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Zhaoyang Li, Yuan Wang, Wangkai Li, Rui Sun, Tianzhu Zhang

Point cloud few-shot semantic segmentation (PC-FSS) aims to segment targets of novel categories in a given query point cloud with only a few annotated support samples. The current top-performing prototypical learning methods employ prototypes originating from support samples to direct the classification of query points. However, the inherent fragility of point-level matching and the prevalent intra-class diversity pose great challenges to this cross-instance matching paradigm, leading to erroneous background activations or incomplete target excavation. In this work, we propose a simple yet effective framework in the spirit of Decoupled Localization and Expansion (DLE). The proposed DLE, including a structural localization module (SLM) and a self-expansion module (SEM), enjoys several merits. First, structural information is injected into the matching process through the agent-level correlation in SLM, and the confident target region can thus be precisely located. Second, more reliable intra-object similarity is harnessed in SEM to derive the complete target, and the conservative expansion strategy is introduced to reasonably constrain the expansion. Extensive experiments on two challenging benchmarks under different settings demonstrate that DLE outperforms previous state-of-the-art approaches by large margins.

Ruiyang Zhang, Hu Zhang, Hang Yu, Zhedong Zheng

The open-world 3D object detection is to accurately detect objects in unstructured environments with no explicit supervisory signals. This task, given sparse LiDAR point clouds, often results in compromised performance for detecting small or distant objects due to the inherent sparsity and limited spatial resolution. In this paper, we are among the early attempts to integrate LiDAR data with 2D images for open-world 3D detection and introduce a new method, dubbed LiDAR-2D Self-paced Learning (LiSe). We argue that RGB images serve as a valuable complement to LiDAR data, offering precise 2D localization cues, particularly when scarce LiDAR points are available for certain objects. Considering the unique characteristics of both modalities, our framework devises a self-paced learning pipeline that incorporates adaptive sampling and weak model aggregation strategies. The adaptive sampling strategy dynamically tunes the distribution of pseudo labels during training, countering the tendency of models to overfit on easily detected samples, such as nearby and large-sized objects. By doing so, it ensures a balanced learning trajectory across varying object scales and distances. The weak model aggregation component consolidates the strengths of models trained under different pseudo label distributions, culminating in a robust and powerful final model. Experimental evaluations validate the efficacy of our proposed LiSe method, manifesting significant improvements of $+7.1$\% AP$_{BEV}$ and $+3.4$\% AP$_{3D}$ on nuScenes compared to existing techniques. To facilitate further research and replication, we will publicly release the code and pre-trained checkpoints.

Shun Iwase, Katherine Liu, Vitor Guizilini, Adrien Gaidon, Kris Kitani, Rares Ambrus, Sergey Zakharov

We present a 3D scene completion method that recovers the complete geometry of multiple unseen objects in complex scenes from a single RGB-D image. Despite notable advancements in single-object 3D shape completion, high-quality reconstructions in highly cluttered real-world multi-object scenes remains a challenge. To address this issue, we propose OctMAE, an architecture that leverages an Octree U-Net and a latent 3D MAE to achieve high-quality and near real-time multi-object scene completion through both local and global geometric reasoning. Because a naive 3D MAE can be computationally intractable and memory intensive even in the latent space, we introduce a novel occlusion masking strategy and adopt 3D rotary embeddings, which significantly improves the runtime and scene completion quality. To generalize to a wide range of objects in diverse scenes, we create a large-scale photorealistic dataset, featuring a diverse set of 12K 3D object models from the Objaverse dataset which are rendered in multi-object scenes with physics-based positioning. Our method outperforms the current state-of-the-art on both synthetic and real-world datasets and demonstrates a strong zero-shot capability.

