CVPR 2024 Papers

Existing point cloud semantic segmentation networks cannot identify unknown classes and update their knowledge due to a closed-set and static perspective of the real world which would induce the intelligent agent to make bad decisions. To address this problem we propose a Probability-Driven Framework (PDF) for open world semantic segmentation that includes (i) a lightweight U-decoder branch to identify unknown classes by estimating the uncertainties (ii) a flexible pseudo-labeling scheme to supply geometry features along with probability distribution features of unknown classes by generating pseudo labels and (iii) an incremental knowledge distillation strategy to incorporate novel classes into the existing knowledge base gradually. Our framework enables the model to behave like human beings which could recognize unknown objects and incrementally learn them with the corresponding knowledge. Experimental results on the S3DIS and ScanNetv2 datasets demonstrate that the proposed PDF outperforms other methods by a large margin in both important tasks of open world semantic segmentation.

We propose the problem of point-level 3D scene interpolation which aims to simultaneously reconstruct a 3D scene in two states from multiple views synthesize smooth point-level interpolations between them and render the scene from novel viewpoints all without any supervision between the states. The primary challenge is on achieving a smooth transition between states that may involve significant and non-rigid changes. To address these challenges we introduce "PAPR in Motion" a novel approach that builds upon the recent Proximity Attention Point Rendering (PAPR) technique which can deform a point cloud to match a significantly different shape and render a visually coherent scene even after non-rigid deformations. Our approach is specifically designed to maintain the temporal consistency of the geometric structure by introducing various regularization techniques for PAPR. The result is a method that can effectively bridge large scene changes and produce visually coherent and temporally smooth interpolations in both geometry and appearance. Evaluation across diverse motion types demonstrates that "PAPR in Motion" outperforms the leading neural renderer for dynamic scenes. For more results and code please visit our project website at https://niopeng.github.io/PAPR-in-Motion/.

Current approaches for 3D scene graph prediction rely on labeled datasets to train models for a fixed set of known object classes and relationship categories. We present Open3DSG an alternative approach to learn 3D scene graph prediction in an open world without requiring labeled scene graph data. We co-embed the features from a 3D scene graph prediction backbone with the feature space of powerful open world 2D vision language foundation models. This enables us to predict 3D scene graphs from 3D point clouds in a zero-shot manner by querying object classes from an open vocabulary and predicting the inter-object relationships from a grounded LLM with scene graph features and queried object classes as context. Open3DSG is the first 3D point cloud method to predict not only explicit open-vocabulary object classes but also open-set relationships that are not limited to a predefined label set making it possible to express rare as well as specific objects and relationships in the predicted 3D scene graph. Our experiments show that Open3DSG is effective at predicting arbitrary object classes as well as their complex inter-object relationships describing spatial supportive semantic and comparative relationships.

We introduce Open3DIS a novel solution designed to tackle the problem of Open-Vocabulary Instance Segmentation within 3D scenes. Objects within 3D environments exhibit diverse shapes scales and colors making precise instance-level identification a challenging task. Recent advancements in Open-Vocabulary scene understanding have made significant strides in this area by employing class-agnostic 3D instance proposal networks for object localization and learning queryable features for each 3D mask. While these methods produce high-quality instance proposals they struggle with identifying small-scale and geometrically ambiguous objects. The key idea of our method is a new module that aggregates 2D instance masks across frames and maps them to geometrically coherent point cloud regions as high-quality object proposals addressing the above limitations. These are then combined with 3D class-agnostic instance proposals to include a wide range of objects in the real world. To validate our approach we conducted experiments on three prominent datasets including ScanNet200 S3DIS and Replica demonstrating significant performance gains in segmenting objects with diverse categories over the state-of-the-art approaches.

Multimodal large language models (MLLMs) have gained significant attention due to their strong multimodal understanding capability. However existing works rely heavily on modality-specific encoders which usually differ in architecture and are limited to common modalities. In this paper we present OneLLM an MLLM that aligns eight modalities to language using a unified framework. We achieve this through a unified multimodal encoder and a progressive multimodal alignment pipeline. In detail we first train an image projection module to connect a vision encoder with LLM. Then we build a universal projection module (UPM) by mixing multiple image projection modules and dynamic routing. Finally we progressively align more modalities to LLM with the UPM. To fully leverage the potential of OneLLM in following instructions we also curated a comprehensive multimodal instruction dataset including 2M items from image audio video point cloud depth/normal map IMU and fMRI brain activity. OneLLM is evaluated on 25 diverse benchmarks encompassing tasks such as multimodal captioning question answering and reasoning where it delivers excellent performance. Code data model and online demo are available at https://github.com/csuhan/OneLLM

