseline for the task of plane estimation.

Comparing different age estimation methods poses a challenge due to the unreliability of published results stemming from inconsistencies in the benchmarking process. Previous studies have reported continuous performance improvements over the past decade using specialized methods; however our findings challenge these claims. This paper identifies two trivial yet persistent issues with the currently used evaluation protocol and describes how to resolve them. We offer an extensive comparative analysis for state-of-the-art facial age estimation methods. Surprisingly we find that the performance differences between the methods are negligible compared to the effect of other factors such as facial alignment facial coverage image resolution model architecture or the amount of data used for pretraining. We use the gained insights to propose using FaRL as the backbone model and demonstrate its effectiveness on all public datasets. We make the source code and exact data splits public on GitHub and in the supplementary material.

Learning 3D scene flow from LiDAR point clouds presents significant difficulties including poor generalization from synthetic datasets to real scenes scarcity of real-world 3D labels and poor performance on real sparse LiDAR point clouds. We present a novel approach from the perspective of auto-labelling aiming to generate a large number of 3D scene flow pseudo labels for real-world LiDAR point clouds. Specifically we employ the assumption of rigid body motion to simulate potential object-level rigid movements in autonomous driving scenarios. By updating different motion attributes for multiple anchor boxes the rigid motion decomposition is obtained for the whole scene. Furthermore we developed a novel 3D scene flow data augmentation method for global and local motion. By perfectly synthesizing target point clouds based on augmented motion parameters we easily obtain lots of 3D scene flow labels in point clouds highly consistent with real scenarios. On multiple real-world datasets including LiDAR KITTI nuScenes and Argoverse our method outperforms all previous supervised and unsupervised methods without requiring manual labelling. Impressively our method achieves a tenfold reduction in EPE3D metric on the LiDAR KITTI dataset reducing it from 0.190m to a mere 0.008m error.