Li Xiaofan, Zhang Yifu, Xiaoqing Ye

With the surge in autonomous driving technologies, the reliance on comprehensive and high-definition bird's-eye-view (BEV) representations has become paramount. This burgeoning need underscores the demand for extensive multi-view video datasets, meticulously annotated to facilitate advanced research and development. Nonetheless, the acquisition of such datasets is impeded by prohibitive costs associated with data collection and annotation. There are two challenges when synthesizing multi-view videos given a 3D layout: Generating multi-view videos involves handling both view and temporal dimensions. How to generate videos while ensuring cross-view consistency and cross-frame consistency? 2) How to ensure the precision of layout control and the quality of the generated instances? Addressing this critical bottleneck, we introduce a novel spatial-temporal consistent diffusion framework, DrivingDiffusion, engineered to synthesize realistic multi-view videos governed by 3D spatial layouts. DrivingDiffusion adeptly navigates the dual challenges of maintaining cross-view and cross-frame coherence, along with meeting the exacting standards of layout fidelity and visual quality. The framework operates through a tripartite methodology: initiating with the generation of multi-view single-frame images, followed by the synthesis of single-view videos across multiple cameras, and culminating with a post-processing phase. We corroborate the efficacy of DrivingDiffusion through rigorous quantitative and qualitative evaluations, demonstrating its potential to significantly enhance autonomous driving tasks without incurring additional costs.

Weitai Kang, Gaowen Liu, Shah Mubarak, Yan Yan

Different from Object Detection, Visual Grounding deals with detecting a bounding box for each text-image pair. This one box for each text-image data provides sparse supervision signals. Although previous works achieve impressive results, their passive utilization of annotation, i.e. the sole use of the box annotation as regression ground truth, results in a suboptimal performance. In this paper, we present SegVG, a novel method transfers the box-level annotation as Segmentation signals to provide an additional pixel-level supervision for Visual Grounding. Specifically, we propose the Multi-layer Multi-task Encoder-Decoder as the target grounding stage, where we learn a regression query and multiple segmentation queries to ground the target by regression and segmentation of the box in each decoding layer, respectively. This approach allows us to iteratively exploit the annotation as signals for both box-level regression and pixel-level segmentation. Moreover, as the backbones are typically initialized by pretrained parameters learned from unimodal tasks and the queries for both regression and segmentation are static learnable embeddings, a domain discrepancy remains among these three types of features, which impairs subsequent target grounding. To mitigate this discrepancy, we introduce the Triple Alignment module, where the query, text, and vision tokens are triangularly updated to share the same space by triple attention mechanism. Extensive experiments on five widely used datasets validate our state-of-the-art (SOTA) performance.

Manu S Pillai, Mamshad Nayeem Rizve, Shah Mubarak

Cross-view video geo-localization (CVGL) aims to obtain a GPS trajectory of a street-view video by matching it with reference aerial-view images. Despite exhibiting promising performance, current CVGL methods face key limitations. They often rely on camera intrinsic and odometry information, utilize context from multiple frames to obtain frame-level features, leading to high computational overhead, and generate temporally inconsistent GPS trajectories by independently retrieving each street-view frame. To address these challenges, in this work, we propose TransCVGL, the first fully transformer-based method for cross-view video geo-localization. We hypothesize that video geo-localization does not require complex temporal modeling, unlike other common video understanding tasks such as action recognition. Instead, we demonstrate that the representations from a street-view geo-localization model can be efficiently aggregated to obtain video-level representation. To achieve this, we propose a transformer-adapter module, GeoAdapter, to aggregate image-level representations of an image geo-localization model and to adapt it to video inputs. Furthermore, to ensure temporally consistent GPS predictions, we introduce TransRetriever, the first transformer-based approach that models independent frame retrievals through an auto-regressive transformer decoder. Finally, we validate the efficacy of our method through extensive experiments, showcasing state-of-the-art performance in benchmark datasets.