Semantic instance and panoptic segmentation of 3D point clouds have been addressed using task-specific models of distinct design. Thereby the similarity of all segmentation tasks and the implicit relationship between them have not been utilized effectively. This paper presents a unified simple and effective model addressing all these tasks jointly. The model named OneFormer3D performs instance and semantic segmentation consistently using a group of learnable kernels where each kernel is responsible for generating a mask for either an instance or a semantic category. These kernels are trained with a transformer-based decoder with unified instance and semantic queries passed as an input. Such a design enables training a model end-to-end in a single run so that it achieves top performance on all three segmentation tasks simultaneously. Specifically our OneFormer3D ranks 1st and sets a new state-of-the-art (+2.1 mAP50) in the ScanNet test leaderboard. We also demonstrate the state-of-the-art results in semantic instance and panoptic segmentation of ScanNet (+21 PQ) ScanNet200 (+3.8 mAP50) and S3DIS (+0.8 mIoU) datasets.

Recent Vision Transformer Compression (VTC) works mainly follow a two-stage scheme where the importance score of each model unit is first evaluated or preset in each submodule followed by the sparsity score evaluation according to the target sparsity constraint. Such a separate evaluation process induces the gap between importance and sparsity score distributions thus causing high search costs for VTC. In this work for the first time we investigate how to integrate the evaluations of importance and sparsity scores into a single stage searching the optimal subnets in an efficient manner. Specifically we present OFB a cost-efficient approach that simultaneously evaluates both importance and sparsity scores termed Once for Both (OFB) for VTC. First a bi-mask scheme is developed by entangling the importance score and the differentiable sparsity score to jointly determine the pruning potential (prunability) of each unit. Such a bi-mask search strategy is further used together with a proposed adaptive one-hot loss to realize the progressive-and-efficient search for the most important subnet. Finally Progressive Masked Image Modeling (PMIM) is proposed to regularize the feature space to be more representative during the search process which may be degraded by the dimension reduction. Extensive experiments demonstrate that OFB can achieve superior compression performance over state-of-the-art searching-based and pruning-based methods under various Vision Transformer architectures meanwhile promoting search efficiency significantly e.g. costing one GPU search day for the compression of DeiT-S on ImageNet-1K.

Recent advances in monocular depth estimation have been made by incorporating natural language as additional guidance. Although yielding impressive results the impact of the language prior particularly in terms of generalization and robustness remains unexplored. In this paper we address this gap by quantifying the impact of this prior and introduce methods to benchmark its effectiveness across various settings. We generate "low-level" sentences that convey object-centric three-dimensional spatial relationships incorporate them as additional language priors and evaluate their downstream impact on depth estimation. Our key finding is that current language-guided depth estimators perform optimally only with scene-level descriptions and counter-intuitively fare worse with low level descriptions. Despite leveraging additional data these methods are not robust to directed adversarial attacks and decline in performance with an increase in distribution shift. Finally to provide a foundation for future research we identify points of failures and offer insights to better understand these shortcomings. With an increasing number of methods using language for depth estimation our findings highlight the opportunities and pitfalls that require careful consideration for effective deployment in real-world settings.

To interpret Vision Transformers post-hoc explanations assign salience scores to input pixels providing human-understandable heatmaps. However whether these interpretations reflect true rationales behind the model's output is still underexplored. To address this gap we study the faithfulness criterion of explanations: the assigned salience scores should represent the influence of the corresponding input pixels on the model's predictions. To evaluate faithfulness we introduce Salience-guided Faithfulness Coefficient (SaCo) a novel evaluation metric leveraging essential information of salience distribution. Specifically we conduct pair-wise comparisons among distinct pixel groups and then aggregate the differences in their salience scores resulting in a coefficient that indicates the explanation's degree of faithfulness. Our explorations reveal that current metrics struggle to differentiate between advanced explanation methods and Random Attribution thereby failing to capture the faithfulness property. In contrast our proposed SaCo offers a reliable faithfulness measurement establishing a robust metric for interpretations. Furthermore our SaCo demonstrates that the use of gradient and multi-layer aggregation can markedly enhance the faithfulness of attention-based explanation shedding light on potential paths for advancing Vision Transformer explainability.

We explore the boundaries of scaling up a multilingual vision and language model both in terms of size of the components and the breadth of its training task mixture. Our model achieves new levels of performance on a wide-range of varied and complex tasks including multiple image-based captioning and question-answering tasks image-based document understanding and few-shot (in-context) learning as well as object detection video question answering and video captioning. Our model advances the state-of-the-art on most vision-and-language benchmarks considered (20+ of them). Finally we observe emerging capabilities such as complex counting and multilingual object detection tasks that are not explicitly in the training mix.

We present a novel multimodal multitask network and associated training algorithm. The method is capable of ingesting data from approximately 12 different modalities namely image video audio text depth point cloud time series tabular graph X-ray infrared IMU and hyperspectral. The proposed approach utilizes modality specialized tokenizers a shared transformer architecture and cross-attention mechanisms to project the data from different modalities into a unified embedding space. It addresses multimodal and multitask scenarios by incorporating modality-specific task heads for different tasks in respective modalities. We propose a novel pretraining strategy with iterative modality switching to initialize the network and a training algorithm which trades off fully joint training over all modalities with training on pairs of modalities at a time. We provide comprehensive evaluation across 25 datasets from 12 modalities and show state of the art performances demonstrating the effectiveness of the proposed architecture pretraining strategy and adapted multitask training.

We develop a theory for the representation of opaque solids as volumes. Starting from a stochastic representation of opaque solids as random indicator functions we prove the conditions under which such solids can be modeled using exponential volumetric transport. We also derive expressions for the volumetric attenuation coefficient as a functional of the probability distributions of the underlying indicator functions. We generalize our theory to account for isotropic and anisotropic scattering at different parts of the solid and for representations of opaque solids as stochastic implicit surfaces. We derive our volumetric representation from first principles which ensures that it satisfies physical constraints such as reciprocity and reversibility. We use our theory to explain compare and correct previous volumetric representations as well as propose meaningful extensions that lead to improved performance in 3D reconstruction tasks.

Modeling object dynamics with a neural network is an important problem with numerous applications. Most recent work has been based on graph neural networks. However physics happens in 3D space where geometric information potentially plays an important role in modeling physical phenomena. In this work we propose a novel U-net architecture based on continuous point convolution which naturally embeds information from 3D coordinates and allows for multi-scale feature representations with established downsampling and upsampling procedures. Bottleneck layers in the downsampled point clouds lead to better long-range interaction modeling. Besides the flexibility of point convolutions allows our approach to generalize to sparsely sampled points from mesh vertices and dynamically generate features on important interaction points on mesh faces. Experimental results demonstrate that our approach significantly improves the state-of-the-art especially in scenarios that require accurate gravity or collision reasoning.

Trajectory prediction is fundamental in computer vision and autonomous driving particularly for understanding pedestrian behavior and enabling proactive decision-making. Existing approaches in this field often assume precise and complete observational data neglecting the challenges associated with out-of-view objects and the noise inherent in sensor data due to limited camera range physical obstructions and the absence of ground truth for denoised sensor data. Such oversights are critical safety concerns as they can result in missing essential non-visible objects. To bridge this gap we present a novel method for out-of-sight trajectory prediction that leverages a vision-positioning technique. Our approach denoises noisy sensor observations in an unsupervised manner and precisely maps sensor-based trajectories of out-of-sight objects into visual trajectories. This method has demonstrated state-of-the-art performance in out-of-sight noisy sensor trajectory denoising and prediction on the Vi-Fi and JRDB datasets. By enhancing trajectory prediction accuracy and addressing the challenges of out-of-sight objects our work significantly contributes to improving the safety and reliability of autonomous driving in complex environments. Our work represents the first initiative towards Out-Of-Sight Trajectory prediction (OOSTraj) setting a new benchmark for future research.

State-of-the-art models on contemporary 3D segmentation benchmarks like ScanNet consume and label dataset-provided 3D point clouds obtained through post processing of sensed multiview RGB-D images. They are typically trained in-domain forego large-scale 2D pre-training and outperform alternatives that featurize the posed RGB-D multiview images instead. The gap in performance between methods that consume posed images versus post-processed 3D point clouds has fueled the belief that 2D and 3D perception require distinct model architectures. In this paper we challenge this view and propose ODIN (Omni-Dimensional INstance segmentation) a model that can segment and label both 2D RGB images and 3D point clouds using a transformer architecture that alternates between 2D within-view and 3D cross-view information fusion. Our model differentiates 2D and 3D feature operations through the positional encodings of the tokens involved which capture pixel coordinates for 2D patch tokens and 3D coordinates for 3D feature tokens. ODIN achieves state-of-the-art performance on ScanNet200 Matterport3D and AI2THOR 3D instance segmentation benchmarks and competitive performance on ScanNet S3DIS and COCO. It outperforms all previous works by a wide margin when the sensed 3D point cloud is used in place of the point cloud sampled from 3D mesh. When used as the 3D perception engine in an instructable embodied agent architecture it sets a new state-of-the-art on the TEACh action-from-dialogue benchmark. Our code and checkpoints can be found at the project website: https://odin-seg.github.io.