Yiqun Duan, Xianda Guo, Zheng Zhu

Monocular depth estimation is a challenging task that predicts the pixel-wise depth from a single 2D image. Current methods typically model this problem as a regression or classification task. We propose DiffusionDepth, a new approach that reformulates monocular depth estimation as a denoising diffusion process. It learns an iterative denoising process to `denoise' random depth distribution into a depth map with the guidance of monocular visual conditions. The process is performed in the latent space encoded by a dedicated depth encoder and decoder. Instead of diffusing ground truth (GT) depth, the model learns to reverse the process of diffusing the refined depth of itself into random depth distribution. This self-diffusion formulation overcomes the difficulty of applying generative models to sparse GT depth scenarios. The proposed approach benefits this task by refining depth estimation step by step, which is superior for generating accurate and highly detailed depth maps. Experimental results on KITTI and NYU-Depth-V2 datasets suggest that a simple yet efficient diffusion approach could reach state-of-the-art performance in both indoor and outdoor scenarios with acceptable inference time.

Xiaofeng Wang, Zheng Zhu, Guan Huang, Chen Xinze, Jiagang Zhu, Jiwen Lu

World models, especially in autonomous driving, are trending and drawing extensive attention due to their capacity for comprehending driving environments. The established world model holds immense potential for the generation of high-quality driving videos, and driving policies for safe maneuvering. However, a critical limitation in relevant research lies in its predominant focus on gaming environments or simulated settings, thereby lacking the representation of real-world driving scenarios. Therefore, we introduce \textit{DriveDreamer}, a pioneering world model entirely derived from real-world driving scenarios. Regarding that modeling the world in intricate driving scenes entails an overwhelming search space, we propose harnessing the powerful diffusion model to construct a comprehensive representation of the complex environment. Furthermore, we introduce a two-stage training pipeline. In the initial phase, \textit{DriveDreamer} acquires a deep understanding of structured traffic constraints, while the subsequent stage equips it with the ability to anticipate future states. Extensive experiments are conducted to verify that \textit{DriveDreamer} empowers both driving video generation and action prediction, faithfully capturing real-world traffic constraints. Furthermore, videos generated by \textit{DriveDreamer} significantly enhance the training of driving perception methods.

Chuanrui Zhang, Yonggen Ling, Minglei Lu, Minghan Qin, Haoqian Wang

We study the 3D object understanding task for manipulating everyday objects with different material properties (diffuse, specular, transparent and mixed). Existing monocular and RGB-D methods suffer from scale ambiguity due to missing or imprecise depth measurements. We present CODERS, a one-stage approach for Category-level Object Detection, pose Estimation and Reconstruction from Stereo images. The base of our pipeline is an implicit stereo matching module that combines stereo image features with 3D position information. Concatenating this presented module and the following transform-decoder architecture leads to end-to-end learning of multiple tasks required by robot manipulation. Our approach significantly outperforms all competing methods in the public TOD dataset. Furthermore, trained on simulated data, CODERS generalize well to unseen category-level object instances in real-world robot manipulation experiments. Our dataset, code, and demos will be available on our project page.

Lingni Ma, Yuting Ye, Rowan Postyeni, Alexander J Gamino, Vijay Baiyya, Luis Pesqueira, Kevin M Bailey, David Soriano Fosas, Fangzhou Hong, Vladimir Guzov 等

We introduce Nymeria - a large-scale, diverse, richly annotated human motion dataset collected in the wild with multi-modal egocentric devices. The dataset comes with a) full-body motion ground truth; b) egocentric multimodal recordings from Project Aria devices, including color, grayscale and eye-tracking cameras, IMUs, magnetometer, barometer, and multi-channel microphones; and c) an additional "observer" device providing a third-person viewpoint. We compute world-aligned 6DoF transformations for all sensors, across devices and capture sessions. The dataset also provides 3D scene point clouds and calibrated eye gaze. We derive a protocol to annotate hierarchical language descriptions of in-context human motion, from fine-grain dense body pose narrations, to simplified atomic actions and coarse activity summarization. To the best of our knowledge, Nymeria dataset is the world's largest collection of human motion in the wild with natural and diverse activities; first of its kind to provide synchronized and localized multi-device multimodal egocentric data; and also the world’s largest dataset of motion with language descriptions. It contains 1200 recordings of 300 hours daily activities from 264 participants 50 locations. The accumulated trajectory from participants is 399.2Km for the head and 1053.3Km for both wrists. To facilitate research, we define multiple research tasks in egocentric body tracking, motion synthesis, and action recognition. The performance of several state-of-the-art algorithms are reported. We will open-source the data and code to empower future exploration of the research community.