The booming of 3D recognition in the 2020s began with the introduction of point cloud transformers. They quickly overwhelmed sparse CNNs and became state-of-the-art models especially in 3D semantic segmentation. However sparse CNNs are still valuable networks due to their efficiency treasure and ease of application. In this work we reexamine the design distinctions and test the limits of what a sparse CNN can achieve. We discover that the key credit to the performance difference is adaptivity. Specifically we propose two key components i.e. adaptive receptive fields (spatially) and adaptive relation to bridge the gap. This exploration led to the creation of Omni-Adaptive 3D CNNs (OA-CNNs) a family of networks that integrates a lightweight module to greatly enhance the adaptivity of sparse CNNs at minimal computational cost. Without any self-attention modules OA-CNNs favorably surpass point transformers in terms of accuracy in both indoor and outdoor scenes with much less latency and memory cost. Notably it achieves 76.1% 78.9% and 70.6% mIoU on ScanNet v2 nuScenes and SemanticKITTI validation benchmarks respectively while maintaining at most 5x better speed than transformer counterparts. This revelation highlights the potential of pure sparse CNNs to outperform transformer-related networks. Our code is built upon Pointcept which is available at https://github.com/Pointcept/Pointcept.

In this paper we address single image-based novel view synthesis (NVS) by firstly integrating view-dependent effects (VDE) into the process. Our approach leverages camera motion priors to model VDE treating negative disparity as the representation of these effects in the scene. By identifying that specularities align with camera motion we infuse VDEs into input images by aggregating pixel colors along the negative depth region of epipolar lines. Additionally we introduce a relaxed volumetric rendering approximation enhancing efficiency by computing densities in a single pass for NVS from single images. Notably our method learns single-image NVS from image sequences alone making it a fully self-supervised learning approach that requires no depth or camera pose annotations. We present extensive experimental results and show that our proposed method can learn NVS with VDEs outperforming the SOTA single-view NVS methods on the RealEstate10k and MannequinChallenge datasets. Visit our project site https://kaist-viclab.github.io/monovde-site.

Given the power of vision transformers a new learning paradigm pre-training and then prompting makes it more efficient and effective to address downstream visual recognition tasks. In this paper we identify a novel security threat towards such a paradigm from the perspective of backdoor attacks. Specifically an extra prompt token called the switch token in this work can turn the backdoor mode on i.e. converting a benign model into a backdoored one. Once under the backdoor mode a specific trigger can force the model to predict a target class. It poses a severe risk to the users of cloud API since the malicious behavior can not be activated and detected under the benign mode thus making the attack very stealthy. To attack a pre-trained model our proposed attack named SWARM learns a trigger and prompt tokens including a switch token. They are optimized with the clean loss which encourages the model always behaves normally even the trigger presents and the backdoor loss that ensures the backdoor can be activated by the trigger when the switch is on. Besides we utilize the cross-mode feature distillation to reduce the effect of the switch token on clean samples. The experiments on diverse visual recognition tasks confirm the success of our switchable backdoor attack i.e. achieving 95%+ attack success rate and also being hard to be detected and removed. Our code is available at https://github.com/20000yshust/SWARM.

This paper presents Neural Visibility Field (NVF) a novel uncertainty quantification method for Neural Radiance Fields (NeRF) applied to active mapping. Our key insight is that regions not visible in the training views lead to inherently unreliable color predictions by NeRF at this region resulting in increased uncertainty in the synthesized views. To address this we propose to use Bayesian Networks to composite position-based field uncertainty into ray-based uncertainty in camera observations. Consequently NVF naturally assigns higher uncertainty to unobserved regions aiding robots to select the most informative next viewpoints. Extensive evaluations show that NVF excels not only in uncertainty quantification but also in scene reconstruction for active mapping outperforming existing methods. More details can be found at https://sites.google.com/view/nvf-cvpr24/.