Jiaman Li, Alexander Clegg, Roozbeh Mottaghi, Jiajun Wu, Xavier Puig, Karen Liu

Synthesizing semantic-aware, long-horizon, human-object interaction is critical to simulate realistic human behaviors. In this work, we address the challenging problem of generating synchronized object motion and human motion guided by language descriptions in 3D scenes. We propose Controllable Human-Object Interaction Synthesis (CHOIS), an approach that generates object motion and human motion simultaneously using a conditional diffusion model given a language description, initial object and human states, and sparse object waypoints. Here, language descriptions inform style and intent, and waypoints, which can be effectively extracted from high-level planning, ground the motion in the scene. Naively applying a diffusion model fails to predict object motion aligned with the input waypoints; it also cannot ensure the realism of interactions that require precise hand-object and human-floor contact. To overcome these problems, we introduce an object geometry loss as additional supervision to improve the matching between generated object motion and input object waypoints; we also design guidance terms to enforce contact constraints during the sampling process of the trained diffusion model. We demonstrate that our learned interaction module can synthesize realistic human-object interactions, adhering to provided textual descriptions and sparse waypoint conditions. Additionally, our module seamlessly integrates with a path planning module, enabling the generation of long-term interactions in 3D environments.

Mingfang Zhang, Yifei Huang, Ruicong Liu, Yoichi Sato

Compared with visual signals, Inertial Measurement Units (IMUs) placed on human limbs can capture accurate motion signals while being robust to lighting variation and occlusion. While these characteristics are intuitively valuable to help egocentric action recognition, the potential of IMUs remains under-explored. In this work, we present a novel method for action recognition that integrates motion data from body-worn IMUs with egocentric video. Due to the scarcity of labeled multimodal data, we design an MAE-based self-supervised pretraining method, obtaining strong multi-modal representation via modeling the natural correlation between visual and motion signals. To model the complex relation of multiple IMU devices placed across the body, we exploit the collaborative dynamics in multiple IMU devices and propose to embed the relative motion features of human joints into a graph structure. Experiments show our method can achieve state-of-the-art performance on multiple public datasets. The effectiveness of our MAE-based pretraining and graph-based IMU modeling are further validated by experiments in more challenging scenarios, including partially missing IMU devices and video quality corruption, promoting more flexible usages in the real world.

Aditya Prakash, Ruisen Tu, Matthew Chang, Saurabh Gupta

3D hand pose estimation in everyday egocentric images is challenging for several reasons: poor visual signal (occlusion from the object of interaction, low resolution & motion blur), large perspective distortion (hands are close to the camera), and lack of 3D annotations outside of controlled settings. While existing methods often use hand crops as input to focus on fine-grained visual information to deal with poor visual signal, the challenges arising from perspective distortion and lack of 3D annotations in the wild have not been systematically studied. We focus on this gap and explore the impact of different practices, i.e. crops as input, incorporating camera information, auxiliary supervision, scaling up datasets. Based on our findings, we present WildHands, a system for 3D hand pose estimation in everyday egocentric images. Zero-shot evaluation on four diverse datasets (H2O, Assembly, Epic, and EgoExo4D) demonstrate the effectiveness of our approach across 2D and 3D metrics, where we beat past methods by 7.4% -- 66%. In system level comparisons, WildHands achieves the best 3D hand pose score on the egocentric split of the ARCTIC, beats the popular FrankMocap system and is competitive the concurrent HaMeR system.

Ziyang Liang, Yiwei Bao, Feng Lu

Deep-learning based gaze estimation methods suffer from sever performance degradation in cross-domain settings. One of the primary reason is that the gaze estimation model is confounded by gaze-irrelevant factor during estimation, such as identity and illumination. In this paper, we propose to tackle this problem by causal intervention, an analytical tool that alleviates the impact of confounding factors by using intervening the distribution of confounding factors. Concretely, we propose the Feature-Separation-based Causal Intervention (FSCI) framework for generalizable gaze estimation. The FSCI framework first separates gaze features from gaze-irrelevant features. To alleviate the impact of gaze-irrelevant factors during training, the FSCI framework further implements causal intervention by averaging gaze-irrelevant features using the proposed Dynamic Confounder Bank strategy. Experiments show that the proposed FSCI framework outperforms SOTA gaze estimation methods in varies cross-domain settings, improving the cross-domain accuracy of the baseline up to 36.2% without touching target domain data.

Artur Jesslen, Guofeng Zhang, Angtian Wang, Wufei Ma, Alan Yuille, Adam Kortylewski

Discriminative models for object classification typically learn image-based representations that do not capture the compositional and 3D nature of objects. In this work, we show that explicitly integrating 3D compositional object representations into deep networks for image classification leads to a largely enhanced generalization in out-of-distribution scenarios. In particular, we introduce a novel architecture, referred to as NOVUM, that consists of a feature extractor and a neural object volume for every target object class. Each neural object volume is a composition of 3D Gaussians that emit feature vectors. This compositional object representation allows for a highly robust and fast estimation of the object class by independently matching the features of the 3D Gaussians of each category to features extracted from an input image. Additionally, the object pose can be estimated via inverse rendering of the corresponding neural object volume. To enable the classification of objects, the neural features at each 3D Gaussian are trained discriminatively to be distinct from (i) the features of 3D Gaussians in other categories, (ii) features of other 3D Gaussians of the same object, and (iii) the background features. Our experiments show that NOVUM offers intriguing advantages over standard architectures due to the 3D compositional structure of the object representation, namely: (1) An exceptional robustness across a spectrum of real-world and synthetic out-of-distribution shifts and (2) an enhanced human interpretability compared to standard models, all while maintaining real-time inference and a competitive accuracy on in-distribution data.

Ruilong Li, Sanja Fidler, Angjoo Kanazawa, Francis Williams

In this paper, we first revisit the existing approach of decomposing large-scale scenes into multiple independently trained Neural Radiance Fields (NeRFs), and identify several fundamental issues that hinder performance improvement with additional computational resources (GPUs), which contradicts the fundamental objective of leveraging multi-GPU setups to enhance large-scale NeRF performance. Subsequently, we introduce NeRF-XL, a principled algorithm designed to efficiently harness multi-GPU setups for performance improvement, thereby enabling NeRF at any scale. At its core, our method allocates non-overlapping NeRFs across disjoint spatial regions and optimizes them jointly across GPUs. We reduce the GPU communication overhead by rewriting the volume rendering equation and relevant loss terms, enhancing training and rendering efficiency. Without any heuristics, our approach gracefully reveals scaling laws for NeRFs in the multi-GPU setting across various types of data and scales, including the first time NeRF reconstruction on the largest open-source dataset to date, MatrixCity, with 258K images covering a 25km^2 city area.

Jianhao Zheng, Daniel Barath, Marc Pollefeys, Iro Armeni

Creating 3D semantic reconstructions of environments is fundamental to many applications, especially when related to autonomous agent operation (e.g. goal-oriented navigation and object interaction and manipulation). Commonly, 3D semantic reconstruction systems capture the entire scene in the same level of detail. However, certain tasks (e.g., object interaction) require a fine-grained and high-resolution map, particularly if the objects to interact are of small size or intricate geometry. In recent practice, this leads to the entire map being in the same high-quality resolution, which results in increased computational and storage costs. To address this challenge we propose MAP-ADAPT, a real-time method for quality-adaptive semantic 3D reconstruction using RGBD frames. MAP-ADAPT is the first adaptive semantic 3D mapping algorithm that, unlike prior work, generates directly a single map with regions of different quality based on both the semantic information and the geometric complexity of the scene. Leveraging a semantic SLAM pipeline for pose and semantic estimation, we achieve comparable or superior results to state-of-the-art methods on a synthetic and a real-world dataset, while significantly reducing storage and computation requirements.

Runzhao Yao, Shaoyi Du, Wenting Cui, Canhui Tang, Chengwu Yang

Learning rotation-invariant distinctive features is a fundamental requirement for point cloud registration. Existing methods often use rotation-sensitive networks to extract features, while employing rotation augmentation to rudely learn an approximate invariant mapping. This makes networks fragile to rotations, overweight, and hinders the distinctiveness of features. To tackle these problems, we propose a novel position-aware rotation-equivariant network, for efficient, light-weighted, and robust registration. The network can provide a strong model inductive bias to learn rotation-equivariant/invariant features, thus addressing the aforementioned limitations. To further improve the distinctiveness of descriptors, we propose a position-aware convolution, which can better learn spatial information of local structures. Moreover, we also propose a feature-based hypothesis proposer. It leverages rotation-equivariant features that encode fine-grained structure orientations to generate reliable model hypotheses. Each correspondence can generate a hypothesis, thus it is more efficient than classic estimators that require multiple reliable correspondences. Accordingly, a contrastive rotation loss is presented to enhance the robustness of rotation-equivariant features against data degradation. Extensive experiments on indoor and outdoor datasets demonstrate that our method significantly outperforms the SOTA methods in terms of registration recall while being lightweight and keeping a fast speed. Moreover, experiments on rotated datasets demonstrate its robustness against rotation variations. All codes will be available.

Yukun Wang, Kunhong Li, Minglin Chen, Longguang Wang, Shunbo Zhou, Kaiwen Xue, Yulan Guo

Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) have greatly advanced novel view synthesis, which is capable of photo-realistic rendering. However, these methods require the foundational assumption of the static scene (e.g., consistent lighting condition and persistent object positions), which is often violated in real-world scenarios. In this study, we introduce MemE, an unsupervised plug-and-play module, to achieve high-quality novel view synthesis in noisy input scenarios. MemE leverages the inherent property in parameter optimization, known as the memorization effect to achieve distractor filtering and can be easily combined with NeRF or 3DGS. Furthermore, MemE is applicable in environments both with and without distractors, significantly enhancing the adaptability of NeRF and 3DGS across diverse input scenarios. Extensive experiments show that our methods (i.e., MemE-NeRF and MemE-3DGS) achieve state-of-the-art performance on both real and synthetic noisy scenes.

Yongjian Zhang, Longguang Wang, Kunhong Li, WANG Yun, Yulan Guo

State-of-the-art stereo matching networks trained on in-domain data often underperform on cross-domain scenes. Intuitively, leveraging the zero-shot capacity of a foundation model can alleviate the cross-domain generalization problem. The main challenge of incorporating a foundation model into stereo matching pipeline lies in the absence of an effective forward process from single-view coarse-grained tokens to cross-view fine-grained cost representations. In this paper, we propose FormerStereo, a general framework that integrates the Vision Transformer (ViT) based foundation model into the stereo matching pipeline. Using this framework, we transfer the all-purpose features to matching-specific ones. Specifically, we propose a reconstruction-constrained decoder to retrieve fine-grained representations from coarse-grained ViT tokens. To maintain cross-view consistent representations, we propose a cosine-constrained concatenation cost (C4) space to construct cost volumes. We integrate FormerStereo with state-of-the-art (SOTA) stereo matching networks and evaluate its effectiveness on multiple benchmark datasets. Experiments show that the FormerStereo framework effectively improves the zero-shot performance of existing stereo matching networks on unseen domains and achieves SOTA performance.